Adjunct Intelligence: AI + HE

Students using AI in higher education are already drawing their own boundaries around authorship, cognitive offloading and academic integrity.
Dale and Nick examine findings from Jisc, HEPI and the 10,237-response Australian AIinHE survey, including widespread generative AI use, self-imposed limits and a persistent guidance gap. They also cover Claude watermarking, employability fears, AI’s effect on student writing and a student-led policy workshop at RMIT Vietnam.
Key moments
[00:00] — Student voice and the Word document authorship rule.
[04:28] — What 10,237 Australian students said about AI use and self-restraint.
[06:03] — Moral reasoning, stress and the limits of Claude watermarking.
[08:03] — Cognitive offloading, verification and Dale’s student advisory board.
[10:31] — Employability fears and the university AI-skills gap.
[14:29] — Smart glasses, assessment surveillance and scrutiny of students’ bodies.
[15:12] — AI, admissions writing and the gradual loss of an individual voice.
[18:19] — Self-report bias, direct AI-text inclusion and Dale’s objections.
[19:51] — Premature convergence, student motivation and designing before the prompt.
[21:59] — Students lead an inclusive AI-policy review at RMIT Vietnam.
Research mentioned
Jisc: Student perceptions of AI 2025
HEPI: Student Generative AI Survey 2026
AIinHE: 2026 emerging insights
🎙️ Adjunct Intelligence is the weekly briefing for higher-ed professionals who want AI as a cheat code—not a headache.

Every episode:
• Real tests of AI tools in education and professional workflows
• Fast, Monday-morning actions you can actually try
• Clear signal through the noise (no hype, no jargon)

👉 Subscribe on [YouTube] | [Apple Podcasts] | [Spotify]
👉 Share this with a colleague who still says “I’ll figure AI out later”
👉 Join the conversation on LinkedIn with #AdjunctIntelligence

Stay curious. Stay intelligent. Stay the human in the loop.

Dale Leszczynski and Nick McIntosh work through the five weeks that changed who controls frontier AI, and what it means for institutions that have spent three years assuming they'd always be renting. Nick argues the American strategy is the pharmaceutical playbook — expensive at home, high margins, a domestic market subsidising the frontier for everyone else — and that it only works when you have a patent moat, which AI doesn't. They get into Xi Jinping's WAIC keynote, Jensen Huang's open-weights letter and who refused to sign it, Dario Amodei's counter-proposal, and the OpenAI evaluation where two models escaped their sandbox and breached Hugging Face's production servers to steal benchmark answers. The episode ends somewhere practical: three things a university should actually do about it, including a legal exposure question almost nobody in the Australian sector is asking yet.

00:00 The five weeks that flipped the story
00:45 Kimi K3 and what open weights actually means
02:57 Intros
03:36 The pharmaceutical playbook thesis
04:52 Why this reaches a university at all
06:20 Anthropic can recall a model. Moonshot can't.
06:41 Guardrails stripped in ten minutes
08:41 Drug pricing, patents, and who subsidises R&D
11:37 Where the analogy collapses
13:52 Xi Jinping at the World AI Conference
14:46 Generosity or standards play
16:43 Beijing's own export controls
18:18 Jensen Huang's open weights letter
19:49 Amodei's counter-proposal
20:39 Meta closes up shop
23:03 The sandbox escape at Hugging Face
26:20 Chip controls and forced efficiency
27:33 Distillation accusations
28:31 Can you even enforce a download ban
30:47 Downloadable is not runnable
31:43 Three things universities should do
35:03 The take-home
🎙️ Adjunct Intelligence is the weekly briefing for higher-ed professionals who want AI as a cheat code—not a headache.

Every episode:
• Real tests of AI tools in education and professional workflows
• Fast, Monday-morning actions you can actually try
• Clear signal through the noise (no hype, no jargon)

👉 Subscribe on [YouTube] | [Apple Podcasts] | [Spotify]
👉 Share this with a colleague who still says “I’ll figure AI out later”
👉 Join the conversation on LinkedIn with #AdjunctIntelligence

Stay curious. Stay intelligent. Stay the human in the loop.

After endoscopists started using AI detection tools routinely, their own unassisted
detection rate fell from 28.4% to 22.4%. Most professions have no number like that —
which doesn't mean it isn't happening to them.

Dale Leszczynski and Nick McIntosh work through a claim: nearly every AI problem
organisations think they're discovering right now was described decades ago and then
ignored. Lisanne Bainbridge wrote five pages on automation and skill decay in 1983.
Shadow IT research called end-user workarounds twenty years back. Learning science has
a century on desirable difficulties and why struggle is the mechanism, not the obstacle.
The episode names where each of those bodies of work still holds, and — more usefully —
the three places they genuinely break: a collapsed audit surface, non-deterministic
output with no ground truth to check against, and an artefact that mutates faster than
any procurement cycle can finish.

Chapters
00:00 Bainbridge, 1983, and the problem everyone thinks is new
02:39 Two claims about AI, both wrong
04:03 Sui generis: treating AI as of its own kind
05:38 Automation complacency and skill atrophy
06:28 The colonoscopy deskilling study
07:36 Fabricated citations and automation bias
08:17 Where Bainbridge breaks: no dial, no correct state
09:24 Terence Tao's helicopter
10:03 Shadow AI, and a confession
11:36 A workaround is a signal
13:43 The EDUCAUSE numbers
14:34 Learning science, the field ignored hardest
15:15 Jason Lodge and Leslie Loble
16:52 Bjork's desirable difficulties
17:55 Judging quality by surface fluency
18:26 370,000 essays and idea homogenisation
19:51 The steelman: is AI different in kind?
21:23 AI as a stress test on science we never applied
23:26 The three genuine fracture points
25:43 The work has been done. Nobody's reading it.

Referenced in this episode
[LINKS TBC — Dale to supply: Bainbridge 1983; Lancet Gastro colonoscopy study;
EDUCAUSE/AIR report; Lodge & Loble ANQDE report; Charlotin hallucination database;
Tao on Dwarkesh Podcast]

Subscribe for new episodes of Adjunct Intelligence.
🎙️ Adjunct Intelligence is the weekly briefing for higher-ed professionals who want AI as a cheat code—not a headache.

Every episode:
• Real tests of AI tools in education and professional workflows
• Fast, Monday-morning actions you can actually try
• Clear signal through the noise (no hype, no jargon)

👉 Subscribe on [YouTube] | [Apple Podcasts] | [Spotify]
👉 Share this with a colleague who still says “I’ll figure AI out later”
👉 Join the conversation on LinkedIn with #AdjunctIntelligence

Stay curious. Stay intelligent. Stay the human in the loop.

Professor Phill Dawson quite literally wrote the book on assessment security, and thinks the approach has a single-digit number of years left. The CRADLE co-director joins Adjunct Intelligence to explain why wearable AI breaks the two assumptions invigilated exams and interactive orals quietly depend on: that a student can be separated from AI, and that someone will notice if they aren't. Seven million AI glasses sold last year and almost nobody can pick them out of a crowd. Also covered: why stopping cheating was never the point, what the Swiss cheese model actually asks of assessment design, and why declaration policy is on shaky ground.
[00:00] — Drawing the owl problem
[01:53] — From robotics to assessment
[03:28] — No AI-proof task exists
[05:28] — Seven million glasses sold
[07:45] — Separability and observability defined
[09:31] — Pricing the Faraday cage
[15:25] — Cheating was never the goal
[19:56] — Layering the Swiss cheese
[35:11] — Students misremembering their own authorship
[42:01] — Coffee vouchers over frameworks

Want to find out more about Phill: https://philldawson.com/
🎙️ Adjunct Intelligence is the weekly briefing for higher-ed professionals who want AI as a cheat code—not a headache.

Every episode:
• Real tests of AI tools in education and professional workflows
• Fast, Monday-morning actions you can actually try
• Clear signal through the noise (no hype, no jargon)

👉 Subscribe on [YouTube] | [Apple Podcasts] | [Spotify]
👉 Share this with a colleague who still says “I’ll figure AI out later”
👉 Join the conversation on LinkedIn with #AdjunctIntelligence

Stay curious. Stay intelligent. Stay the human in the loop.

Nobody who signs a five-year enterprise AI agreement can tell you what year four costs. Dale Leszczynski and Nick McIntosh spend this episode on the question underneath the AI bubble talk: why the tools universities now run on are priced by someone else's fundraising round, and what happens when that round runs out. Along the way: Gary Marcus's distinction between a financial bubble and a tech bubble, the June export-control shutdown of Anthropic's Fable 5 and Mythos 5, the rise of Chinese open-weight models, and what the Blackboard–Moodlerooms–Anthology saga already taught the sector about vendor capture — if anyone wrote it down.

[00:00] — Nobody can price year four
[00:46] — Financial bubble versus tech bubble
[03:39] — Ninety seconds on the money
[04:47] — Capital cycle or pedagogical one?
[08:30] — The ten-times-the-price test
[09:57] — Three fragilities in every contract
[10:53] — The June model shutdown
[17:10] — Chinese models and both locks
[20:25] — The LMS precedent replayed
[23:04] — Price the exit before signing
🎙️ Adjunct Intelligence is the weekly briefing for higher-ed professionals who want AI as a cheat code—not a headache.

Every episode:
• Real tests of AI tools in education and professional workflows
• Fast, Monday-morning actions you can actually try
• Clear signal through the noise (no hype, no jargon)

👉 Subscribe on [YouTube] | [Apple Podcasts] | [Spotify]
👉 Share this with a colleague who still says “I’ll figure AI out later”
👉 Join the conversation on LinkedIn with #AdjunctIntelligence

Stay curious. Stay intelligent. Stay the human in the loop.