Project: Course: Navigating Ethical Challenges of AI in Student Learning Course dates: 1–3 September 2026 (Tuesday–Thursday) · 7 sessions × 2 hours = 14 contact hours Audience: Higher-education / pre-university educators, academic staff, programme leaders (Malaysian HE) Status: 🟢 Content complete — all 7 slide decks, facilitator guide, assessments, course pack, print pack, and sources are built and ready for delivery.
The complete curriculum for TFIS's AI Ethics in Education course (workshop), delivered over three days at a Malaysian higher-education institution. The course is a single argument in seven movements: understand the AI-in-education landscape → build ethical fluency → ship an institutional AI-use guideline.
tfis-ethics-edu/
├── AGENTS.md ← this file
├── README.md ← project readme
├── course-ai-ethics-course-pack.md ← standalone master pack (monorepo root, also copied into sources/)
├── reports/
│ └── MIT-AI-Committee-Final-Report.pdf ← MIT AI in Education report (Aug 2026)
└── TFIS AI Ethics in Education/
├── PROJECT-BRIEF.md ← project brief (updated through build-out)
├── 01-syllabus/
│ └── syllabus.md ← v1.0 — the contract: LOs, commitments, session specs
├── 02-slides/
│ ├── s1.html (350 lines) ← The AI Revolution in Higher Education
│ ├── s2.html (341 lines) ← Understanding AI Ethics (case-first: Ofqual + St George's)
│ ├── s3.html (314 lines) ← Academic Integrity in the AI Era (case-first: Texas A&M)
│ ├── s4.html (207 lines) ← Workshop Part 1: Rubric builder + feedback assistant
│ ├── s5.html (250 lines) ← Workshop Part 2: Crutch effect + redesign sprint
│ ├── s6.html (241 lines) ← Institutional Guidelines Part 1: Diagnose
│ └── s7.html (229 lines) ← Institutional Guidelines Part 2: Ship
├── 03-facilitator-guide/
│ ├── facilitator-guide.md ← Delivery-day companion (run sheets, deck cues, logistics)
│ └── prompt-handouts.md ← P01–P15, print-ready per-participant handouts
├── 04-assessments/
│ ├── templates.md ← D1–D6 instrument templates + S8 summative rubric + scenario cards
│ ├── eai-cmm.md ← EAI-CMM: Ethical AI Educator Capability Maturity Model
│ └── capstone-brief.md ← One-pager: what participants ship (handed out S1)
├── 05-print-pack/
│ ├── print-pack.html (473 lines) ← A4 print-ready pack (HTML → PDF workflow)
│ └── print-pack.pdf ← Generated PDF
└── sources/
├── source-register.md ← Restricted source register (Stanford HAI primary)
├── course-ai-ethics-course-pack.md ← v1.0 — full course pack (scripts, prompts, appendices)
├── AI_Governance_Bill_Fact_Sheet.md ← NAIO fact sheets (S6–7 grounding)
├── malaysia-ai-governance-note.md ← Crosswalk: Bill ↔ course principles (S6)
└── cases/
├── ofqual-2020.md ← UK grading algorithm (opens S2)
├── st-georges-1980s.md ← Admissions bias (S2 historical anchor)
└── texas-am-commerce-2023.md ← ChatGPT false-accusation (opens S3)
| Day | Movement | Sessions | Output |
|---|---|---|---|
| Tuesday (1/9) | UNDERSTAND | S1, S2 | EAI-CMM baseline; ethics triage; case memo |
| Wednesday (2/9) | BUILD | S3, S4, S5, S6 | Verification logs; redesigned assessment; guardrailed tutor; gap analysis |
| Thursday (3/9) | SHIP | S7 (+S8) | Guideline v0.1; EAI-CMM delta; 90-day action plan |
Core claims (the seven axioms):
Two spines run throughout:
sources/course-ai-ethics-course-pack.md. Syllabus reorders S2/S3 where case-first requires it.[LOCAL] slots for MQA/MOHE/senate.Every participant leaves with:
| Convention | Rule |
|---|---|
| Slide naming | 02-slides/s{1..7}.html — one single-file HTML per session |
| Prompts | P01–P15, model-agnostic, [BRACKETS] for fill-in-values |
| Instruments | D1–D7: exit tickets, verification log, redesign sheet, case memo, guideline skeleton, action plan, EAI-CMM score sheet |
| Session structure | All specs in 01-syllabus/syllabus.md — the binding contract |
| Run sheets | 03-facilitator-guide/facilitator-guide.md — operational layer (timings, deck cues) |
| Course pack | sources/course-ai-ethics-course-pack.md — full scripts, talking points, source register, appendices |
| Brand | Cream (#FAF5EB) / Pine (#16453A) / Sun (#F2B63C) — Archivo (headings) + Hanken Grotesk (body) |
| S2/S6 decks | Obey the evening rule; physically order case slides before framework slides |
| Case dossiers | sources/cases/ — one per opening case: factual core, discussion prompts, facilitator notes |
| Print pack | 05-print-pack/print-pack.html → PDF — A4 printable, cover page, all instruments, all prompts |
| Study | Where | What it proves |
|---|---|---|
| 2026 AI Index (Stanford HAI) | S1, S3, S6 | Capability/gap widening; 88% org adoption; 4 in 5 students using AI; 6% of policies clear |
| MIT AI in Education Report (Ad Hoc Committee, Aug 2026) | S1, S2, S5, S6 | 8 principles, 30 sub-recommendations; apprenticeship-displacement risk; grading-system provocation; guideline component evidence base |
| Liang et al., Patterns 2023 | S3 (evidentiary core) | 61%+ false-positive bias vs non-native English writers; one-prompt evasion |
| Bastani et al., PNAS 2025 | S5 (the keystone) | Crutch-effect RCT: +48%/+127% assisted, −17% unassisted; guardrails engineered harm away |
| Wang & Demszky, Tutor CoPilot 2024 | S1 | Real-time AI-assisted tutoring: +4pp mastery overall, +9pp for lowest-rated tutors |
| Anthropic Education Report, Apr 2025 | S1, S2, S3 | ~50% of student AI use is direct answer-seeking; misuse patterns at scale |
| Ofqual 2020 · St George's 1980s · Texas A&M 2023 | S2, S3 opening cases | Automated judgment failures pre-/post-AI; bias laundered into consistency; detection-chaos |
malaysia-ai-governance-note.md.reports/MIT-AI-Committee-Final-Report.pdf; referenced in S1, S2, S5, S6 and cited in the source register under a new MIT section.This course was drafted with AI assistance (Claude) under human direction and verification. All sources are restricted to the register in sources/source-register.md — practicing the diligence the course preaches. The course materials carry their own AI-use disclosure statements throughout.
AGENTS.md v1.0 · 7 Jul 2026 · Owner: Khalil (TFIS)