1.1 Rationale and Vision

In 2026, AI can write code, draft reports, summarize literature, design experiments, and generate exam questions. But there is a growing global consensus that certain dimensions of education must remain human‑centric, focused on capacities that are "incomputable by AI" — deep judgment, ethical discernment, care, trust, embodied practice, and intercultural negotiation.

This course is designed precisely around those non‑replaceable dimensions of university education in science, engineering, and management. It treats AI as a powerful tool, but never as the teacher, leader, or moral agent.

The course title and design emphasize:

• Human‑centric interactive education – face‑to‑face and real‑time collaboration, argumentation, caring mentorship and leadership.
• Not replaceable by AI – intentional focus on activities and subjects where AI can support, but not substitute, human presence and responsibility.
• Interdisciplinary – students from science, engineering and management work together on complex, real‑world problems where disciplines intersect.

1.2 Instructor Profile: Prof. Willie LU

The course is taught by Prof. Willie LU, whose background is integral to its design:

• 33+ years in wireless communications, ICT and AI‑related systems (technology and engineering).
• ~20 years in intellectual property (IP) and innovation strategy/management (patents, technology transfer, corporate and government advisory).
• ~22–24 years in international relations and global business (cross‑border projects, standards work, OECD missions, global education initiatives).

This experience enables him to:

• Show realistic AI capabilities and limits in STEM and management contexts.
• Embed IP, authorship, and innovation strategy into course activities (who owns AI‑assisted work? what is "original" in an AI era?).
• Situate education within global politics and economics, including AI governance, data sovereignty and "AI colonialism".

1.3 Course Aims

The course aims to:

1. Identify and protect university‑level subjects and learning experiences that cannot be meaningfully replaced by AI in science, engineering and management.
2. Develop students' capacity to design, critique, and lead human‑centric, AI‑aware learning environments and professional practices.
3. Prepare students to be leaders and stewards of AI use in their future institutions and organizations.

1.4 Learning Outcomes

By the end of the course, students will be able to:

1. Analyze where AI genuinely adds value and where it undermines essential human learning (in labs, design studios, case discussions, ethics, negotiation, leadership).
2. Explain why certain university subjects and experiences are non‑replaceable by AI:
   • Experimental and design labs, systems integration projects.
   • Management and policy case seminars.
   • Ethics, IP/innovation, and international relations courses.
   • Negotiation, leadership, and cross‑cultural collaboration workshops.
3. Design sessions, courses and programs that:
   • Use AI as augmentation, not replacement.
   • Center human judgment, embodiment, and relational practice.
4. Formulate and justify AI‑use and non‑use policies for courses and programs, including:
   • IP and authorship guidelines.
   • Data, privacy, and equity considerations.
5. Lead informed discussions in their institutions about AI adoption, drawing on international policy, human‑centered AI frameworks and practical experience.

1.5 Audience and Prerequisites

• Audience: Advanced undergraduates and graduate students in:
  • Science (natural and social).
  • Engineering (all branches).
  • Management / business / entrepreneurship.
• Recommended background:
  • Basic familiarity with AI concepts (especially large language models).
  • Prior experience with labs, design projects or case‑based courses.
  • Introductory exposure to ethics or social implications of technology (helpful but not required).

1.6 Course Format

• Duration: 15 weeks.
• Format: Seminar + studio / workshop.
• Mode: In‑person or hybrid; synchronous interaction is essential.
• Signature feature: Every week includes at least one human‑only core activity — something that, by design, cannot be meaningfully done by AI (e.g., live ethical deliberation, embodied lab judgment, intercultural negotiation).

Throughout the course, we repeatedly revisit a set of subject types (not just topics) that are structurally not replaceable by AI, even when AI is strong:

1. Experimental and Design Labs (Science & Engineering)
   • Physics, chemistry, biology labs.
   • Electronics, embedded systems, mechanical/civil labs.
   • HCI, robotics, human‑subject experiments.
   • Human‑only core: embodied judgment, tacit knowledge, safety decisions.
2. Systems Integration and Capstone Design
   • Interdisciplinary engineering capstones (mechatronics, climate/energy systems).
   • Complex socio‑technical projects (smart cities, health systems).
   • Human‑only core: problem framing, trade‑off negotiation, owning consequences.
3. Research Seminars and Advanced Scientific Reasoning
   • Journal clubs, thesis seminars, research methods.
   • Human‑only core: evaluating novelty, interpreting ambiguity, epistemic responsibility.
4. Case‑Based Management and Policy Courses
   • MBA‑style case seminars, operations and strategy.
   • Science/technology policy, regulatory governance.
   • Human‑only core: values conflict, persuasive dialogue, situated judgment.
5. Negotiation, Leadership, and Team Dynamics
   • Negotiation skills, leadership labs, organizational behavior.
   • Human‑only core: emotion, trust, vulnerability, non‑verbal communication.
6. Professional Ethics, IP, and Innovation Strategy
   • Engineering ethics, business ethics, research ethics.
   • IP law for engineers/scientists, innovation management.
   • Human‑only core: moral agency, fairness, legitimacy, responsibility.
7. International Relations and Global Business Practice
   • IR simulations, diplomacy labs, global strategy.
   • Human‑only core: history, identity, cross‑cultural signaling, trust‑building.

These lenses are used explicitly in weekly discussions, activities and assignments.

Each week is designed to expand to rich content when fully written (concepts, cases, activities, teacher notes).

Week 1 – AI in Higher Education and the "Incomputable Human"

Goals

• Survey how AI is used in higher education as of 2026.
• Introduce the concept of "humans incomputable by AI" as an educational aim.
• Map where AI currently can and cannot replace educational work.

Content

1. Current AI Uses
   • AI tutors, grading assistants, content generators, analytics.
   • Examples from STEM and business schools.
2. Incomputable Human Capacities
   • Deep ethical judgment in ambiguous situations.
   • Embodied skill and tacit knowledge (e.g., sensing danger in a lab).
   • Trust, care, and long‑term mentoring.
   • Intercultural and political understanding that depends on lived history.
3. Subject‑Type Mapping
   • Quick mapping of where labs, capstones, case seminars, ethics/IR courses are being "AI‑augmented" vs. at risk of being hollowed out.

Activities

• Diagnostic Map: Students individually map one of their own courses:
  • Components: Information delivery, practice, feedback, collaboration, assessment.
  • What AI can already do; what is still clearly human.
• Plenary Discussion:
  • Where would you refuse AI replacement, even if technically possible?

Deliverable (Ungraded Reflection)
1–2 pages: "Where I most value human‑only education in my current studies and why."

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Week 2 – Defining Human‑Centric Interactive Education

Goals

• Make "human‑centric" concrete in the context of university subjects.
• Introduce human‑centered design (HCD) and Prof. LU's "Incomputable Human" framing.
• Identify initial non‑replaceable subject types at your institution.

Content

1. Human‑Centered Design (HCD) for Learning
   • Empathy, co‑design with learners, iterative prototyping.
   • Examples from engineering and management courses redesigned using HCD.
2. Human‑Centric AI in Education
   • Human‑centered AI principles: AI supports, humans decide and are accountable.
   • Emphasis that AI in education should enhance, not replace interpersonal and ethical dimensions of learning [1-3].
3. From Standardized Curriculum to "Incomputable Human"
   • How centrally designed, uniform curricula interact with generative AI.
   • WEST LAKE or similar models arguing that students should:
     • Offload what AI can do alone.
     • Focus education on what AI cannot become.

Activities

• Subject Audit in Triads:
  • Interdisciplinary teams (science/engineering/management) each pick:
    • One lab course.
    • One case‑based course.
    • One ethics/IR or leadership course.
  • For each, discuss: What is the human‑only core?
• Instructor mini‑lecture connecting examples to Prof. LU's IR/IP work.

Assignment 1 – Irreplaceable Subject Audit (15%)

• Choose a real university course (preferably one of the types A–H above).
• Analyze:
  • Which components could plausibly be automated by AI.
  • Which components must remain human and why (cognitive, affective, ethical, IP, intercultural).
• Propose a redesigned, human‑centric version of the course that:
  • Uses AI carefully where appropriate.
  • Explicitly strengthens and assesses the human‑only core.

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Week 3 – Productive Struggle in Advanced STEM and Analytics

Goals

• Show why struggle, error and "Aha!" moments are central in STEM and analytics courses.
• Explain how AI's default "answer‑giving" behavior can short‑circuit learning.

Content

1. Desirable Difficulties & Productive Failure
   • Evidence that moderate, scaffolded difficulty yields durable understanding.
   • Examples from mathematics, control systems, network engineering, statistics.
2. AI as Over‑Smoother
   • When AI solves complex problems instantly, students may:
     • Bypass conceptual model building.
     • Lose tolerance for ambiguity or partial understanding.
3. Human Instructor Roles
   • Calibrating challenge, timing hints, recognizing confusion and boredom.
   • Asking: "Why do you think this?"; "What else could be going on?"

Activities

• Mini‑Lesson Design Lab:
  • Students design a 20–30 minute mini‑lesson in their own discipline that:
    • Forces learners to attempt a problem without AI first.
    • Introduces AI only after students commit to reasoning.
    • Includes explicit reflection on what AI could and could not do for them.

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Week 4 – Interdisciplinary and Cross‑Cultural Collaboration

Goals

• Analyze why real interdisciplinary and international collaboration are irreducibly human.
• Link to IR/strategy courses and global capstones.

Content

1. Team Cognition and Epistemic Cultures
   • How engineers, scientists and managers weigh evidence differently.
   • Why AI can translate jargon but cannot harmonize values and risks.
2. Cross‑Cultural Dynamics
   • High vs. low context communication, power distance, face‑saving.
   • Prof. LU's experience in multinational AI and telecom projects.
3. AI's Limits in Collaboration
   • AI as facilitator for logistics and information, but not as:
     • Trust holder.
     • Political representative.
     • Moral stakeholder.

Activities

• Global Scenario Simulation:
  • Teams role‑play a cross‑border project (e.g., deploying AI‑enabled logistics in a developing country).
  • AI allowed only for factual look‑ups or translation.
  • All negotiation, compromise, and risk ownership must be human.
• Debrief: Where did you feel that AI couldn't have taken your role?

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Week 5 – Labs, Studios, and Fieldwork: Human‑Only Cores

Goals

• Highlight why experimental labs, design studios and fieldwork cannot be fully virtualized or automated.
• Connect to safety, ethics and tacit skills.

Content

1. Embodied and Tacit Knowledge
   • Recognizing a mis‑aligned component by touch or sound.
   • Smelling chemical danger, seeing nonverbal signs of distress in field sites.
2. Limits of Simulation
   • AI‑based digital twins and VR labs are powerful, but:
     • Cannot fully simulate risk, moral weight, and responsibility.
3. IP and Ownership in Hands‑On Settings
   • Students' prototypes, code, and data when AI tools assist:
     • Who owns what?
     • How is credit shared among humans vs. AI?

Activities

• Lab/Studio Redesign Workshop:
  • Students choose a lab or studio course and redesign it to:
    • Use AI for repetitive calculations, data cleaning, or scenario generation.
    • Double down on human‑only aspects in assessment (observed practice, notebook quality, live critique, safety decisions).
• Instructor commentary from Prof. LU's hardware/networking labs.

Assignment 2 – Human‑Centric Lab/Studio Session Design (15%)

• Design a 60–90 minute lab/studio/field session that:
  • Requires in‑person or synchronous presence.
  • Includes at least one safety, ethical or IP dilemma.
  • Specifies allowed/disallowed AI uses and justification.

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Week 6 – Human–AI Collaboration Models in Learning

Goals

• Provide a structured view of how AI and humans should share roles in learning processes.
• Apply this to specific subjects (labs, cases, ethics, IR).

Content

Human–AI Role Matrix

1. Human‑in‑the‑Loop vs Human‑on‑the‑Loop
   • Dangers of "rubber‑stamping" AI suggestions.
   • Examples in grading, admissions, hiring, lending, etc.
2. Subject‑Specific Examples
   • In a lab: AI suggests likely fault sources; human decides whether to stop the experiment.
   • In a case seminar: AI offers stakeholder maps; human group decides whose voices count.

Activities

• Human–AI Collaboration Charter:
  • Each student picks one non‑replaceable subject (e.g., ethics seminar, IR simulation, leadership lab) and writes a one‑page charter:
    • Which tasks AI may assist.
    • Which tasks must remain human‑only and why.
    • Ownership and authorship rules.

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Week 7 – Assessment That Resists AI Substitution

Goals

• Show why many traditional assignments (essays, static problem sets) are now vulnerable to AI.
• Develop alternatives that preserve authenticity and human effort.

Content

1. Weaknesses of Traditional Assessment in the AI Era
   • Generic prompts produce generic, AI‑generable answers.
   • Style‑based AI detectors are unreliable and inequitable.
2. Authentic and Human‑Centric Assessment
   • Oral exams and viva voce.
   • Live problem‑solving sessions.
   • Portfolio‑based assessment with process logs and reflections.
   • Localized or personal context (field visits, interviews, workplace links).
3. Examples Across Fields
   • Engineering: Live design review and code walkthrough.
   • Management: Live negotiation or board presentation on a case.
   • Ethics/IR: Public testimony simulation, cross‑examined by peers.

Activities

• Assignment Redesign Lab:
  • Students bring a vulnerable assignment and turn it into:
    • A live, interactive assessment.
    • With transparent AI‑use policy.
    • With visible process and human judgment.

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Week 8 – Learning Environments and Culture that Center Humans

Goals

• Connect physical/digital space and norms to who speaks, who learns, and how AI is used.
• Design human‑centric environments (classroom and online).

Content

1. Space and Interaction
   • Room layout, visibility, group structures.
   • Online: breakout rooms, cameras, collaborative docs, AI tool boundaries.
2. Norms and Psychological Safety
   • Ground rules for disagreement, listening, feedback.
   • Explicit agreements about AI use in synchronous sessions.
3. Human‑Led Analytics
   • Using AI dashboards carefully:
     • For support, not surveillance or ranking without context.

Midterm Essay (15%)

• 8–10 pages: "Designing a Human‑Centric Environment for an AI‑Rich Subject"
  • Choose one non‑replaceable subject type (e.g., lab, ethics seminar, IR simulation, leadership course).
  • Propose:
    • Space arrangements (physical/virtual).
    • Norms and AI policies.
    • Assessment structure.
    • How the design protects and highlights the human‑only core.

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Week 9 – Equity, Bias, and Global Power in AI‑Augmented Education

Goals

• Examine how AI can deepen or mitigate educational inequities.
• Situate human‑centric education within global inequalities.

Content

1. Bias and Disparate Impact
   • Examples of biased AI in proctoring, admissions, grading [2-3].
2. Resource Disparities
   • Who gets AI‑rich, human‑rich education vs AI‑only?
   • Risk of wealthy students having access to human mentors, others to bots.
3. Global Asymmetries
   • Data and compute controlled by a small set of countries and firms.
   • "AI colonialism" in exported educational platforms.

Activities

• Stakeholder Mapping Exercise:
  • Case: A global MOOC platform replaces in‑person teaching in rural colleges.
  • Students map who gains, who loses, and what human‑centric safeguards are necessary.

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Week 10 – Ethics, Responsibility, and Non‑Delegable Judgments

Goals

• Clarify which decisions must remain non‑delegable to AI.
• Connect to professional codes in engineering, science and management.

Content

1. Ethical Frameworks
   • Human rights, virtue ethics, deontology and consequentialism.
   • Sector‑specific codes for engineers, managers, scientists.
2. Non‑Delegable Domains
   • Final grading and promotion decisions.
   • Safety‑critical approvals (e.g., deployment of medical devices, infrastructure).
   • Disciplinary and integrity rulings.
   • Handling vulnerable disclosures (harassment, mental health, discrimination).
3. Students as Future Decision‑Makers
   • They will choose what AI does in firms, labs, and government.

Assignment 3 – Ethical Scenario Analysis (10%)

• Choose a realistic scenario in which AI is proposed to replace a human‑centric practice (education or workplace).
• Analyze:
  • Ethical risks and non‑delegable decisions.
  • IP and privacy issues.
  • Cultural and global implications.
• Propose a governance model that keeps humans in charge where it matters.

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Week 11 – Implementing Human‑Centric Education in Institutions

Goals

• Move from individual courses to programs and institutions.
• Connect to innovation strategy and management.

Content

1. Levels of Change
   • Course → Program → Department → Institution.
2. Protecting Non‑Replaceable Subjects
   • Labs with real equipment.
   • Ethics and IR seminars.
   • Leadership, negotiation and field experiences.
3. Faculty Development & Incentives
   • Training in AI literacy and interactive pedagogy.
   • Reward structures that value human‑intensive teaching.

Activities

• Internal Strategy Memo:
  • Students draft a 2–3 page memo to a dean or program director.
  • Argue for preserving and investing in 3–5 specific non‑replaceable subjects in their institution.

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Week 12 – Economics, Value, and the "Human Premium"

Goals

• Examine why human‑centric education continues to have value (and may even gain value) in an AI‑rich economy.
• Help students articulate a "human premium" argument.

Content

1. Pressures on Higher Education
   • Cost, scalability, relevance, AI‑driven alternatives.
2. What Justifies Tuition in an AI World?
   • Not mere content; instead:
     • Mentoring and networks.
     • Interdisciplinary collaboration.
     • Identity formation, ethical grounding, leadership.
3. Career Resilience
   • Why human skills (communication, collaboration, ethical reasoning, systems thinking) become more valuable as AI improves [4].

Assignment 4 – Institutional Position Paper (10%)

• 8–12 pages: "Our Human‑Centric Advantage in the AI Era"
  • Select an institution or program.
  • Identify its non‑replaceable educational experiences.
  • Show how it can reframe them as a competitive advantage.

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Weeks 13–15 – Capstone: Designing Uniquely Human Learning Experiences

Week 13 – Scoping and Proposals

Goal

• Teams design an implementable human‑centric learning module (course, program component, leadership lab, ethics/IR simulation) for science, engineering and/or management students.

Requirements

• Must explicitly integrate at least two non‑replaceable subject types (e.g., lab + ethics; IR simulation + leadership).
• Must define:
  • Human‑only cores.
  • AI‑use and non‑use policy.
  • IP and authorship rules.
  • Assessment and evaluation methods.

Deliverables

• 3–5 page proposal (ungraded but required), including:
  • Objectives and target students.
  • Subject types involved.
  • Outline of main activities.
  • Human vs AI roles.

Week 14 – Prototyping and Peer Critique

Activities

• Each team runs a 15–20 minute prototype of one critical interactive element (e.g., a lab safety debate, a stakeholder negotiation, a leadership simulation).
• Peer feedback guided by questions:
  • Where is the non‑replaceable human interaction?
  • Could AI realistically do this? If yes, what would be lost?

Week 15 – Final Presentations and Personal Frameworks

Capstone Presentation (25% combined with design dossier)

• Teams present a 20–30 minute overview of their design:
  • Rationale and non‑replaceable elements.
  • Human–AI boundary design.
  • Implementation and evaluation plan.
• Q&A focuses on feasibility, ethics, and long‑term implications.

Final Individual Reflection (5–7 pages)

• "My Personal Framework for Human‑Centric Learning and Work in an AI World"
  • Which learning experiences and professional practices you will never willingly delegate to AI in your field — and why.
  • How you will use AI responsibly as a tool.
  • How your discipline and region shape your stance.

You can expand course contents as follows:

• Each Week (15 weeks):
  • 3–5 pages: Conceptual exposition, diagrams, mini‑lectures.
  • 2–4 pages: Case studies and examples (STEM/management/IR/ethics).
  • 1–3 pages: Detailed activity instructions, variations, debrief notes.
• Assignments & Capstone:
  • 1–2 pages each: Detailed prompts, rubrics, and sample outlines (x4).
  • 10–15 pages: Capstone guidance, example designs, critique rubrics.

Total: ~15 weeks × ~8 pages ≈ 120 pages + 10–15 pages of supporting materials.

This course, under the title:

"Human‑Centric Interactive Education Not Replaceable by AI – For Interdisciplinary Students of Science, Engineering, and Management"

offers an integrated, implementable curriculum that:

• Identifies which subjects and experiences in university education are fundamentally non‑replaceable by AI, and why.
• Trains students to design and defend such experiences using human‑centered AI principles.
• Harnesses Prof. Willie LU's unique combination of technology, IP/innovation strategy, and international relations to keep the course grounded in real‑world practice and global policy.
• Prepares graduates not only to survive AI disruption but to lead in creating educational and professional environments that deliberately cultivate the "incomputable human".