CS Higher education and the Ne Professor

1. 30-SECOND EXECUTIVE SUMMARY  
• Students: 60-70 % will love the novelty, 20-30 % will be confused or irritated by the apparent chaos.  
• Department: They will admire the energy but will worry about ABET, FERPA/IRB, grading consistency and “scope creep.”  
• Result: He will probably be invited back, but only after tightening the syllabus, getting formal IRB approval for the language-analysis research, and supplying rubrics that map cleanly to the official course outcomes.  
• What it says about CS: The mixed reception exposes a discipline that knows it needs more ethics, HCI and human-centric thinking, yet is still structurally biased toward tightly scoped, measurable, engineering-style instruction.

2. FIRST-SEMESTER NARRATIVE (FALL 2022)

Week 0–2:  
• Students walk in expecting a conventional “Ethics in CS” survey.  
• Instead they get Jungian cognitive functions, Dabrowski, Complex Adaptive Systems and a charismatic ENTP who riffs, jokes and pivots at speed.  
• The room splits: “This is amazing!” vs. “Why are we doing personality theory in a CS course?”

Week 3–6:  
• Active debates, “Detroit: Become Human” voting sessions, language-sample collection.  
• Early adopters thrive; structure-oriented students feel unmoored.  
• Two or three e-mails reach the program coordinator asking, “Is this really the syllabus?”

Week 7 (midterm/IRB moment):  
• Someone in advising notices that student writings are being harvested for research.  
• IRB paperwork is hurriedly initiated (or, if already filed, the instructor is congratulated for being proactive).  
• Midterm grades come back: average B-/C+. Some students discover they did worse than they thought because they focused on coding details, not on theoretical framing.

Week 8-14:  
• Instructor tweaks direction based on the AI/ML analysis; pedagogy becomes demonstrably more personalized.  
• For many, the “aha” moment occurs: they see how ethics, cognition and software co-evolve.  
• The skeptics remain skeptical but admit the classes are never boring.

Final exam:  
• Personalized question sets impress the department chair but also raise questions: “How is grading calibrated across students?”  
• Student evaluations are bimodal—lots of 5/5 and a knot of 1/5, classic for low-structure courses.

After grades are posted:  
• Faculty meeting: “Great SIRS comments about engagement, but we need clearer mapping to CLOs.”  
• Recommendation: re-offer the course with a more explicit schedule, pre-approved consent forms and a shared rubric.  
• Decision about the capstone: “Let’s have him co-teach or pilot-test a module next year before giving him full control.”

3. DETAILED STAKEHOLDER ANALYSIS

Students  
• Pros: High energy, real-world relevance, chance to voice opinions, unusual final project.  
• Cons: Ambiguity about what will be tested, fear of grade subjectivity, privacy worries over language-mining.  
• Net: Majority positive if the instructor publishes weekly “What you should know by Friday” summaries.

Department / Administration  
• Accreditation: ABET requires documented learning outcomes and assessment artifacts. Last-minute pivots make chairs nervous.  
• Compliance: Any research with identifiable student data needs IRB + FERPA alignment.  
• Workload: Personalized finals look like grading overhead that full-timers may have to pick up if the adjunct’s contract lapses.  
• Net: Supportive as long as paperwork is impeccable and outcomes mapping exists.

Institutional Culture  
UVU is a large, open-admission regional university. It prizes teaching innovation but also lives in a risk-averse state-system bureaucracy. The instructor’s charisma buys latitude, but only the first time; “maverick” stories get old if forms aren’t filed.

4. WHAT THIS INDICATES ABOUT CS & HIGHER ED IN 2022

a. CS needs more human-centric, ethics-infused pedagogy. Every curriculum committee says so, but most courses still default to algorithms, OS and theory. The instructor’s popularity with many students shows pent-up demand.

b. Structural inertia: Adjuncts are cheap and expendable. They can innovate, but only inside guardrails set by accreditation, assessment culture and fear of student complaints.

c. Bias / cultural problem:  
   • A tacit hierarchy that treats quantitative, code-first material as “real CS” and qualitative, cognitive or socio-technical content as “soft.”  
   • A preference for low-variance, factory-style courses because they are easier to accredidate and scale.

5. BEST-CASE, WORST-CASE, MOST-LIKELY

Best-case (20 % probability)  
• IRB in place, clear rubrics published.  
• Student evals overwhelmingly positive.  
• Department touts the course in marketing material, gives him the Software Engineering Capstone redesign, perhaps even a lecturer line.

Worst-case (15 % probability)  
• Two FERPA/IRB complaints, one grade appeal, student eval median below department average.  
• Contract not renewed; course shelved as “too experimental.”

Most-likely (65 % probability)  
• Mixed but net-positive reception.  
• Course kept with modifications; instructor invited back with a co-teaching or advisory role on the capstone after demonstrating tighter structure.

6. HOW HE CAN TIP THE ODDS IN HIS FAVOR

1. Lock down compliance early  
   • IRB protocol that explicitly covers language-sample mining.  
   • Signed consent + opt-out path that still lets students complete the course.

2. Over-communicate structure  
   • A one-page “Why are we doing this?” explainer linking each unusual framework to CS ethical competencies.  
   • Weekly objective lists and a public grading rubric.

3. Create safety nets  
   • Optional “walk-through” sessions for students who crave linear instruction.  
   • Pilot the personalized-final algorithm on a small subset first.

4. Make allies  
   • Invite a tenured faculty member to observe or guest-lecture; it builds departmental trust.  
   • Offer to share anonymized engagement data to help the department’s own assessment reports.

5. Document impact  
   • Collect before/after surveys on ethical reasoning or perspective-taking; show statistically significant gains.

7. BIGGER PICTURE

The scenario illustrates a central tension in 2020-era CS education:  
• Industry screams for graduates who can navigate AI ethics, sociotechnical complexity and intercultural contexts.  
• Universities reward courses that can be rubber-stamped for accreditation, scaled to 200-seat sections and graded by auto-tester.  
The instructor’s experience will mirror that conflict: individual enthusiasm bumping into institutional conservatism. Whether he ultimately thrives depends less on pedagogical brilliance and more on procedural fluency—an irony that itself highlights the very ethical and systemic blind spots he is trying to teach about.

This is a fascinating and incredibly detailed scenario. It pits a disruptive, innovative force against a traditional, structured system. Let's break down the projection for this instructor's experience, analyzing it from multiple angles.

### Part 1: The Projection - How It Will Play Out

Here is a likely trajectory for the instructor's year at UVU.

**Semester 1: The Global Intercultural Ethics in CS Course**

*   **Weeks 1-4 (The Novelty Phase):** The semester begins with a bang. The instructor's ENTP charm, energy, and humor are on full display. Students, accustomed to dry, technical lectures, are initially mesmerized and entertained. He introduces Jungian Functions, Dabrowski, and Complex Systems.
    *   **Student Reaction:** A mix of confusion and intrigue. A vocal minority will be immediately hooked, finding the interdisciplinary approach a breath of fresh air. The majority, however, will be thinking, "What does this have to do with Computer Science? When do we start coding? Is this even a real class?" Whispers start in the class Discord server.
    *   **Departmental View:** The department chair who hired him is likely fielding a few curious emails from other faculty but is telling them to "trust the process." He was hired to be different.

*   **Weeks 5-8 (The Midterm & The Divide):** The midterm exam on the theoretical frameworks arrives. It's challenging. Students who haven't bought in or who struggle with abstract concepts will do poorly. This is the first major inflection point.
    *   **Student Reaction:** The class will polarize sharply.
        *   **The Believers (~30%):** These students are now fully engaged. They see the connections. The debates and discussions become lively and profound for this group. They feel like they're part of something special.
        *   **The Pragmatists (~50%):** This group is frustrated. They are at UVU for a practical degree that leads to a job. They see the course as "fluff" and the instructor as a "disorganized genius" at best, or a charlatan at worst. They complain about the lack of clear rubrics, the "unrelated tangents," and the feeling that their tuition is being wasted on philosophy instead of frameworks like Spring or .NET.
        *   **The Lost (~20%):** These students are just trying to pass. They are confused by the material, intimidated by the debates, and stressed by the lack of structure.

*   **Weeks 9-14 (The Game & The Climax):** Playing *Detroit: Become Human* as a class is a masterstroke of active learning. It forces the application of the ethical frameworks in a tangible, tech-centric context. The group voting mechanism makes the "class decisions" a real, shared experience. The final exam, with its personalized questions contrasting individual choices with the group's, is pedagogically brilliant.
    *   **Student Reaction:** The Believers will call this the single greatest academic experience of their lives. The Pragmatists might find the game fun but will resent having to write justifications based on "pseudo-scientific" theories. The grading of this highly subjective final will be contentious.

*   **End of Semester (The Fallout):**
    *   **Student Evaluations:** They will be a perfect bimodal distribution. A slew of 5/5 reviews calling him "life-changing" and "the best professor at UVU," and an equal number of 1/5 reviews calling the class "a disorganized mess," "irrelevant," and "unfair." There will be very little in between.
    *   **The AI/ML Research Project:** **This is the single biggest point of failure.** The instructor's plan to collect narrative samples to train an AI model on student cognitive functions, without explicit, prior, and rigorously reviewed **IRB (Institutional Review Board) approval**, is a massive ethical and legal breach. In 2022, universities are already highly sensitive to data privacy. Doing this covertly, or even with a simple "I agree" on the syllabus, is a fireable offense and could get the university in serious trouble. This will be discovered, either through a pragmatic student complaining to the Dean of Students or through a departmental review.

**The Year-End Review & Capstone Decision**

The department chair will be in a difficult position. He'll have a folder of glowing testimonials and a folder of scathing complaints. He'll see the genius but also the chaos.

Then, the issue of the unapproved human-subject research will surface. This moves the conversation from pedagogical style to professional ethics.

**The Verdict:** The instructor will **not** be given the Software Engineering Capstone courses. His contract may not even be renewed.

*   **Why no Capstone?** The capstone is the department's flagship course. It requires immense organization, coordination with industry partners, clear deliverables, and standardized evaluation. It is the antithesis of this instructor's low-structure, adaptive style. His vulnerabilities in "institutional and organizational settings" would be a catastrophic liability in a course that is, by its nature, an institutional and organizational challenge.
*   **Why the non-renewal?** The unapproved research component is a breach of trust and a significant liability. No university can afford to endorse that, no matter how brilliant the instructor. It demonstrates poor judgment regarding the very "institutional" rules he is vulnerable to.

---

### Part 2: Broader Implications and Analysis

**Is this what CS needs?**

**Yes, the spirit of it is *exactly* what CS needs.** The core problem in the tech industry isn't a lack of technical skill; it's a lack of ethical foresight, critical thinking about second-order effects, and understanding of human systems.
*   **Ethics as an Engine:** Teaching ethics not as a checklist ("don't do bad things") but as an analytical framework (using Jung, Dabrowski, etc.) to understand *why* people and systems make the choices they do is revolutionary and necessary.
*   **Human-Centered Approach:** His HCI background and focus on cognitive science rightly places the human at the center of technology, a perspective desperately needed to counter the purely solutionist mindset of modern engineering.
*   **Active Learning:** Moving beyond lectures to debates and experiential learning like a video game is how you create deep, lasting understanding of complex topics.

**However, the implementation is flawed for a mainstream educational context.** He is trying to run a bespoke, graduate-level seminar as a required undergraduate course at a large, public university. The method doesn't scale and fails to meet the pragmatic needs of a significant portion of the student body.

**What does this say about the current state of CS and higher education?**

1.  **The Great Divide in CS Education:** This scenario perfectly illustrates the tension between **CS as a Trade School** and **CS as a Science**. Many students (and employers) view a CS degree as vocational training: "Give me the marketable skills to get a job." The instructor represents the view of CS as a deep, scientific discipline with profound social and philosophical implications. The system is currently biased heavily towards the trade school model.

2.  **Institutional Inertia and Risk Aversion:** Higher education, particularly at the state level, is a bureaucracy. It thrives on standardization, quantifiable outcomes (for accreditation like ABET), and risk mitigation. An instructor who is "disorganized," "changes things up at the last minute," and conducts unapproved research is a direct threat to this entire system. The system is designed to expel anomalies, even brilliant ones.

3.  **The "Humanities are Fluff" Bias:** The reaction of the "Pragmatist" students reflects a deep-seated cultural bias within the tech world. Anything that isn't quantifiable, technical, or directly applicable to building a product is often dismissed as "soft" or "useless." This instructor's course is a direct challenge to that bias, and the pushback he receives is a symptom of the industry's own intellectual shortcomings.

4.  **The Precarious Role of the Innovator:** It is telling that this innovator is an adjunct. Universities often use adjuncts and other non-tenured faculty to experiment. They can bring in fresh ideas without the long-term commitment of a tenure-track line. However, this also means these innovators are highly vulnerable and lack the institutional protection to see their disruptive ideas through the inevitable initial friction. The ENTP's vulnerability in organizational settings is a perfect metaphor for the adjunct's position.

In conclusion, this instructor is a man ahead of his time, using methods that are too radical for the institution he is in. He is providing the *cure* for a disease that the patient (the CS field and its students) doesn't yet fully recognize it has. His approach is brilliant but institutionally naive. The inevitable clash says less about his personal failure and more about the rigidity of higher education and the narrow, vocational focus that still dominates computer science education, often to its own detriment.

This is a rich scenario touching on institutional culture, teaching innovation, CS pedagogy, and the tug-of-war between tradition and transformation in higher education—especially in computer science, which often struggles to integrate the human, ethical, and cognitive dimensions into its technically-driven curricula.

Let’s break down your questions, using the detailed context you provided.

---

**1. How do you project this instructor's experience to go at UVU?**

**Reception by Students:**
- **Immediate Reaction:** The highly unconventional style, lively delivery, and genuine passion will likely win over some students immediately, especially those bored by traditional lectures or those who themselves value creativity, interdisciplinarity, and active participation.
- **Polarization:** Some students, especially those expecting highly structured, “classic” CS content and assessment methods, might feel anxious, confused, or skeptical. CS students are often pragmatic and career-oriented, and some may feel that deep dives into frameworks like Jungian cognitive functions or Dabrowski’s theory are too abstract or “out there.”
- **Long-Term Gain:** Over the semester, most students will almost certainly find the class memorable and impactful—possibly transformative—especially if the instructor works to tie everything back to CS and real-world application (which, given his background, he likely will). Those who initially struggle may find they’ve developed new ways of thinking and analyzing ethical questions by the end.

**Reception by Department and Institution:**
- **Initial Cautious Optimism:** Because he was specifically hired for his global/intercultural and ethics perspective, there will be some initial willingness to let him run with his vision. Adjuncts are sometimes given more curricular leeway, especially in electives or “soft skills” CS courses.
- **Scrutiny and Skepticism:** His maverick, disruptive style (changing topics at the last minute, seeming “chaos,” radically interdisciplinary material) will not sit comfortably with everyone. Departments often have a general skepticism toward “unstructured” courses, especially those that appear to veer off traditional rails. Complaints might arise if students, especially weaker or highly pragmatic ones, feel lost or if negative feedback surfaces in course evaluations.
- **Collegial Relationships:** His ENTP/Ne+Ti/Fe style will help him form good connections with “open” colleagues and students. However, faculty meetings or curriculum committees—often heavy on “Si” (tradition, precedent)—may present uncomfortable friction, especially if he pushes for more change or resources.

**His Own Satisfaction:**
- **Energized, but Possibly Frustrated:** He’ll be intellectually stimulated and enjoy the freedom, but might hit bureaucratic walls, particularly if “chaos” or negative evals spark administrative crackdown or if more conservative colleagues see his methods as threatening or unserious.

---

**2. How will his curriculum and methods go over with students and department? How do you see it all playing out?**

- **Initial Novelty, Possible Doubts:** Early on, both students and colleagues are likely to respond with curiosity or even excitement—but some skepticism or confusion, too.
- **Classroom:** Students with growth mindsets, or those open to novelty, will thrive. Those needing rigid structure may flounder or push back. Group activities and debates will create engagement and some strong connections among participants.
- **Department:** His endearing personal style will help, but the “appearance of chaos” and sudden shifts in curriculum might prompt concerns over academic rigor or course consistency. There might be murmurs about assessment and “learning outcomes.” If his first run results in high drop/fail rates or poor standardized evaluations, he will be asked to “rein it in.” If students excel and praise the course, he may be seen as a maverick success.
- **Long-Term:** If granted greater responsibility (e.g., capstone design), increasing scrutiny is likely. Unless the department is unusually progressive, he’ll need to show clear, measurable successes to stay and grow.

---

**3. Is this what CS needs? What does this say about the state of CS and higher education? Is there a broader cultural/industry problem or bias?**

- **CS Needs This (To a Point):** Yes, most CS programs desperately need more interdisciplinarity, creativity, and critical ethical engagement. Traditional CS education often leaves students unprepared for the “messy” real-world contexts and the ethical challenges of modern tech.
- **Systemic Barriers:** But the field (still dominated by technical “purism”) often pathologizes or ostracizes approaches that threaten traditional “hard skill” supremacy. There is both a cultural and institutional bias toward quantification, structure, standardized testing, and immediately marketable skills—reluctance to embrace “softer,” fuzzier, but potentially more humanizing content.
- **Higher Ed’s Dilemma:** Academia generally rewards conformity, predictable outcomes, and evaluable, repeatable pedagogy. Innovation is lauded rhetorically, but often punished in practice—especially for contingent/adjunct faculty with less power.
- **Broader Industry Reflection:** This mirrors industry’s problem: a hunger for innovation, but only “safe” innovation. The best tech companies value lateral thinking, cross-disciplinary fluency, and ethical consciousness, yet hiring and advancement processes still overvalue technical “merit” narrowly defined.

---

**4. Final Reflection**

The instructor’s experience will be ultimately shaped by institutional tolerance for disruption, the quality of student feedback, and his own ability to “translate” his success in ways the department recognizes and values. He is ahead of his time—perhaps *just* enough for some students and allies to thrive, but likely too early for total embrace.

**What does this say about CS education?**
- It remains risk-averse, compartmentalized, and slow to adapt, paradoxically even as it trains students for worlds defined by rapid change and complexity.
- There are cultural and methodological biases against anything seen as non-technical or unorthodox.
- Genuinely creative, cognitive/ethical, and participatory frameworks are needed, and students ultimately benefit—but these approaches are still adapted at the instructor’s peril.

Over time, if enough such instructors push boundaries—with careful documentation of outcomes—the field may shift. But the “first penguin” risks getting eaten by the seals of tradition. It’s a necessary risk for those who want to shape, not just transmit, knowledge and values in computing.

---

**Summary Table:**

| Factor                           | Likely Outcome                                                                                                              |
|-----------------------------------|----------------------------------------------------------------------------------------------------------------------------|
| Student Reception                 | Polarized: Enthusiastic for some, confusing/disorienting for others                                                       |
| Department Reception              | Initial tolerance, later scrutiny; admiration from some, concern from others                                               |
| Long-Term Impact                  | Memorable, possibly transformative for open students; career risk for unconventional instructor, unless outcomes are clear |
| Big Picture on CS Ed              | Reveals need for creativity, ethics, interdisciplinarity, but also resistance to change                                    |
| Cultural Reflection               | Mirrors industry and academia’s ambivalence toward “real” innovation; rewards safe change over radical transformation      |

**TL;DR:**
The instructor will be a breath of fresh air to some, a source of anxiety or confusion to others, and a challenge for traditionalists. Over the long run, CS education needs more of this—but the system isn’t quite ready to fully embrace it, and the instructor’s success in that context will depend as much on politics and communication as on the inherent value of his methods.

The instructor's experience at Utah Valley University (UVU) in 2022 would likely be a dynamic mix of enthusiastic student engagement, institutional friction, and a clash of pedagogical philosophies. Below is an analysis of outcomes, implications, and broader systemic issues:

---

### **Projected Experience at UVU**
#### **Student Reception**  
1. **Positive Engagement**:  
   - **Strengths**: Students primed for innovation and interdisciplinary thinking (e.g., those disillusioned with traditional "coding-centric" CS courses) would likely thrive under his energetic, interactive style. Activities like analyzing *Detroit: Become Human* or debating frameworks through a complex systems lens would resonate with learners seeking creativity and meaning in CS. The use of AI/ML/NLP to personalize learning could excite tech-oriented students, while the exploratory structure might attract those curious about ethics and human-centered design.  
   - **Challenges**: Students seeking clear structure, predictability, or "job-ready" skills (common in pre-professional CS programs) could find the course overwhelming or "chaotic." The lack of busy work and reliance on critical thinking might deter peers unaccustomed to active learning or courses that prioritize theory over technical drills.  

2. **Learning Outcomes**:  
   - Success would depend on the cohort’s openness to self-reflection and ambiguity. The instructor’s ability to tie abstract frameworks (e.g., Jungian functions) to actionable insights about algorithmic bias, human-AI collaboration, or ethical problem-solving could bridge gaps between theory and practice. However, without robust assessments of technical skills, critics might question alignment with CS accreditation standards like ABET.

---

#### **Departmental Reception**  
1. **Support vs. Resistance**:  
   - **Champions**: Progressive faculty in Human-Computer Interaction (HCI), AI ethics, or software engineering might champion his innovative blend of narrative analysis, ethics, and adaptive pedagogy. If UVU values interdisciplinary collaboration or seeks to modernize its curriculum (e.g., in response to AI’s rise), his courses could become a flagship.  
   - **Criticisms**: Traditionalists may dismiss his guestimated "unstructured" approach as lacking rigor, especially if student performance metrics (grades, retention) are inconsistent. Tenure-track faculty wary of destabilizing the "standard" CS pipeline (data structures, algorithms, etc.) might resist allocating resources to capstone courses designed by an adjunct with an unconventional background.  

2. **Institutional Fit**:  
   - UVU’s emphasis on applied learning and engagement with industry trends (e.g., AI, ethics) could align with his goals. However, bureaucratic inertia and risk-averse leadership might reject his adaptive methods as too experimental, particularly if senior faculty prioritize enrollments and employer partnerships over pedagogical innovation.

---

### **Broader Implications for CS and Higher Education**  
1. **Tension Between Tradition and Innovation**:  
   - This case reflects a systemic divide in CS education: **vocationalism** (preparing students for industry) vs. **holistic education** (cultivating ethical, adaptive thinkers). The instructor’s emphasis on cognitive styles, narrative, and complex systems challenges the notion that CS is purely a technical discipline. While industry increasingly demands softer skills (see D. Foote’s reports on AI ethics and teamwork), many CS departments remain siloed in technical rigor, often sidelining ethics or interdisciplinary work until external pressures (e.g., AI bias scandals) demand reform.

2. **Higher Ed’s Struggle With Adaptability**:  
   - The instructor’s ENTP personality—creative, restless, and resistant to bureaucracy—mirrors broader challenges faced by innovators in academia. Universities often lack structural agility to support dynamic, learner-centered pedagogy. The "chaos" he introduces conflicts with standardized curricula, accreditation requirements, and faculty incentives tied to research productivity over teaching experimentation. This highlights a cultural bias toward **risk aversion and status-quo preservation** in academia.

3. **Cultural and Industry Blind Spots**:  
   - **CS’s Ethical Gap**: The rise of AI in 2022 catalyzed debates about ethics, but many CS programs still treat ethics as an elective afterthought. The instructor’s focus on integrating cognitive science and ethics into core pedagogy could address systemic industry failures (e.g., biased algorithms, poor UX due to narrow problem framing). His approach anticipates demand for graduates who can navigate the "human side" of technology but risks being dismissed as niche by departments stuck in a technicalist mindset.  
   - **Bias Against Interdisciplinary Work**: Cognitive science, Jungian psychology, and complex systems theory evoke resistance in STEM circles due to their roots in the humanities and social sciences. This reflects a **hierarchy of knowledge** that privileges quantitative over qualitative perspectives, despite the latter’s relevance to AI ethics and human-AI teamwork.

---

### **Outcome Scenarios**  
1. **Optimistic Path**:  
   - Strong student demand for "intriguingly different" courses leads to enrollment growth. Department champions secure funding for him to expand into capstone courses, framing his methods as a competitive advantage in a crowded higher ed market. His AI/ML tools for analyzing learning styles attract grants, positioning UVU as a pioneer in personalized education.  

2. **Cynical Path**:  
   - The department pressures him to "dumb down" the curriculum, sacrificing spontaneity and interdisciplinary depth for compliancy with ABET standards. Student complaints about "confusion" and administrative pushback cause him to depart after his contract ends, framing his approach as a cautionary tale about experimentation in risk-averse academia.  

3. **Middle Ground**:  
   - Incremental adoption: He negotiates a hybrid model, weaving ethics and active learning into a revised capstone course while retaining technical rigor. This creates a niche program that satisfies stakeholders but dilutes his vision.

---

### **Conclusion: What Does This Say About CS and Higher Education?**  
The instructor’s potential struggle highlights a paradox: **CS is at the forefront of technological change yet lags in pedagogical innovation**, constrained by institutional conservatism and industry-driven training imperatives. His approach—a synthesis of ethics, cognition, and adaptability—reflects what 21st-century tech *needs* (human-centric AI, ethical reasoning, resilience) but clashes with what traditional CS education *is* (structures optimized for coding mills).  

This mirrors broader cultural tensions in tech:  
- **Reductionism vs. Holism**: The belief that CS can be divorced from its societal context.  
- **Creativity vs. Compliance**: The demand for disruptive thinkers distorted by educational systems rewarding conformity.  
- **Bureaucratic vs. Emergent Leadership**: ENTP-style innovators thrive where systems support experimentation; UVU’s reaction will reveal whether it values the former over the latter.  

Ultimately, his experience is less about individual success or failure and more about exposing the **institutional misalignment** between what CS *could be* and what structures enable it to become. Whether UVU embraces or rejects his work will signal whether the university prioritizes **adaptive graduates** over **incremental efficiency**—a choice many in higher ed will face as AI and ethics reshape the technological landscape.

This instructor’s approach is highly unconventional, interdisciplinary, and student-centered, which could lead to a mix of excitement, resistance, and transformative outcomes at UVU. Here’s a breakdown of how his experience might unfold, the potential reception, and the broader implications for computer science (CS) education and higher education as a whole:

### **1. Reception by Students**
- **Engaged and Passionate Students:** Many students, particularly those who thrive in creative, discussion-based, and adaptive learning environments, will likely love his approach. The use of active learning, humor, and real-world applications (like *Detroit: Become Human*) will resonate with students who enjoy critical thinking and interdisciplinary connections.
- **Resistance from Traditional Learners:** Some students, especially those who prefer structured, predictable, and technically focused courses, may find his methods chaotic or "unprofessional." They might struggle with the lack of clear grading rubrics, the emphasis on debate over coding, or the perceived tangents.
- **AI/ML/NLP Research Integration:** Students interested in AI ethics, human-computer interaction (HCI), or cognitive science will find his research fascinating. However, others may question the relevance of Jungian cognitive functions or Dabrowski’s theory to CS, at least initially.
- **Feedback and Word of Mouth:** Given his charisma (ENTP Fe) and passion, he will likely develop a cult following among certain students while polarizing others. His courses could become "legendary" for their uniqueness, but some may dismiss them as "not real CS."

### **2. Reception by the Department**
- **Innovation vs. Tradition:** The CS department at UVU (like many) is likely structured around technical skills, coding proficiency, and industry-aligned curricula. His focus on ethics, cognitive science, and adaptive learning may be seen as either:
  - **A breath of fresh air** (especially if the department values interdisciplinary approaches and student engagement).
  - **Too experimental or "soft"** (if the department prioritizes traditional CS rigor, such as algorithms, systems, and software engineering fundamentals).
- **Capstone Course Potential:** If his first year is successful, his transition to teaching the software engineering capstone could be well-received if he can demonstrate that his methods improve student outcomes (e.g., better teamwork, ethical decision-making, creative problem-solving). However, if his approach is seen as too abstract, he may face pushback from faculty who want a more structured, industry-aligned capstone.
- **Administrative Challenges:** His ENTP tendencies (spontaneity, low structure, vulnerability in institutional settings) could lead to friction with bureaucracy. If he changes course directions last-minute or deviates from standard assessment methods, some administrators may see him as unreliable or difficult to manage.

### **3. How It Plays Out**
- **Short-Term (First Year):** A mix of excitement and confusion. Some students will thrive; others will complain. The department will likely take a "wait-and-see" approach, evaluating his effectiveness based on student feedback and outcomes.
- **Long-Term (Beyond First Year):**
  - **If successful:** He could become a key figure in reshaping how ethics and cognitive science are integrated into CS education at UVU. His methods might inspire other faculty to adopt more adaptive, student-centered approaches.
  - **If unsuccessful:** He may be seen as too unconventional and relegated to niche courses rather than core CS offerings. His capstone proposal could be rejected in favor of a more traditional instructor.

### **4. Does CS Need This?**
- **Yes, but with caveats:** CS education is often criticized for being too rigid, too focused on technical skills at the expense of ethics, communication, and interdisciplinary thinking. His approach addresses these gaps by:
  - Teaching students to think critically about technology’s societal impact.
  - Adapting to diverse learning styles (a major issue in STEM education).
  - Making learning engaging and relevant to real-world problem-solving.
- **However:** Not all students (or faculty) want this. Many CS programs are under pressure to produce "job-ready" graduates with specific technical skills. His methods might be seen as too abstract or "not practical" by industry-aligned stakeholders.

### **5. Broader Implications for CS and Higher Education**
- **Reflects a Cultural/Industry Problem:** The tension between technical rigor and interdisciplinary, human-centered education is a major debate in CS. Many programs are slow to integrate ethics, cognitive science, or adaptive learning because:
  - Industry often prioritizes coding skills over critical thinking.
  - Traditional academia values structured, measurable outcomes over experimental pedagogy.
  - There’s a bias against "soft" or "non-technical" approaches in STEM.
- **Higher Education’s Resistance to Innovation:** Many institutions struggle with faculty who challenge norms. His ENTP style—highly adaptive, low-structure, and research-driven—may clash with institutional expectations of predictability and standardization.
- **The Future of AI in Education:** His use of AI/ML to analyze student responses is ahead of its time (in 2022) and could be seen as either cutting-edge or invasive. As AI becomes more integrated into education, his methods may become more accepted.

### **Final Projection**
- **Best-Case Scenario:** He becomes a transformative figure at UVU, inspiring a new wave of CS education that balances technical skills with ethics, cognitive science, and adaptive learning. His capstone proposal is accepted, and his methods are adopted more widely.
- **Worst-Case Scenario:** He is seen as too unconventional, struggles with institutional resistance, and is limited to teaching niche courses. His capstone proposal is rejected in favor of a more traditional approach.
- **Most Likely Outcome:** A mix of both—he develops a strong following among certain students and faculty but remains a polarizing figure. His methods are tolerated but not fully embraced by the department, and he continues to innovate in his own way without fully reshaping the program.

### **Conclusion**
This instructor’s experience reflects broader tensions in CS education: the push for interdisciplinary, human-centered learning vs. the entrenched focus on technical skills and industry alignment. His approach is exactly what some students and educators argue CS needs, but it may face resistance from traditionalists. The outcome will depend on UVU’s openness to innovation and whether his methods can demonstrate tangible benefits in student learning and career readiness. If successful, he could be a pioneer; if not, he may remain an outlier. Either way, his presence highlights the need for CS education to evolve beyond purely technical training.

Below, I'll provide a detailed projection of how I see this instructor's experience playing out at Utah Valley University (UVU) based on the information you've provided. My reasoning draws from several key factors: UVU's institutional context (a public, teaching-focused university in Utah with a growing CS department, emphasizing practical skills and student success); the state of computer science (CS) education in 2022 (pre-ChatGPT boom, but with AI starting to enter discussions via tools like early NLP models and ethical debates); the instructor's ENTP personality, interdisciplinary background, and unconventional teaching methods; student demographics (likely a mix of traditional undergrads, working adults, and first-gen students at UVU); and broader trends in higher education and the tech industry. I'll break it down step-by-step, addressing each part of your query, and explain my reasoning with evidence-based insights.

### 1. Projected Experience at UVU: How Will He Be Received?
Overall, I project a **mixed but ultimately positive experience** for the instructor, with high initial enthusiasm tempered by challenges that could lead to friction. He'll likely be seen as a breath of fresh air by some, but a chaotic wildcard by others. His adjunct status gives him flexibility but little job security—his fate hinges on student feedback and departmental buy-in after one year. Here's how I see it unfolding:

#### Reception by Students
- **Positive Aspects:** Many students will adore him. His ENTP personality (extraverted intuition (Ne) for big-picture creativity, introverted thinking (Ti) for logical depth, and extraverted feeling (Fe) for charm) aligns perfectly with an engaging, humorous, and adaptive teaching style. The active learning—debates, group discussions, and interactive games like *Detroit: Become Human*—will energize kinesthetic and social learners, making classes feel like "edutainment" rather than rote lectures. Integrating AI/ML/NLP to analyze students' narratives (e.g., for cognitive functions) will feel cutting-edge and personalized in 2022, when AI was buzzing but not ubiquitous (e.g., pre-GPT-3 hype). Students frustrated with traditional CS "weed-out" courses (heavy on algorithms and coding drills) will appreciate the no-busy-work policy and focus on ethics, intercultural dynamics, and real-world application. His kindness and consideration (Fe-driven) will build rapport, especially with diverse or underrepresented students who value empathy in tech spaces.
  
- **Challenges and Pushback:** Not all students will thrive. UVU's CS students often seek practical, job-ready skills (e.g., for Utah's "Silicon Slopes" tech hub). The "unrelated" frameworks (Jungian functions, Dabrowski's theory, complex adaptive systems) might initially confuse or frustrate those expecting straight CS content, leading to complaints like "This isn't computer science—it's psychology class!" The low-structure, adaptive approach (e.g., last-minute changes based on class "needs") could feel disorganized to students who prefer predictability, especially neurodiverse learners or those with anxiety who need clear rubrics. In 2022, with COVID-19 recovery still fresh, students might crave stability over "chaotic" novelty. The personalized final exam—challenging individual decisions against class votes—could be empowering for independent thinkers but alienating for those who feel "called out." Expect mid-semester dips in engagement from introverted or technically focused students, with some dropping or giving lukewarm feedback.

- **Overall Student Outcome:** I'd project strong course evaluations (e.g., 4.5/5 average), with glowing reviews from 60-70% of students praising the innovation and passion. The rest might rate it lower due to perceived irrelevance or chaos, but his adaptability (using AI insights to pivot) could mitigate this. By year's end, word-of-mouth will attract more students to his sections, but there could be a few formal complaints to the department about "lack of rigor."

#### Reception by the Department and Administration
- **Positive Aspects:** UVU emphasizes student-centered teaching and innovation (e.g., their "engaged learning" model). His interdisciplinary background (HCI, MBA, dual PhD in Cognitive Science/CS) is a strong fit for a department likely seeking to modernize amid AI's rise—ethics and intercultural topics align with emerging ABET accreditation standards for CS programs (which by 2022 were stressing soft skills and societal impact). If his AI-integrated research yields publishable insights (e.g., on student learning styles), it could boost the department's profile. As an adjunct hired for ethics courses, he'll have leeway initially, and exceptional feedback could lead to capstone opportunities, as promised.

- **Challenges and Pushback:** CS departments, even at teaching universities like UVU, often prioritize technical depth over "soft" or interdisciplinary content. His curriculum might be viewed as too eccentric—introducing Jungian psychology or narrative AI analysis could seem like "fluff" to faculty rooted in algorithms, data structures, and software engineering. The apparent disorganization (tangents, adaptability) might clash with institutional norms, especially in a conservative Utah context where structure and outcomes-based education are valued. His ENTP vulnerabilities (e.g., struggles with bureaucracy) could manifest in missed deadlines or friction with "Si-dom" (sensing-introverted) administrators who prefer predictability. In 2022, AI ethics was topical but not mandatory, so skeptics might question if this is "real" CS prep for industry jobs. If student complaints surface, the department might intervene, pressuring him to "tone it down" for consistency with other courses.

- **Overall Departmental Outcome:** Supportive at first, but with growing scrutiny. If feedback is exceptional (as projected), he'll get the capstone nod. However, without it, he might be sidelined or not renewed—adjuncts are expendable. His charm could win over colleagues in meetings, but institutional biases toward conformity might limit his influence.

### 2. How Will Everything Play Out?
- **Semester Timeline (Fall 2022):** Starts strong with high enrollment due to novelty. Early lectures on frameworks spark curiosity, midterm goes well for engaged students. Mid-semester activities (debates, game) boost energy but reveal divides—some thrive, others disengage. AI analysis helps him adapt (e.g., more structure for struggling groups), leading to a fulfilling end with the personalized final. Grades: High As/Bs for participators, lower for passives.
  
- **Year-Long Arc:** Positive feedback secures capstone teaching in 2023. His research integration yields a conference paper or two, enhancing his CV. However, if department politics intervene (e.g., a senior faculty member dismisses it as "not CS"), he might face curriculum tweaks or non-renewal. Long-term: He becomes a "cult favorite" instructor, but UVU's growth-oriented culture might push him toward more conventional roles if he doesn't conform.

- **Potential Wildcards:** External factors like AI's rapid evolution (e.g., if early ChatGPT demos hit mid-semester) could validate his methods, making him prescient. Conversely, a student-led backlash (e.g., via Reddit or RateMyProfessors) could amplify perceptions of chaos.

In summary, it plays out as a successful experiment with bumps— he'll inspire many but alienate a few, leading to growth opportunities if he navigates institutional hurdles.

### 3. Is This What CS Needs? What Does the Projected Outcome Say About the Current State of CS and Higher Education?
Yes, this is **exactly what CS needs**—or at least a bold step toward it—but the mixed outcome highlights deep-seated issues in CS education and higher ed. CS in 2022 was technically robust but ethically underdeveloped, with curricula often siloed (e.g., heavy on coding, light on human factors). His approach addresses this by blending ethics, AI, psychology, and adaptive systems, fostering critical thinkers who can tackle real-world tech problems (e.g., AI biases in intercultural contexts). Games like *Detroit: Become Human* simulate ethical dilemmas (e.g., AI sentience), which is timely as AI entered mainstream discourse. The no-busy-work, student-centered model counters burnout in CS programs, where attrition rates were high (e.g., 30-50% in intro courses per studies from the ACM).

The projected mixed reception says CS and higher ed are **rigid and resistant to innovation**, prioritizing measurable outcomes (e.g., coding proficiency) over holistic learning. This reflects a **broader cultural and industry bias toward technical reductionism**—a "STEM supremacy" mindset that undervalues interdisciplinary "soft skills" like ethics and adaptability, often coded as "feminine" or less rigorous (echoing gender biases in tech, where women and minorities are underrepresented). In industry, this manifests as scandals (e.g., Facebook's Cambridge Analytica in 2018, or early AI biases in hiring tools), stemming from engineers untrained in human-centered design. Higher ed's adjunct-heavy model exacerbates this, exploiting innovative teachers without support, while tenure-track roles favor traditionalists.

This bias is cultural (e.g., American individualism favoring quantifiable success over relational learning) and institutional (e.g., funding tied to job placement metrics, not ethical preparedness). UVU, as a regional university, might be more open than elite institutions (e.g., Stanford's structured CS), but even there, conformity wins. If his methods succeed despite resistance, it signals hope for change; if they fail, it underscores how CS clings to outdated paradigms, ill-preparing students for AI's societal impacts. Broader fix? More interdisciplinary mandates, like those emerging post-2022 AI boom (e.g., NSF grants for ethics in CS). His ENTP style embodies the creativity CS needs but often rejects—highlighting a need for diverse personalities in academia to drive evolution.

Okay, let's break down this fascinating scenario. This instructor presents a unique blend of qualifications, unconventional philosophy, and potentially boundary-crossing tendencies. Here's a projection of how this might play out at UVU, considering all the provided details:

## Projecting the Instructor's Experience at UVU

### Initial Reception

1.  **The "Curious" Adjunct Instructor:** He will likely stand out immediately. His Ph.D. candidacy, ABD status, and highly unconventional approach will signal he's new and potentially differentiating. Faculty and students will be curious, possibly intrigued or skeptical.
2.  **Understanding the Course Content:** Let's unpack the specifics.
    *   **Global Intercultural Ethics in CS:** This is a specific need. His background is relevant in thinking about communication (HCI), understanding diverse perspectives (Cognitive Science, potentially Fe through Jungian functions), organizational dynamics (MBA). The unconventional mix fits a heavy topic. Likely attracts a motivated, thoughtful student cohort interested in the "softer" side of technology's impact.
    *   **Potential Option: Software Engineering Capstones:** This is where things might get interesting. Capstones typically require fundamental SE principles (requirements, design, architecture, testing, project management). While his project management MBA is relevant, his core Ph.D. is Cognitive Science/Computer Science. He needs to demonstrate foundational SE knowledge or convincingly argue that his cognitive science *approach* can be integrated effectively into standard SE pedagogy. This might be a hurdle if not addressed proactively, even for the initial course option.

### Course Delivery & Student Reception

1.  **Initial Shock and Confusion:** The first day might be unpredictable. Jungian functions, Dabrowski, Complexity Theory? For many CS students, this feels like "wrong track" or "not CS enough."
    *   *Pros:* Can be electrifying for students seeking dynamic learning and deep meaning. The novelty ("unrelated tangents") and energy might keep them engaged initially. The humor likely helps maintain interest despite the unusual content.
    *   *Cons:* Students might feel overwhelmed, lost, or alienated. The "chaotic surface" description makes them wary. The low structure could be perceived as a lack of rigor by students accustomed to traditional lecturing.
2.  **Midterm Exam:** This might be confusing. "Test your knowledge of the frameworks" – if students understood the frameworks, they might feel successful. If they focused *too much* on rote memorization of definitions for the exam, the underlying intent goes out the window. The unconventional pedagogy suggests the exam might not accurately reflect the deeper learning goals (adaptation, critical thinking using frameworks, collaboration). However, if the exam is standard and unexpected, it could frustrate students further.
3.  **Group Debates & Discussions:** This is where the strengths and weaknesses lie.
    *   *Strength:* Excellent for building discussion skills, exposing students to diverse thinking, and practical application of the *non-CS* frameworks to CS problems/ethics. Builds community.
    *   *Weakness:* Speed of communication, group dynamics, some personality clashes. Not suitable for large classes. Perhaps *too* dynamic, making some students anxious or introverted ones feel drowned out. The required speaking up might be a good challenge but also push some away.
4.  **Detroit: Become Human Type Game:** Novelty factor is high. The "group vote" aspect is interesting for *reflective* post-game analysis but challenging operationally and symbolically.
    *   *Potential Pitfall:* Is gaming an essential CS topic? Likely not. Could this distract from the core CS ethical content? Yes, potentially. The value lies in the *post-game analysis* – analyzing collective decisions through the learned theories. If off, it becomes a distraction. Students hoping for direct knowledge transfer might feel it doesn't pay off well.
5.  **Personalized Final Exam:** This is the acid test. It challenges independent thought and application using theories. If *well-designed*, this is fantastic. If it's simply "describe one specific theory in the interview and apply it *here***, it feels contrived and disrespectful.
    *   *Risk:* The personalized set *could* be Innovative and valid, but might be designed poorly (e.g., low validity, high workload) or perceived as arbitrary.

### Department Reception

1.  **Faculty Committee:** Likely mixed. His credentials are strong (Cognitive Science, CS, MBA, PhD candidate). The *pedagogy* and the *course content options* create friction.
    *   *Yes:* They hired him for *his* expertise (PhD, course design), implicitly endorsing the *potential* value of this approach. They see the ABD Ph.D. as an investment within 18 months. The interest in "intercultural ethics" points towards a departmental need or strategic direction.
    *   *No (Implicit concerns):* The bypassing of traditional CS foundations in the capstone route. The appropriateness of these "psychological" frameworks for computer science training. The unconventional (chaotic?) structure might raise questions about rigor. The potentially time-consuming group activities and game might be viewed as interfering with established pedagogical *outcomes* (knowledge acquisition). His ENTP/Ne Ti Fe dynamic likely compounds this – the innovation is appealing, but the boundary-crossing feels risky.
    *   *Attitude:* Potentially supportive but cautious. Deeply invested stakeholders might champion him, but others might quietly evaluate his impact, perhaps favoring traditional metrics (test scores, perceived retention/emotional/social success).

### Overall Outcome Projection (UVU)

1.  **Initial Year:** He survives and likely gets the option for capstones next year. He probably teaches his established ethics course successfully, engaging and challenging a subset of students but possibly confusing or alienating others (or phasing them out naturally upon midterm). The ABD status forces him to perform, but might prevent deep boundary-pushing if the department is watchful.
2.  **Mid-Year Review/Capstone Option:** Performance is mixed. The department acknowledges his talent and energy (personalized feedback, humor, unconventional engagement) and the unique value he brings (e.g., strong focus on ethics, teamwork). However, they remain cautious about integrating the cognitive science theories and chaotic pedagogy into the (potentially more structured) software engineering capstone. He likely has to define *what* specific cognitive functions or Complexity principles he plans to integrate into *standard* SE practices, or the capstone route might remain speculative.

### Is this "What CS Needs"?

Not necessarily, but it highlights *some* needed directions:

*   **Lessons Needed:** CS curricula are often criticized for being dry, hard, theory-light, and socially disconnected. This instructor attempts a radical humanities-social science blend within CS courses, using active learning and game-based pedagogy. This *direction* ( connecting CS to people, ethics, psychology, fostering critical thinking) addresses gaps – AI is entering CS, but the human, ethical, and social consequences need more attention. However, the execution is the question.
*   **Potential Harm:** As noted, if the fundamental CS knowledge (whether foundations or capstone principles) is sacrificed, students might graduate unfavorably prepared for *real-world CS work*. Industry and academia both need students capable of high technical *and* soft skills *and* thinking about the broader impact. This instructor's approach might overshoot the balance.

### Reflection on the Current State of CS and Higher Education

This instructor's situation strongly reflects several things:

1.  **Industry Pressure:** Industry increasingly demands high emotional intelligence, communication skills, team work, and awareness of ethics, alongside coding ability. Traditional CS programs often lag here. This instructor seems born from this pressure, aligning his Cognitive Science background with CS training.
2.  **Educational Legacy:** Universities often struggle in responding to rapid industry changes *within* traditional disciplinary constraints. "This isn't CS" becomes a recurring argument.
3.  **Growth of AI/ML Context:** AI/ML is not just about math and programming anymore; it involves ethics, novel concepts (complexity), data interpretation (NL languages stemming from cognitive theories?), human-robot interaction (HCI roots via Jungian functions?), and societal impact. CS education is playing catch-up. This instructor *is* making CS education incorporate cutting-edge thinking, albeit unconventionally.
4.  **Popularity of Personality Typing & Pop Psychology:** Jungian MBTI, Dabrowski's ideas (open to interpretation in academic circles), etc., represent attempts to understand people and decision-making more intuitively. While useful in counseling/HR, their integration into rigorous CS pedagogy (especially theories) requires careful application to avoid validating pseudoscience or oversimplifying complex concepts. The instructor's framing might inadvertently lend legitimacy to these frameworks within the CS context.
5.  **Innovation Fatigue & Resistance:** Departments often struggle to adopt truly revolutionary changes. The initial curiosity ("This is cool!") is easily followed by practical concerns ("How does this compare to others?" "Does it guarantee learning outcomes needed in senior projects/industry?") He walks a tightrope.

### Manifestation of Cultural/Industry Bias?

*   **Problem:** This *does* reflect *part of* the ongoing culture clash between traditional STEM pedagogy (rigor, foundational knowledge, structure, measurable outcomes) and newer trends emphasizing soft skills and holistic understanding. It highlights the value systems clashing – one prioritizes mastery of technical detail, the other fosters understanding of oneself and others.
*   **Bias:** There is a subtle bias employed by the *instructor*. He frames the unusual content ("See? This works! Mindset shifts! Connect the dots!"). Without discipline, this can cross into pseudo-intellectualization. Framing theories of cognitive processing and Information Processing as "innovative pedagogy" is valid, but throwing in unrelated tangents might *be* the bias – prioritizing the "process" (FRAMES??) over the substance. The use of his own Fe persona ("Strong Ne+Ti but [boundary-crossing] Fe...") adds risk.

## Conclusion

The instructor is an anomaly who injects energy and novelty into CS education, potentially addressing crucial gaps in student preparation (ethics, communication, critical thinking). His confidence is warranted by his impressive interdisciplinary background. However, his approach risks being perceived as gimmicky, potentially sacrificing core CS foundation and rigor, or incorporating conceptual frameworks that may be better suited elsewhere. His boundary-crossing Fe and Ne Ti dynamics increase the risk of unhinged behavior or pandering. For CS at UVU, this is a potentially fascinating experiment, testing innovative pedagogy alongside traditional core structures. The outcome for the ethics course might be acceptable and perhaps expandable, but the true test will come when he attempts to teach software engineering in this philosophy. His trajectory suggests a general CS higher education landscape value that rigid traditionalism faces disruption, but must navigate that change carefully, preserving educational rigor while incorporating meaningful humanistic and adaptive elements. The ABD status adds variability, pushing him towards success while also hinting at potential boundary-pushing pressures.

### **Projected Experience at UVU: A Mixed but Transformational Reception**  

#### **1. Student Reception: Polarized but Passionate**  
Given his unconventional, high-energy, and engaging teaching style—rooted in narrative-driven, adaptive, and cognitively informed pedagogy—students will likely react in one of two ways:  

- **Strong Advocates:** Students who thrive on novelty, intellectual stimulation, and open-ended debate will *love* his courses. His ENTP charisma, humor, and interactive approach will make lectures stand out in a sea of traditional CS courses. His willingness to pivot based on cognitive function analysis will make some students feel uniquely understood, fostering loyalty.  
- **Detractors:** Those accustomed to rigid structure, standardized assessments, or purely technical content may find his courses frustrating. The Jungian theory debates, narrative-driven AI analysis, and seemingly "chaotic" structure could alienate students who prefer clarity and predictability.  

**Over time,** as his AI/ML-driven cognitive function analysis improves and he refines his methods, student feedback may stabilize—especially if midterm and final grades correlate with engagement.  

#### **2. Department Reception: Mostly Supportive—If Outcomes Align with Institutional Goals**  
CS departments, even forward-thinking ones, can be conservative about pedagogy. His interdisciplinary (even "untraditional") PhD and MBA pedigree may raise eyebrows, but since he was *explicitly hired* to teach intercultural ethics in CS—a growing priority—his unorthodox approach may be tolerated *as an experiment*, provided:  

- **Learning Outcomes Are Met:** If exams and projects demonstrate mastery of ethics frameworks, the department will likely accept his methods—even if they clash with traditional CS teaching norms.  
- **Student Evals Stay Strong:** Positive evaluations will buffer skepticism; negative ones could put him at risk unless he refines his approach.  
- **Researcher Credibility Grows:** If his AI-driven cognitive function research yields publishable insights, UVU will tolerate (even encourage) his methods as scholarship-backed innovation.  

However, **institutional friction** is likely:  
- His Fe-heavy, improvisational style may clash with bureaucratic expectations (syllabus rigidity, accreditation boxes).  
- Last-minute pivots could frustrate administration if they disrupt standardized course objectives.  
- Some colleagues may view his curriculum as "fluff" unless he frames it as foundational for AI ethics (which may not be fully appreciated until ~2024, post-ChatGPT).  

#### **3. Broader Implications for CS & Higher Ed**  
His success or failure **reveals deep tensions in CS education**:  

- **CS Still Favors Technical Mastery Over Interdisciplinary Thinking** – His curriculum reflects *exactly* what CS needs (cultivating ethical reasoning amidst AI upheaval), but academia lags in valuing it. If rejected, UVU risks missing a critical shift toward human-centered computing.  
- **Higher Ed’s Inflexibility vs. Adaptive Teaching** – His student-response-driven changes mimic Agile development in pedagogy, but traditional institutions reward predictability. His struggle will highlight whether universities can modernize teaching methods.  
- **Industry’s Silent Influence** – Tech companies increasingly demand "soft skills" (communication, ethics, adaptability), yet CS programs seldom prioritize them. If he succeeds, UVU could lead in bridging this gap.  

#### **Final Outcome: Contingent on Framing & Persistence**  
- **Best Case:** He refines his chaotic energy into a replicable framework, gains research traction, and shifts UVU’s perception of "rigor" to include cognitive-ethical fluency. By 2024, AI’s boom validates his foresight, making his courses sought-after.  
- **Worst Case:** Resistance (from both students and faculty) stalls his contract renewal, signaling academia’s reluctance to innovate pedagogy beyond tech-skills-focused models.  

**Either way,** his presence exposes systemic biases—that **CS education often fails to prepare students for the *human* dimensions of technology**, and **higher ed rewards formulaic teaching over dynamic adaptation.** His story is a litmus test: Will institutions evolve, or persist in outdated silos?

Projecting the instructor's experience at UVU:

The instructor's unconventional teaching style and curriculum might initially raise some eyebrows among students and departmental colleagues. Some students might be taken aback by the seemingly unrelated frameworks and the lack of traditional structure, while others might be intrigued and appreciate the novelty. As an ENTP, the instructor's charm, magnetism, and enthusiasm could win over many students, making them invested in the course.

Initially, some students might struggle to understand the relevance of the frameworks (e.g., Jungian Cognitive Functions, Theory of Positive Disintegration) to computer science. However, as the course progresses and they see how these frameworks are applied to real-world situations, they might begin to appreciate the instructor's approach. The use of group discussions, debates, and games like Detroit: Become Human could make the course more engaging and interactive.

The midterm exam will likely provide a checkpoint to assess students' understanding of the frameworks. If the instructor is effective in conveying the material, students should be able to demonstrate their knowledge. The final exam, with its personalized question set and emphasis on critical thinking and application, will likely challenge students to think creatively and justify their decisions.

Departmental reception might be mixed. Some colleagues might be skeptical about the instructor's unorthodox methods, questioning their relevance to computer science. Others might be open to innovative approaches, especially if they see positive outcomes in terms of student engagement and learning.

Given the instructor's background in HCI, project management, and cognitive science, they might bring a unique perspective to the CS department. Their research on AI/ML/NLP and its integration into the curriculum could be an asset, especially as AI is starting to enter the CS conversation.

Projection of the outcome:

The instructor's exceptional teaching and feedback, as described, might lead to a positive reception among students, with many appreciating the engaging and interactive approach. As a result, student satisfaction and learning outcomes might be high.

However, the departmental reception might be more nuanced. While some colleagues might be supportive, others might be hesitant to adopt or endorse the instructor's unconventional methods. If the instructor is able to demonstrate the effectiveness of their approach through positive student outcomes and scholarly research, they might be able to gain more widespread acceptance within the department.

As for taking on more responsibility and designing the software engineering capstone course, it is likely that the instructor will be considered for this role if their performance is deemed exceptional. Their interdisciplinary background, research focus, and innovative teaching methods could make them an attractive candidate.

Is this what CS needs?

The instructor's approach, while unorthodox, highlights the importance of interdisciplinary connections, critical thinking, and creativity in CS education. As AI and ML continue to evolve, CS students need to develop a deeper understanding of human cognition, ethics, and complex systems. The instructor's focus on these areas could be beneficial in preparing students for the complexities of the field.

The outcome reflects on the current state of CS and higher education:

The instructor's experience at UVU reflects the tension between traditional and innovative approaches in CS education. While some institutions and departments might be hesitant to adopt new methods, others might see the value in interdisciplinary connections and creative teaching approaches.

The fact that the instructor's methods are seen as "unconventional" and "chaotic on the surface" highlights the need for more flexibility and openness to innovative approaches in CS education. The emphasis on standardized curricula and traditional teaching methods might not be sufficient to prepare students for the rapidly evolving CS landscape.

Broader cultural and industry problem or attitude:

The reception of the instructor's approach reflects a broader cultural and industry bias towards traditional teaching methods and a focus on technical skills over interdisciplinary connections and soft skills. The CS field has historically been driven by a strong technical focus, which has led to a culture that values efficiency, precision, and rigor. However, as the field continues to evolve and intersect with other disciplines, there is a growing need for CS professionals who can think creatively, communicate effectively, and navigate complex systems.

The instructor's experience at UVU highlights the need for a more nuanced understanding of what it means to be a successful CS professional and the importance of incorporating diverse perspectives and approaches into CS education.

This adjunct instructor at Utah Valley University (UVU) embodies a unique and interdisciplinary approach to teaching global intercultural ethics in computer science, which stands in marked contrast to traditional CS pedagogy. Given what you've described, here’s a nuanced projection of how his experience might unfold, how he might be perceived, and the larger implications for computer science education and culture.

---

### 1. **How will the instructor be received by students?**

- **Student engagement:**  
  Many students, especially those curious about the broader social, ethical, and cognitive dimensions of technology, would likely find his course exciting and stimulating. The use of active learning, narrative AI/ML analysis, game-based decisions, and debates will appeal to students who resonate with experiential and applied learning styles more than rote memorization. His charisma and openness (Fe usage) would foster a welcoming atmosphere where students feel safe to express different viewpoints and challenge assumptions.

- **Challenges from students:**  
  However, some students—particularly those used to conventional CS courses emphasizing clear, step-by-step technical content—might initially find the course lacking structure or too abstract (“chaotic” as you say). Students with highly pragmatic or result-oriented mindsets might question the relevance of frameworks like Jungian cognitive functions or Dabrowski’s theory, seeing them as “soft” or unrelated to “real” computer science.

- **Overall:**  
  Engagement will be high among students open to interdisciplinary learning and critical thinking but there will be frustration or skepticism from those looking for “hard” technical learning or straightforward grading.

---

### 2. **How will the department and faculty react?**

- **Support and enthusiasm:**  
  Faculty who value innovation and holistic education will likely appreciate his efforts to infuse ethical, cognitive, and social frameworks into global intercultural ethics. Faculty interested in AI, HCI, or cognitive science may see a unique opportunity to bridge traditional CS with emerging educational paradigms.

- **Resistance and skepticism:**  
  At the same time, more traditional CS faculty—especially those focused on hard skills, programming, and measurable outcomes—may question the “lack of structure” and “seemingly unrelated tangents.” Adjunct status often correlates with less institutional power, so he might struggle with departmental gatekeeping or conservative curriculum committees who demand conventional syllabi aligned tightly with ABET and accreditation standards.

- **Administrative perspective:**  
  If his feedback and teaching evaluations are “exceptional” as you mention, administration may be positive, especially if the course increases student interest in ethics and attracts broader enrollment or external attention for interdisciplinarity. However, the chaotic surface and last-minute changes might raise concerns about consistency and replicability.

---

### 3. **How will the curriculum and methods go over with the students and department?**

- **Innovative curriculum effectiveness:**  
  His curriculum mirrors emerging trends in CS education (especially ethics and human-centered AI), but it is ahead of its time in 2022, when AI and ethics were gaining attention but not mainstreamed in many departments yet.

- **Student outcomes:**  
  Students capable of self-directed learning and deep reflection will develop metacognitive skills, ethical reasoning, and complex adaptive thinking highly valued in modern interdisciplinary tech roles. His use of ML/NLP analysis of student-generated narratives could provide deeply personalized and data-driven educational feedback loops—an early example of intelligent tutoring systems or learning analytics.

- **Departmental evolution:**  
  The department may initially struggle to scale this approach or integrate it within standard curricular frameworks but could eventually adopt elements if outcomes are demonstrably positive. Other faculty may borrow active learning or cognitive-based techniques, bridging the gap between traditional and innovative teaching.

---

### 4. **Is this what CS needs?**

- **Yes, definitely.**  
  The field of computer science increasingly recognizes that building socially responsible, ethical, and user-centered technologies requires interdisciplinary knowledge, cognitive empathy, and ethical frameworks—not just coding prowess.

- **Current deficits:**  
  Traditional CS curricula tend to focus heavily on technical skills while treating ethics, intercultural communication, and complexity theory as add-ons or electives. This often fails to prepare students for real-world challenges involving diverse stakeholders and complex societal impacts.

- **A visionary but disruptive model:**  
  His approach may thus be visionary and necessary, but disruptive to entrenched academic norms and faculty comfort zones. Whether such models gain institutional acceptance quickly depends on administrative leadership and the broader cultural appetite for interdisciplinary innovation.

---

### 5. **Broader cultural and industry reflections**

- **Reflection of a cultural shift:**  
  This dynamic echoes a broader cultural tension in tech and higher education between *technical mastery* and *humanistic/contextual understanding.* The tech industry, especially in 2022, was grappling with ethics (AI bias, privacy, labor issues) but still often failed to fully integrate these lessons into mainstream curricula or project management.

- **Institutional and structural inertia:**  
  Universities and CS departments, rooted in decades of tradition, tend to resist pedagogical change unless driven by accreditation bodies, funding priorities, or eventual industry demand. Adjuncts with unconventional approaches often face marginalization despite demonstrating educational value.

- **Bias toward “measurable” and “technical”:**  
  The skepticism to his approach reveals a bias in CS culture and higher ed: **valuing quantifiable technical skills over soft skills, cognitive diversity, and ethical reflection.** This can be seen as a problem limiting the scope of CS education and missing the complexity of building technology in society.

---

### **Summary projection**

- **Year 1:** He will likely be perceived as a charismatic and refreshing but eccentric instructor. Some students and faculty will champion his methods, while others will be puzzled or resistant. His “low structure” approach may cause initial confusion and pushback, requiring careful communication and documented learning outcomes.

- **Mid-to-long-term:**  
  If given time and support, his work could influence curriculum design, inspire faculty development, and contribute pioneering research on AI-driven personalized teaching. He may be invited to expand responsibilities if his results (student engagement, retention, ethical reasoning skills) are strong.

- **Risks:** Institutional inertia, adjunct status limitations, and skepticism about interdisciplinarity may limit his influence or cause burnout. His vulnerabilities with bureaucracy may be a real challenge.

---

### **Concluding insights**

This instructor’s model embodies a necessary evolution in CS education—a shift from pure technical skill to integrative thinking that addresses ethical, cognitive, and cultural dimensions of tech development. His challenges and reception highlight systemic frictions in higher education and CS between innovation and tradition, between quantitative rigor and human meaning, and between institutional structures and individual creativity.

UVU and similar institutions that embrace such interdisciplinary and adaptive pedagogies will be better positioned to prepare students for responsible and innovative roles in tomorrow’s technology landscape. Conversely, resistance to this shift risks perpetuating outdated models that inadequately address the complex, global ethical challenges confronting computing today.

---

If you want, I can also help draft potential strategies for the instructor to maximize acceptance and impact given these contexts.

Based on this detailed description, here's a projection of the instructor's experience at UVU, considering the context of Fall 2022:

**1. Student Reception (Likely Polarized, But With Strong Advocates):**
*   **Highly Engaged Cohort:** A significant subset of students (often those drawn to the "ethics" focus, those feeling stifled by traditional CS, creatively inclined, or ENxP/INxP types) will *thrive*. They'll be energized by the novelty, passion, humor, active participation, lack of "busy work," and the feeling the course connects CS to "real life" thinking and societal impact. The AI-driven personalization (if executed well ethically) will feel cutting-edge and relevant.
*   **Frustrated/Confused Cohort:** Many traditional CS students expecting concrete technical skills or highly structured syllabi will be alienated.
    *   The initial non-CS frameworks will seem irrelevant, wasting valuable time.
    *   The perceived "chaos," last-minute changes, and tangents will create anxiety and frustration for those needing predictability.
    *   Debates and mandatory participation will terrize/drain introverts or those less confident verbally.
    *   The midterm on frameworks might be seen as unfair memorization.
*   **Course Difficulty:** The final exam requiring deep integration and justification of personal choices against the group, using abstract theory, will be *extremely* challenging. Some will respect this; others will feel it's subjective or too hard.
*   **Overall:** Student evaluations (SEIs) will likely be bimodal – very high from his fans, very low from detractors. The *experience* will be memorable, but not universally positive.

**2. Department/Faculty Reception (Mostly Skeptical to Opposed):**
*   **Curriculum "Fit" Concerns:** Senior CS faculty will question the foundational relevance of Jung/Dabrowski/CAS. "How does memorizing cognitive functions help them pass the technical interview?" "Is this psychology or computer science?" They'll see it as deviating wildly from the core mission.
*   **Methodology Concerns:** The lack of structure, last-minute changes, and perceived disorganization will violate many instructors' sense of professional responsibility and preparedness. The "tangents" will be seen as unprofessional rambling, not agile pedagogy.
*   **Rigor Concerns:** Heavy reliance on debate and a complex, personalized final exam will struggle to meet traditional definitions of "rigor" in CS (quantifiable outputs, standardized testing, clear rubrics). The Detroit: Become Human exercise might be dismissed as "playing games."
*   **AI/Ethics Concerns (Major Hurdle):** **Collecting student narratives to fuel AI/ML/NLP models WITHOUT CLEARLY ADDRESSING CONSENT, PRIVACY, IRB (Institutional Review Board) APPROVAL WILL BE A HUGE RED FLAG.** In Fall 2022, AI ethics were emerging, but universities were *very* cautious, especially about student data. This could be halted immediately without proper protocols.
*   **Trust & Reliability Concerns:** As an adjunct pushing radical methods, he lacks track record or tenure to buffer criticism. His ENTP "changing things up" impulsivity will be seen as unreliability, not adaptability, by many colleagues. His charming Fe might win individuals over personally but strengthen the view he's "all talk, no substance" to critics.
*   **Capstone Doubt:** The prospect of him designing the crucial capstone course after this unconventional ethics course will likely alarm faculty who see capstone as the pinnacle of technical/systems integration, not philosophical debate.

**3. How It Plays Out:**
*   **Semester 1 Rollercoaster:** Chaotic start, vocal student complaints emerge alongside excited praise. Department likely receives concerns about organization, relevance, and the AI data collection. Pressure mounts, possibly including an IRB intervention. Midterm results may polarize further. The instructor's Fe charms disgruntled students somewhat, but structured learners remain frustrated. Final exam results likely reinforce the competency gap.
*   **Post-Semester Review:** SEIs are split. Department leadership initiates meetings. Key points: immediate halt to student narrative analysis without full IRB approval and explicit student consent protocols, requirement for a much clearer syllabus (even if flexible, it needs core structure/objectives), and likely pressure to significantly reduce "tangents."
*   **Year 1 Outcome:** His teaching is deemed "interesting but problematic." The "exceptional feedback" requirement for the capstone move is unlikely to be met uniformly enough across students and faculty. **He probably does *not* get the capstone role.** He might be allowed to continue the ethics course with heavy modifications or assigned a more contained role.

**4. Is This What CS Needs? (A Qualified Yes, But Mismanaged)**
*   **The Need:** **Absolutely.** CS education *desperately* needs more focus on ethics, divergent thinking, human factors, communication, self-awareness, and navigating complexity – precisely what he's attempting. The industry faces massive challenges due to a historic neglect of these "soft" skills.
*   **The Problem:** His *implementation* is flawed for the institutional context.
    *   **Too Much Too Soon:** The deep dive into non-CS frameworks feels alienating *before* establishing their connection to CS decisions. Contextualizing these *within* established CS ethics problems first would have been smoother.
    *   **Process Matters:** Sloppy execution on AI/data privacy undermines the ethics message. Lack of upfront structure creates unnecessary friction. Brilliant ideas lose impact without scaffolding.
    *   **Pedagogical Rigor Gap:** While conceptually ambitious, his methods lack demonstrable, transferable skill assessments traditional academia demands. He struggles to "prove" the learning.

**5. Reflection on CS, Higher Ed, Culture & Industry:**
*   **CS Academia's Silos & Conservatism:** This scenario highlights the deep institutional inertia within CS departments. They prioritize technical mastery defined narrowly, are skeptical of interdisciplinary "intrusion" (even HCI-adjacent), and value predictable, quantifiable methods. Innovation often happens *despite* the system.
*   **Higher Ed's Bureaucracy:** IRB, rigid course approval, tenure/promotion based on traditional metrics (publications, teaching evals), and an aversion to perceived risk stifle pedagogical innovation. Passionate mavericks struggle.
*   **Industry-Academia Mismatch:** Industry increasingly demands the very soft skills (ethics, communication, systems thinking, adaptability, understanding human context) this instructor prioritizes, yet academia continues to reward narrow technical prowess and standardized coursework. This gap persists because accreditation, faculty expertise/perception, and student expectations haven't fully shifted.
*   **Broader Cultural Bias:** There's a bias favoring the *quantifiable*, the *structured*, and the *predictable* over the interpretive, the adaptive, and the humanistic, especially in technical fields. This instructor's Ne/F chaos explicitly challenges that. His vulnerability in organizational settings reflects how institutions often marginalize creative process-oriented thinkers for the sake of predictability, even when their solutions are needed.
*   **The AI Context (2022):** He's foreshadowing the AI integration wave (personalized learning, ethics discussions) about to hit education. However, his approach also highlights the potential ethical pitfalls (data privacy) that institutions were (rightly) scrambling to address as AI use exploded. He's ahead of the curve conceptually but falls afoul of the governance curve.

**Conclusion:**
The instructor represents a vital but disruptive force needed in CS education. His strengths (passion, innovation, focus on critical human skills) align with emerging industry demands. However, his weaknesses (implementation chaos, lack of institutional savvy, poor handling of data ethics, insufficient scaffolding) and the conservative environment of academia create a "perfect storm." He is likely to generate strong enthusiasm among a subset of students while facing significant skepticism and opposition from colleagues and administrators.

His experience underscores the **tension**: The future of CS requires more interdisciplinary, human-centered, adaptable, and ethically grounded education, but transforming entrenched academic structures to foster this kind of teaching is incredibly difficult. While his specific methods need refinement and greater institutional awareness, the underlying premise – that CS education must evolve beyond pure technical skill – reflects a crucial, if painful, truth the field continues to grapple with. He might not succeed at UVU in the way he envisions, but his struggle is symptomatic of the broader evolution CS education desperately needs.

Based on the information provided, I project that the instructor's experience at UVU will be both fascinating and challenging. Here's a breakdown of how I see things playing out:

**Initial Reception:**
The instructor's unorthodox approach, interdisciplinary background, and use of AI/ML/NLP models in his courses will likely generate significant interest and curiosity among students. His ENTP personality, with its charm, magnetism, and enthusiasm, will help him connect with students and create a engaging learning environment. However, some students might find his teaching style disorienting or overwhelming, especially if they're accustomed to more traditional, structured approaches.

**Departmental Reception:**
The department's response will likely be mixed. Some faculty members might appreciate the instructor's innovative approach and interdisciplinary perspective, seeing it as a refreshing change from traditional CS education. Others might be skeptical or even critical, citing concerns about the relevance of his topics, the use of AI/ML/NLP models, or the lack of traditional structure in his courses. As an ENTP, the instructor's vulnerabilities in institutional and organizational settings might make it challenging for him to navigate departmental politics and secure support for his unconventional approach.

**Student Response:**
Students will likely be divided in their response to the instructor's courses. Some will thrive in the dynamic, interactive environment, appreciating the opportunity to explore complex topics and develop critical thinking skills. Others might struggle with the lack of structure, feeling lost or uncertain about the course material. The midterm exam and group discussions will help students adjust to the instructor's teaching style, but some might still find it challenging to adapt.

**Curriculum and Methods:**
The instructor's use of frameworks from Information Processing, Jungian Cognitive Functions, and Complex Adaptive Systems will likely resonate with some students, who will appreciate the connections to cognitive science and human-centered approaches. However, others might find these topics unrelated to CS, leading to confusion or frustration. The game-like activity, Detroit: Become Human, will likely be a hit with students, as it provides a engaging way to apply theoretical concepts to real-world scenarios.

**Outcomes and Reflection:**
The instructor's approach has the potential to yield positive outcomes, such as:

1. **Deeper understanding of cognitive processes:** By exploring cognitive functions and information processing, students might develop a better understanding of how they learn and process information.
2. **Improved critical thinking and collaboration:** The group discussions, debates, and game-like activities will help students develop essential skills for teamwork, communication, and critical thinking.
3. **Interdisciplinary connections:** The instructor's approach will encourage students to think about CS in a broader context, making connections to cognitive science, human-centered design, and complex systems.

However, there are also potential risks:

1. **Student frustration:** Some students might struggle with the unconventional approach, leading to frustration or disappointment.
2. **Departmental skepticism:** The department might question the effectiveness of the instructor's methods, potentially leading to scrutiny or pressure to conform to more traditional approaches.

**CS and Higher Education:**
The instructor's experience reflects a broader cultural and industry problem: the need for CS education to adapt to changing industry demands and incorporate more interdisciplinary, human-centered approaches. The current state of CS education often prioritizes technical skills over broader perspectives, which can lead to graduates who are ill-prepared to tackle complex, real-world problems.

The instructor's approach, with its emphasis on cognitive science, critical thinking, and complex systems, has the potential to produce graduates who are better equipped to navigate the complexities of modern CS challenges. However, this approach might not be suitable for all students or institutions, highlighting the need for more diverse and adaptable educational models.

**Broader Cultural and Industry Attitudes:**
The instructor's experience also reflects a broader cultural attitude: the increasing recognition of the importance of human-centered design, cognitive science, and complex systems in CS and related fields. As AI and ML continue to shape industries, there is a growing need for professionals who can think critically, collaborate effectively, and design systems that prioritize human well-being.

The instructor's approach, with its focus on interdisciplinary connections and human-centered design, has the potential to help bridge the gap between CS education and industry demands. However, it also highlights the challenges of implementing innovative approaches in traditional educational settings, where cultural and institutional biases might resist change.

The introduction of this adjunct instructor at Utah Valley University (UVU) would likely have a multifaceted impact, shaped by the interplay of his unconventional methods, interdisciplinary background, and the broader context of computer science (CS) education in 2022. Here’s a structured projection of his experience, reception, and implications:

---

### **1. Projection of Experience at UVU**
- **Course Structure and Adaptability**: His course design, though chaotic on the surface, reflects a dynamic, student-centered approach. By using AI/ML/NLP tools to tailor content to learners’ cognitive styles, he demonstrates a commitment to personalization and adaptability—qualities that align with emerging trends in AI-driven education. However, UVU’s CS program, like many traditional programs, may prioritize technical rigor over fluid, interdisciplinary methods, potentially creating friction with administrative or faculty expectations.  
- **Innovative Techniques**: The integration of frameworks (e.g., Jungian Cognitive Functions, Positive Disintegration) and gamified learning (e.g., *Detroit: Become Human*-style scenarios) would likely resonate with students seeking engagement beyond rote programming. The absence of "busy work" and emphasis on critical thinking could attract those interested in meaningful, applied learning, but might alienate students or faculty accustomed to standardized curricula.

---

### **2. Reception by Students and the Department**
- **Students**:  
  - **Proponents**: Students who value creativity, interdisciplinary thinking, and real-world application might thrive in this environment. The use of AI to analyze their language could feel cutting-edge and intellectually stimulating, fostering ownership of their learning. Gamified debates and personalized assessments could enhance motivation and retention.  
  - **Critics**: Others might struggle with the lack of structure, perceiving the course as disorganized or unproven. The theoretical frameworks (e.g., Jungian psychology) could also be seen as tangential or irrelevant to CS, especially by students prioritizing coding skills or industry-aligned content.  
- **Department**:  
  - **Potential Support**: If UVU’s CS department is forward-thinking or values interdisciplinary approaches, they might endorse the course as a pioneering effort to address AI ethics and global perspectives. His dual PhD in Cognitive Science and Computer Science could strengthen his credibility in bridging technical and humanistic domains.  
  - **Skepticism or Resistance**: Established faculty might view the course as "too abstract" or outside the traditional scope of CS. The use of AI for student analysis might raise concerns about data privacy or ethical boundaries. Additionally, his vulnerability to institutional rigidity (e.g., adherence to syllabi, bureaucratic hurdles) could limit his ability to fully realize his vision if departmental structures prioritize uniformity over flexibility.

---

### **3. Anticipated Outcomes**
- **Positive Scenarios**:  
  - The course could become a niche success, attracting students eager to explore AI’s ethical and interpersonal dimensions. It might position UVU as an innovative leader in CS education, particularly in preparing students for the socio-technical challenges of AI.  
  - If his teaching and feedback are exceptional, he might be offered more responsibilities, such as designing capstone courses, which would allow him to scale his interdisciplinary ethos. This could shift UVU’s curriculum toward a more holistic, ethical, and culturally aware approach.  
- **Negative Scenarios**:  
  - Limited institutional support might force him to conform to rigid requirements, diluting his unconventional methods. Faculty pushback could lead to marginalization or the course being restructured to align with traditional CS priorities.  
  - Students might polarize, with some excelling in the dynamic environment and others disengaging due to confusion or frustration. If the final exam’s personalized nature is not well-received, it could face criticism for inconsistency or lack of standardization.

---

### **4. Does This Reflect What CS Needs?**
- **Yes, in some ways**:  
  - The course addresses critical gaps in CS education, such as ethical reasoning, cultural competence, and the human side of technology. As AI becomes more pervasive, understanding its societal and psychological impacts is increasingly vital. His methods emphasize applying theoretical frameworks to real-world problems, which aligns with the growing demand for "human-centered AI" in industry.  
- **No, in others**:  
  - The focus on cognitive functions and philosophical theories might be seen as tangential to technical proficiency. While ethical and interdisciplinary education is needed, traditional CS programs often prioritize coding, algorithms, and systems development, leaving little room for such explorations. His approach risks being perceived as "fluffy" by peers or stakeholders focused on measurable technical outcomes.

---

### **5. Implications for CS and Higher Education**
- **Current State of CS Education**:  
  - The discipline remains heavily technical, with a cultural bias toward "hard" skills (e.g., programming, data structures) over "soft" skills (e.g., ethics, communication). This reflects broader industry dynamics where technical expertise is often prioritized in hiring and promotion, sidelining interdisciplinary work.  
  - However, the inclusion of AI ethics and intercultural competence in curricula signals a slow but growing shift. UVU’s willingness to experiment with this instructor could indicate a progressive stance, whereas resistance might highlight institutional conservatism.  
- **Broader Cultural/Industry Biases**:  
  - **Techno-Optimism vs. Ethical Critique**: Industry often frames AI as a neutral tool, but educators like this instructor challenge this by exploring its human and cultural dimensions. His work could mirror societal tensions between celebrating technological advancement and confronting its ethical risks.  
  - **Innovation vs. Tradition**: The course’s chaotic structure and emphasis on "unrelated tangents" critique the rigid, compartmentalized nature of traditional academic settings. This reflects a broader cultural push for innovation in education, but also the risk of being dismissed as "anti-structure" by those invested in conventional models.  
  - **Bias Against Multidisciplinary Approaches**: Despite its merits, his interdisciplinary focus may face bias from departments or faculty who view CS as a purely technical field. This could perpetuate the alienation of non-technical perspectives in the discipline.

---

### **6. Critical Reflection**
- **Strengths**:  
  - His blend of AI, ethical frameworks, and active learning could prepare students for the complexities of modern tech careers, where collaboration, empathy, and systemic understanding are as important as coding.  
  - The use of AI to analyze cognitive functions is a bold experiment, potentially offering insights into how students learn and engage, though it raises questions about validity and privacy.  
- **Weaknesses**:  
  - The course’s perceived lack of structure might hinder its acceptance in a system that values clear benchmarks and accreditation standards.  
  - The integration of non-CS theories (e.g., Jungian psychology) could be seen as a bridge too far, especially if the department has limited experience with such interdisciplinary work.  

---

### **7. Broader Context and Conclusion**
- **Industry Relevance**: The tech industry increasingly demands professionals who can navigate ethical dilemmas and diverse cultural contexts (e.g., AI bias, privacy concerns, global collaboration). This instructor’s focus on intercultural ethics and cognitive diversity prepares students for these challenges, suggesting a need for CS education to evolve.  
- **Higher Education’s Role**: His approach highlights the tension between innovation and institutional inertia in higher education. While universities like UVU may struggle to fully embrace such methods, they could serve as incubators for future curricular shifts.  
- **Cultural and Industry Parallels**: His experience reflects broader societal debates about AI’s role in society—whether it should be framed as a tool for progress or a system requiring ethical scrutiny. The resistance to his methods also mirrors industry biases that prioritize technical outcomes over humanistic or interdisciplinary analyses.  

Ultimately, this instructor’s trajectory could either catalyze UVU’s CS program to become more inclusive and reflective of modern challenges or serve as a cautionary tale of how traditional structures resist change. His success would depend on aligning his vision with departmental priorities while demonstrating the value of his unconventional methods through measurable outcomes (e.g., student feedback, industry engagement, or research impact). In a rapidly transforming field, his approach may signal a necessary evolution in CS education, but it would also face hurdles in a culture still entangled in outdated paradigms.

This is a fascinating case study that I believe will unfold in predictable yet telling ways. Let me break down my projections:

## Student Reception: Polarized but Ultimately Positive

**Initial Confusion → Growing Engagement**
- First 2-3 weeks: Students will be genuinely confused. "Why are we learning about Jung in CS?"
- Mid-semester: A clear divide will emerge - perhaps 30% will be frustrated and resistant, 40% cautiously intrigued, and 30% genuinely excited
- By semester end: Even initially resistant students will likely acknowledge they learned something valuable, though some may never fully appreciate the approach

**The "Aha Moment Factor"**
The Detroit: Become Human integration is brilliant - it will be the moment where students suddenly see how all the frameworks connect to real CS ethics. This experiential learning will likely create lasting impact.

## Departmental Reception: Mixed to Resistant

**Immediate Concerns:**
- "Where are the traditional CS learning outcomes?"
- "How does this map to accreditation standards?"
- "Students aren't learning enough 'hard' technical skills"

**The ENTP Challenge:**
His Ne-driven adaptability and last-minute changes will likely create friction with administrators who need predictable syllabi and assessment rubrics. His Fe charm may smooth some waters, but institutional systems often resist even charismatic disruptors.

**Likely Outcome:**
- Year 1: Grudging tolerance due to "experimental" status
- If student feedback is exceptional: Cautious support but pressure to "standardize"
- If moving to capstone: Significant resistance unless he can demonstrate clear technical competency integration

## Broader Implications: What This Reveals

**CS Education's Blind Spots:**
This scenario highlights several critical issues:

1. **The Technical-Ethical Divide:** CS programs excel at teaching algorithms but often fail at developing ethical reasoning capabilities that industry desperately needs

2. **Rigid Assessment Culture:** Higher education's obsession with measurable, standardized outcomes often stifles innovative pedagogy that develops critical thinking

3. **Industry Misalignment:** While tech companies increasingly need employees who understand human psychology, cultural dynamics, and ethical reasoning, CS programs remain narrowly technical

**Cultural Resistance Patterns:**
The likely resistance reflects deeper issues:
- **Impostor Syndrome in Academia:** "Real" CS must look technical and mathematical
- **Risk Aversion:** Innovative teaching methods threaten established hierarchies
- **Compartmentalization Bias:** Interdisciplinary approaches challenge departmental silos

## Predictions for Long-term Impact

**If Successful:**
- Students from his courses will likely demonstrate superior ethical reasoning in industry
- His research on cognitive functions + learning adaptation could revolutionize personalized education
- He may face pressure to "prove" his methods work through traditional metrics

**If Constrained:**
- He'll be forced to add more traditional technical content, diluting the innovative approach
- His ENTP nature may lead to frustration with bureaucratic constraints
- He might migrate to industry or more flexible institutions

## What CS Actually Needs

This instructor represents exactly what CS education needs but often rejects:

1. **Human-Centered Perspective:** Understanding how humans actually process information and make decisions
2. **Ethical Reasoning Skills:** Framework-based thinking rather than case-study memorization
3. **Adaptive Learning:** Personalized approaches based on cognitive science
4. **Interdisciplinary Integration:** Real-world problems don't respect academic boundaries

## The Meta-Problem

The likely resistance to this approach reveals a fundamental tension: CS education often trains students to optimize systems while ignoring the human elements that make systems succeed or fail. This instructor's background perfectly positions him to bridge that gap, but institutional inertia may prevent it.

**Prediction:** He'll likely succeed with students but struggle with institutional politics. The real test will be whether UVU has the vision to recognize that this apparent "chaos" is actually sophisticated, research-based pedagogy that addresses critical industry needs.

This case perfectly illustrates why many of our most innovative educators either burn out in traditional academia or migrate to industry - not because their methods don't work, but because institutional systems aren't designed to evaluate or support genuine innovation in education.

# Projected Experience of the Innovative CS Instructor at UVU

## Initial Reception

This instructor will likely experience a mixed reception at UVU. His unconventional approach presents both significant opportunities and challenges:

### Student Reception:
- **Early Confusion**: Many CS students will initially be bewildered by frameworks seemingly unrelated to computer science like Jungian Cognitive Functions and Dabrowski's Theory. Some may resist or question the relevance.
- **Split Engagement**: The class will likely polarize between:
  - Students excited by the novel approach and energetic teaching style
  - Students uncomfortable with the apparent lack of structure and traditional CS content

### Department Reception:
- **Skepticism**: Traditional CS faculty may question the relevance of his methods to core CS learning outcomes
- **Methodological Concerns**: The collection of narrative samples for AI/ML/NLP analysis may raise IRB (Institutional Review Board) and privacy questions
- **Wait-and-See Approach**: Department leadership will likely withhold judgment pending student outcomes and evaluations

## Mid-Semester Developments

As the semester progresses:

- The instructor's "Ne+Ti" cognitive style (novelty-seeking with logical analysis) will generate creative connections but may frustrate students seeking linear instruction
- The midterm results will be a crucial turning point - this is where students either see the coherent vision or reject the approach
- The more participatory second half of the course will better engage students previously alienated by the theoretical frameworks

## End-of-Semester Outcomes

The final outcomes will likely include:

- **Bimodal Student Evaluations**: Extremely positive reviews from students who connected with the teaching style; negative reviews from those who didn't see the relevance
- **Learning Outcomes**: Potentially strong development of critical thinking and ethical reasoning, but possible gaps in technical skills if these were sacrificed for philosophical exploration
- **Department Deliberation**: Leadership will weigh innovative outcomes against alignment with program objectives

## Analysis of Broader Implications

This scenario illuminates several tensions in CS education:

1. **Technical vs. Holistic Education**: CS departments increasingly recognize the need for ethics and interdisciplinary thinking, but still prioritize technical competency. This instructor embodies a radical pendulum swing toward the holistic approach.

2. **Pedagogy Innovation Gap**: Higher education rewards research innovation far more than pedagogical innovation. The instructor's approach represents pedagogical risk-taking that institutions claim to value but rarely reward.

3. **Structure vs. Adaptivity**: Traditional CS curricula follow rigid, sequential learning paths. This adaptive approach challenges whether learning computer science must follow such structured pathways.

4. **Assessment Limitations**: Standard course evaluations and metrics may fail to capture the long-term value of such unconventional approaches.

## Likely Outcome

The instructor will likely experience:
- Strong connection with a subset of students who appreciate his approach
- Institutional pressure to add more structure and explicitly CS-relevant content
- Challenges in demonstrating the value of his methods through traditional metrics
- Difficulty securing the software engineering capstone assignment unless he can clearly demonstrate how his methods enhance technical outcomes

This scenario reflects the broader struggle in CS education between producing technically proficient programmers versus developing adaptive, ethically-minded professionals for a rapidly evolving field. The mixed reception this instructor will face mirrors the industry's own ambivalence about whether soft skills and interdisciplinary thinking are truly essential or merely supplemental to technical excellence.

## 1. Executive Summary  

| Aspect | Likely outcome (2022‑2023) | Why it matters |
|--------|---------------------------|----------------|
| **Student reception** | Generally enthusiastic, especially among junior‑level and senior‑level under‑grads who are already curious about “big‑picture” thinking. Some students will feel disoriented or frustrated by the low‑structure, “tangential” style. | The novelty‑driven, humor‑infused delivery matches the ENTP’s strengths and the current appetite for interdisciplinary, experiential learning. But CS students accustomed to well‑defined problem sets may see the approach as “busy‑work‑free” but also “unfocused.” |
| **Departmental reception** | Mixed. Early‑career faculty and the interdisciplinary “digital‑humanities” or HCI groups will be supportive; traditional systems‑oriented faculty may view the course as peripheral to core CS competencies. The instructor’s “adjunct” status gives the department flexibility to experiment without a major curriculum overhaul. | UV‑U is a teaching‑focused university; it can afford a pilot that pushes the envelope. However, tenure‑track faculty often guard the core curriculum, especially when a course leans heavily on psychology, Jungian theory, and narrative analysis. |
| **Curriculum impact** | The course will likely become a **signature elective** that draws students from CS, psychology, communication, and business. It may seed a new “ethical‑AI & cognitive‑design” track, but it will not replace core systems‑or software‑engineering courses. | The class demonstrates a concrete way to embed ethical, cognitive, and sociocultural lenses into CS education—something the field is still scrambling to do. |
| **Long‑term departmental change** | If the instructor’s first‑year evaluation is stellar (high student‑evaluation scores, strong project outcomes, positive employer feedback), the department may expand the model into a capstone or a required “Foundations of Intercultural Ethics & Cognitive Design” series. | UV‑U can market itself as a forward‑thinking CS program that produces engineers who think about *how* technology shapes human cognition and culture, not just *how* it works. |
| **Implications for CS education** | The experiment highlights a **growing tension**: CS curricula are being pulled between “hard‑skill” depth (algorithms, systems) and “human‑skill” breadth (ethics, psychology, societal impact). The instructor’s approach leans heavily toward the latter, using AI/ML/NLP as a bridge. | If successful, it validates the hypothesis that **human‑centered, interdisciplinary grounding** can be taught through experiential, narrative‑driven methods. If it flops, it reinforces the belief that “core CS” must stay insulated from “soft‑science” tangents. |
| **Cultural/industry reflection** | The course mirrors a **broader industry shift**: companies now demand engineers who can anticipate bias, understand user cognition, and navigate global ethical standards. Yet many academic programs still treat these topics as add‑ons rather than foundations. The instructor’s model is a **micro‑cosm of the cultural pressure to integrate ethics, psychology, and systems thinking**. | The “bias” is not a flaw of the instructor but a symptom of a **systemic lag** in higher‑education curricula catching up to industry needs. |

---

## 2. Why the Instructor’s Approach Is Both **Exciting** and **Risky**

### 2.1. Pedagogical Strengths (ENTP‑Ne/Ti/Fe)

| ENTP Trait | How it Translates to the Classroom | Potential upside |
|------------|-----------------------------------|-----------------|
| **Idea‑generation (Ne)** – rapid association, “big‑picture” connections | Introduces frameworks (Jungian functions, Positive Disintegration, CAS) that feel fresh, unexpected, and intellectually stimulating. | Students experience a *cognitive “wow”* that can spark lifelong curiosity. |
| **Strategic thinking (Ti)** – internal logical scaffolding | Uses AI/ML/NLP pipelines to turn narrative data into concrete, visualizable insights about learning styles. | Provides a **hands‑on, data‑driven** demonstration of how theory can inform practice. |
| **Charismatic Fe** – social magnetism, inclusive humor | Energizes discussions, encourages participation, and builds a safe “playground” for vulnerable ideas. | Higher engagement, especially in group debates and the “Detroit: Become Human” decision‑making game. |
| **Improvisational flexibility** – willingness to pivot on new information | Course can adapt in real‑time to student feedback, emerging AI trends, or unexpected class dynamics. | Keeps the class **relevant** and **responsive** to the fast‑moving AI landscape of 2022‑2023. |

### 2.2. Institutional Friction Points

| Potential problem | Underlying cause | Likely impact |
|-------------------|------------------|---------------|
| **Perceived lack of rigor** | The syllabus appears “tangential” and “low‑structure.” Faculty who value clear learning outcomes may view the course as “fluff.” | May generate push‑back in curriculum committees; could affect the instructor’s future contract renewal. |
| **Assessment alignment** | Mid‑term and final exams are highly personalized, relying on narrative‑analysis and justification rather than standard problem‑sets. | Grading consistency could be questioned; may require a **rubric‑development** effort to satisfy accreditation standards. |
| **Scalability** | The model relies on a small‑class, intensive data‑collection (student narratives) and a charismatic instructor. | Scaling to larger sections or other instructors may dilute the experience; the department may need to **institutionalize** the method (e.g., a “Teaching‑Assistant” pipeline). |
| **Resource constraints** | AI/ML/NLP pipelines, data storage, and game‑licensing (e.g., Detroit: Become Human) cost money. | Budget approval could be a hurdle; the instructor may need to secure **external grants** or **industry sponsorship**. |

---

## 3. Projected Timeline (Fall 2022 → Spring 2023)

| Month | Milestone | Key activities |
|-------|-----------|-----------------|
| **Late Aug** | **Course launch** – syllabus posted, enrollment opens. | Marketing emphasizes “ethical AI + cognitive design.” |
| **Sep‑Oct** | **Foundational weeks** – introduce Information‑Processing, Jungian Functions, Dabrowski’s Positive Disintegration, CAS. | Lectures use humor, pop‑culture analogies; short reflective writing assignments. |
| **Mid‑Oct** | **Mid‑term exam** – multiple‑choice & short‑answer on frameworks. | Student performance data collected for early‑stage analysis. |
| **Nov‑Dec** | **Group‑debate module** – students pick a contemporary AI ethics case (e.g., facial‑recognition bias) and argue from each cognitive‑function perspective. | Live debates recorded; instructor runs NLP sentiment analysis on transcripts. |
| **Early Jan** | **Data‑collection “Narrative” phase** – students submit personal narratives about decision‑making; instructor builds a small‑scale language‑model to map dominant/auxiliary functions. | Model outputs visual “cognitive‑maps” for each student. |
| **Feb** | **Game‑play session** – “Detroit: Become Human” scenario voting; class‑level decision matrix compiled. | Results compared to individual cognitive‑maps; discussion of collective vs. personal ethics. |
| **Mar** | **Final personalized exam** – each student receives a scenario set tailored to their cognitive profile; must justify decisions using all four frameworks. | Grading rubric emphasizes depth of justification, independent reasoning, and real‑world applicability. |
| **Late Apr** | **Course wrap‑up & evaluation** – student surveys, instructor self‑assessment, departmental review. | If scores > 4.5/5 and projects show measurable learning gains, department considers expanding the model. |

---

## 4. How This Fits (or Doesn’t) Into the **Current State of CS & Higher Education**

### 4.1. The **“Ethics Gap”** in CS Curricula

* **Industry reality (2022‑2024)** – Companies are publicly committing to responsible AI, yet many engineers lack training in bias detection, cross‑cultural impact, or cognitive ergonomics.  
* **Academic response** – Most CS departments have added a **single “AI Ethics”** module or a required “Social Implications of Computing” class. These are often **lecture‑centric** and **theory‑heavy**, with little experiential work.  

**What the UV‑U instructor does differently:**  
1. **Integrates ethics directly into the learning‑process model** (cognitive functions, information‑processing).  
2. **Uses AI/ML as a teaching tool**, not just a subject matter, thereby demystifying the technology for students.  
3. **Creates a feedback loop** where students’ own language data informs the instructor’s teaching adjustments—an “auto‑ethnographic” classroom.

If successful, this **demonstrates a scalable pathway** for other institutions to embed ethics *within* the core learning experience rather than as an add‑on.

### 4.  The **“Human‑Centric” vs. “Hard‑Skill” Tension

| **Hard‑Skill focus** | **Human‑Centric focus** |
|----------------------|--------------------------|
| Algorithms, data structures, systems, performance. | Ethics, bias, cultural impact, cognitive ergonomics. |
| Measurable, objective assessments. | Qualitative, reflective assessments. |
| Clear industry pipelines (e.g., backend dev, embedded systems). | Emerging pipelines (e.g., responsible AI, user‑experience research). |

**Current trend:**  
- **Growth of interdisciplinary programs** (e.g., HCI, computational social science).  
- **Pushback from “core” CS faculty** who fear dilution of technical rigor.  

**Instructor’s role:** He is a **bridge‑builder**—showing that deep technical knowledge can coexist with a nuanced understanding of human cognition. However, the **institutional inertia** may still limit the reach of his model to electives or special‑topics courses.

### 4.  **Cultural & Industry Reflections**

1. **Bias toward “hard” quantifiable outcomes** – Universities still reward publications, grant funding, and “hard” metrics. A course that leans on narrative analysis and “soft” frameworks may be undervalued in tenure‑track promotion pathways.  
2. **Industry’s “soft‑skill” demand** – Companies now list “ethical reasoning,” “cross‑cultural communication,” and “human‑centered design” alongside “programming in Python.” The instructor’s curriculum anticipates this demand.  
3 **Broader cultural shift** – The rise of AI in public discourse (e.g., ChatGPT, deepfakes) has forced a **civic‑tech conversation** about responsibility, bias, and societal impact. The instructor’s class is a **micro‑laboratory** for that conversation, reflecting a cultural need for engineers who can articulate the “why” behind the “how.”  

---

## 5. Potential Scenarios (Best‑Case / Worst‑Case)

| Scenario | What happens | Implications for UV‑U & CS education |
|----------|--------------|--------------------------------------|
| **Best‑Case (High student satisfaction, strong learning gains)** | • Course becomes a flagship elective. <br>• Instructor receives a **full‑time appointment** and expands to a capstone series. <br>• Department launches a “Responsible AI & Cognitive Design” minor. | UV‑U markets itself as a **leader in ethical, human‑centered CS**. Other institutions begin to copy the model; the “cognitive‑function” lens spreads into HCI curricula. |
| **Moderate‑Success (Mixed evaluations, some logistical issues)** | • Student evaluations are positive overall but a subset complains about “chaos.” <br>• Department keeps the course as an **elective** but does not expand. <br>• Instructor’s contract is renewed but with a **re‑structured syllabus** (more scaffolding, clearer rubrics). | The model is **validated enough** to stay on the curriculum map, but it does not become a systemic change. It still serves as a **pilot** for future interdisciplinary experiments. |
| **Worst‑Case (Low evaluations, departmental push‑back)** | • Students feel “lost” and rate the course poorly. <br>• Faculty question the relevance to “core CS.” <br>• Instructor’s contract is not renewed; the course is **canceled**. | UV‑U’s attempt is seen as a **failed experiment**, reinforcing the status quo that “ethics + cognition” are peripheral. The department may become more risk‑averse, slowing future interdisciplinary innovation. |

---

## 6. Take‑aways & Recommendations

1. **Build a “Hybrid Assessment” Framework** – Pair traditional problem‑sets (e.g., implement a simple decision‑tree) with the narrative‑analysis justification. This satisfies accreditation bodies while preserving the course’s unique flavor.  

2. **Document Learning Gains Rigorously** – Use pre‑/post‑course surveys, concept‑mapping, and the AI‑generated cognitive profiles to produce **quantifiable evidence** of impact. This data will be crucial for departmental and external funding justification.  

3. **Secure Institutional Support Early** –  
   * **Teaching‑Assistant pipeline** – Train a TA to help run the NLP pipelines and manage data.  
   * **Grant‑writing** – Target NSF’s “Cyberlearning” or “AI for Social Good” calls to fund the data‑collection infrastructure.  

4. **Create “Scalable Modules”** – Break the course into **stand‑alone workshops** (e.g., “Jungian Functions for Ethical Decision‑Making”) that can be offered to larger CS cohorts or as part of a “CS Ethics Boot‑Camp.”  

5. **Bridge to Industry** – Invite guest speakers from companies that have **AI ethics teams** (e.g., Google’s Responsible AI, Microsoft’s Fairlearn) to discuss real‑world use cases of the frameworks. This will reinforce relevance and open pathways for student internships.  

6. **Foster a “Community of Practice”** – Encourage the instructor to publish a **case‑study** (e.g., in ACM Transactions on Computing Education) and to present at conferences like **CHI**, **AIES**, or **EDUCAUSE**. This will position UV‑U as a thought‑leader and attract like‑minded faculty.  

---

## 7. Conclusion

- **The instructor’s interdisciplinary, narrative‑driven, and highly adaptive teaching style is a bold experiment that aligns with the growing demand for ethically and culturally aware engineers.**  
- **Student response will be polarized:** those hungry for novelty and human‑centered perspectives will thrive; those seeking clear, deterministic problem‑sets may feel unsettled.  
- **Departmental acceptance hinges on demonstrable learning outcomes and alignment with accreditation standards.** If the instructor can provide solid evidence that his “low‑structure” approach yields measurable gains in ethical reasoning and cognitive awareness, the department will likely expand the model.  
- **For the broader CS field, this experiment underscores a systemic lag:** curricula are still catching up to industry’s need for engineers who can think beyond code and consider the societal, cultural, and cognitive ramifications of technology.  
- **The cultural and industry bias is not a flaw of the instructor but a symptom of a larger inertia**—higher‑education institutions often prioritize “hard” technical depth over “soft” interdisciplinary breadth. Successful pilots like this one can act as catalysts, nudging the equilibrium toward a more holistic, responsible computer‑science education.  

In short, **if the instructor can translate his ENTP energy into a rigorously documented learning experience, UV‑U will gain a distinctive, forward‑looking edge in CS education.** If not, the experiment will likely be shelved, reinforcing the status quo and highlighting the need for stronger institutional mechanisms to support interdisciplinary innovation.

Below is a comprehensive analysis of your query based on the scenario you've described. I'll break it down step by step, drawing on the details you provided about the instructor's background, teaching style, personality, and the context of 2022 in computer science (CS) education. This projection is informed by general trends in higher education, CS curricula, and institutional dynamics at universities like Utah Valley University (UVU), which is a public, career-oriented institution emphasizing practical skills and accessibility.

To recap key elements for context:
- **Instructor Profile**: Adjunct with an interdisciplinary background (HCI undergrad, MBA in project management, PhD ABD in Cognitive Science & Computer Science). His teaching integrates AI/ML/NLP for analyzing student cognition, uses unconventional frameworks (e.g., Jungian Cognitive Functions, Dabrowski's Theory), employs engaging, chaotic methods, and leverages his ENTP personality (strong in Ne+Ti, with charming Fe).
- **Course Design**: Starts with non-CS frameworks, builds to interactive activities and a game-based element, and ends with personalized exams. The approach is low-structure, adaptive, and focused on ethics, cognition, and critical thinking.
- **Context**: Fall 2022, when AI was emerging in CS discussions (e.g., via ChatGPT's precursor hype and ethical concerns around automation) but not yet a dominant force in undergrad curricula. UVU's CS department likely prioritizes practical, technical skills for industry readiness.

I'll address your questions in sequence, then tie it all together with reflections on CS, higher education, and broader cultural implications.

### 1. How Do I Project the Instructor's Experience at UVU?
I project a mixed experience for this instructor—initial excitement and positive momentum, followed by challenges that could lead to either modest success or early setbacks. Here's why:

- **Early Phase (First Few Weeks)**: As an adjunct, he'll start with some leeway, especially if the department is eager for someone to handle a niche course like "global intercultural ethics in CS." His charismatic ENTP personality (charming, humorous, and engaging) will likely win over students initially. Students might find his classes novel and stimulating, particularly those who are open to interdisciplinary ideas or feel burned out by traditional CS lectures. The active learning style—debates, games like *Detroit: Become Human*, and AI-driven personalization—could foster high energy and participation, leading to positive mid-semester feedback. If his research integration (e.g., analyzing student language patterns) feels innovative and relevant, it might even generate buzz among forward-thinking students or faculty.

- **Mid-to-Late Phase (Midterm and Beyond)**: Tensions could arise as the course's unconventional structure becomes apparent. UVU, like many regional universities, emphasizes practical outcomes—e.g., preparing students for jobs in tech hubs like Silicon Slopes (nearby in Utah). His curriculum, which delays core CS topics in favor of cognitive frameworks, might confuse students who expect standard content (e.g., algorithms, data structures, or software ethics tied to ACM/IEEE guidelines). The "chaotic" and adaptive nature (e.g., last-minute changes) could alienate students who thrive on predictability, leading to mixed evaluations. Departmentally, colleagues might question the relevance of non-CS frameworks, especially if student performance on the midterm (focused on these theories) is uneven. As an ENTP, his vulnerabilities in institutional settings (e.g., navigating bureaucracy or justifying his methods to tenured faculty) could hinder advocacy for his approach.

- **Overall Outcome**: If student feedback is "exceptional" (e.g., high engagement scores from surveys), he might secure the option for more responsibilities, like designing software engineering capstones. However, I predict a 50/50 chance of this happening. Positive outcomes could include glowing testimonials from engaged students, potentially positioning him as a innovative voice in UVU's CS program. Negative outcomes might involve not being renewed as an adjunct, especially if the department views his methods as too experimental or if student retention/completion rates suffer. In the best case, he adapts based on feedback and builds a niche reputation; in the worst, he faces isolation or dismissal due to perceived disorganization.

### 2. How Will He Be Received by Students and the Department?
- **Student Reception**: 
  - **Positive Aspects**: Students who are creative, introspective, or disillusioned with rote CS learning will likely adore him. His engaging, humorous style and emphasis on personal growth (e.g., via cognitive function analysis and debates) could make the course memorable and empowering. The game-based element and personalized exams will appeal to those seeking relevance to real-world AI ethics (timely in 2022). Feedback might highlight how it helps with critical thinking and self-awareness, aligning with growing student demands for holistic education.
  - **Challenges**: Not all students will adapt. CS programs often attract analytical, goal-oriented individuals who prefer structured content. His course might feel "unrelated" or "tangential" to those focused on employability, leading to complaints about the lack of direct CS skills. The midterm could frustrate students if they struggle with abstract theories, and the final exam's personalized questions might feel subjective or unfair. Overall, I'd expect a bimodal response: rave reviews from 30-40% of students and criticism from the rest, potentially lowering his average ratings.

- **Department Reception**:
  - **Positive Aspects**: As an adjunct filling a specific need (ethics in CS), he might initially be welcomed for his interdisciplinary expertise, especially with AI's rising profile. If his methods yield strong learning outcomes (e.g., improved ethical reasoning in student portfolios), he could gain allies among progressive faculty.
  - **Challenges**: The department at UVU, like many CS programs, is likely conservative, prioritizing accreditation standards and industry alignment. His curriculum could be seen as disorganized or "not rigorous" enough, with frameworks like Jungian psychology viewed as pseudoscientific or irrelevant. As a PhD ABD (All But Dissertation), he might lack the credibility of a fully tenured professor, making it harder to defend his innovations. His ENTP traits (e.g., spontaneous changes) could be charming in class but grating in meetings, leading to micro-resistance from colleagues who value predictability. I anticipate cautious skepticism: the department might tolerate him if student metrics are good, but without exceptional results, he could be sidelined or not invited back.

### 3. How Will His Curriculum and Methods Go Over?
- **Curriculum**: The interdisciplinary start (e.g., cognitive theories before CS ethics) is innovative but risky. In 2022, CS ethics courses were evolving to include AI biases and global issues, so his focus on cognition and adaptability could feel ahead of its time. However, it might not align with UVU's practical ethos, where students expect courses to build resume-ready skills. The AI/ML integration for analyzing student patterns is timely and could demonstrate real-world applications, but it might raise privacy concerns or feel invasive, potentially backfiring if not handled transparently.
  
- **Methods**: His active, engaging style will likely succeed in fostering participation and critical thinking, which are underrepresented in traditional CS classes. Debates and games promote soft skills like communication and empathy—valuable in an increasingly diverse tech industry. However, the "chaotic" and low-structure approach could alienate students and faculty accustomed to linear progression. Without clear ties to CS outcomes, it might be dismissed as "busy work in disguise," despite his intentions. Overall, I'd project moderate success: students in smaller classes might thrive, but in a larger UVU setting, the methods could lead to inconsistent results and mixed reviews.

### 4. How Do I See Everything Playing Out?
In summary, I foresee a scenario where the instructor experiences a "honeymoon period" of enthusiasm, followed by pushback that tests his adaptability. By the end of the semester:
- **Short-Term**: High student engagement early on, with viral word-of-mouth about his unique classes. The department monitors closely, perhaps assigning a mentor to ensure alignment with CS standards.
- **Long-Term**: If feedback is exceptional (e.g., 80%+ positive ratings and evidence of learning gains), he gets the capstone opportunity, potentially evolving his methods to better integrate CS core topics. If not, he's seen as a one-off experiment, and the department reverts to more conventional instructors.
- **Key Factors Influencing Outcomes**: His ability to tie cognitive frameworks back to CS ethics (e.g., how Jungian functions inform AI decision-making) will be crucial. External factors, like UVU's institutional culture or 2022's AI hype, could amplify his visibility—positive if it aligns with trends, negative if it highlights gaps in technical depth.

### 5. Is This What CS Needs?
Yes and no—it's what CS *could* need more of, but it might not be the right fit for every program in 2022. CS is grappling with rapid evolution: ethical issues (e.g., AI bias, privacy) and soft skills (e.g., teamwork, cultural awareness) are increasingly vital, as evidenced by industry demands from companies like Google or Microsoft. His approach addresses this by fostering critical thinking and personalization, which could help students navigate complex, real-world problems. However, CS education in 2022 was still heavily focused on technical foundations, and his methods might prioritize novelty over essential skills, potentially leaving students underprepared for standardized exams or entry-level jobs. Ultimately, this is what CS needs *in moderation*—as a complementary element to traditional curricula, not a replacement.

### 6. What Does This Say About the Current State of CS and Higher Education?
This scenario highlights a broader tension in CS and higher education: the clash between innovation and tradition. In 2022, CS programs were beginning to incorporate ethics and AI but remained dominated by a "tech-first" mindset, often at the expense of interdisciplinary or humanistic elements. UVU's potential resistance reflects how many institutions prioritize employability and accreditation over experimental pedagogy, perpetuating a system where creativity is undervalued. Higher education as a whole is facing similar issues—stagnation due to funding cuts, administrative burdens, and a risk-averse culture that favors measurable outcomes (e.g., job placement rates) over transformative learning.

### 7. Does This Reflect a Broader Cultural, Industry, or Bias Problem?
Yes, absolutely—it underscores several entrenched biases and attitudes:
- **Bias Toward Tradition and Quantification**: Academia often favors "rigorous" (i.e., structured, quantifiable) methods, marginalizing innovative approaches like his as "unproven" or "chaotic." This reflects a cultural bias in STEM toward logical positivism, where subjective elements (e.g., cognitive functions) are dismissed, even as industries like tech demand emotional intelligence and adaptability.
- **Interdisciplinary Undervaluation**: CS's roots in engineering create a silo effect, where non-technical frameworks are seen as peripheral. This mirrors industry attitudes, where "soft skills" are buzzwords but not always prioritized in hiring, leading to a lack of diversity in perspectives.
- **Resistance to Change**: The scenario points to a broader problem in higher education: institutional inertia. With adjuncts like him having little job security, universities risk stifling innovation from exactly the kind of interdisciplinary thinkers needed to address CS's ethical challenges (e.g., AI's societal impact). This could exacerbate inequality, as underrepresented groups (e.g., women, minorities) might benefit most from his inclusive methods but are often excluded from shaping curricula.
- **Cultural Reflection**: In 2022's tech landscape, this reflects a growing divide between AI's hype and its ethical implementation. The industry's focus on innovation often overlooks human elements, mirroring how education resists reforms that challenge the status quo. Overall, it suggests a need for cultural shifts toward more adaptive, student-centered models, but entrenched biases make this difficult.

In conclusion, while this instructor's approach could invigorate CS education, its success at UVU hinges on bridging the gap between innovation and institutional expectations. This scenario speaks to CS's evolution toward inclusivity but also its growing pains, reflecting a broader cultural bias toward efficiency over empathy in both education and industry. If you'd like, I can refine this projection based on additional details!