Adjunct Intelligence: AI + HE

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]

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🎙️ 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)

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👉 Share this with a colleague who still says “I’ll figure AI out later”
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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.

Experienced developers in METR's randomised trial felt 20% faster with AI and measured 19% slower — a 39-percentage-point gap between feel and fact. Dale Leszczynski and Nick McIntosh take that perception problem into the graduate employment data: Stanford's Canaries in the Coal Mine payroll research, PwC's 2026 AI Jobs Barometer and its "seniorised" entry-level roles, DEWR's first AI and employment report, the 2025 Graduate Outcomes Survey showing underemployment rising a third straight year, and Anthropic's Economic Index putting Australia first for per-capita AI use. Then the fix: supervised unaided practice and a defended technical review — the verification skills no computing degree examines.
[00:00] — Experts misjudge AI speedup
[02:41] — Two job datasets collide
[05:24] — Job ads versus actual hires
[06:47] — Australia's first AI employment report
[09:44] — Computing graduate employment falls
[10:39] — Australia tops AI usage index
[12:40] — Graduate outcomes: the before photo
[14:43] — Frontier models ship, checking lags
[20:22] — Two fixes universities already own
[24:45] — Hosts put numbers on tape
Link promised on air: Stanford/ADP Canaries Dashboard — https://canaries.stanford.edu
🎙️ 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.

What is an AI agent, actually? This episode of Adjunct Intelligence cuts through the agentic AI hype with a guest who builds and governs these systems inside a university. Starting from Simon Willison’s definition — a large language model using tools in a loop — the conversation covers the anatomy of agents, what they unlock for learning design, and the darker side: Einstein completing entire Canvas course loads, an OpenClaw agent attacking an open-source maintainer, and the lethal trifecta that makes prompt injection an unsolved security problem. Practical, sceptical, and finishing with homework for every educator: try one agentic tool, safely.

[00:00] — Agents: hype versus reality
[04:16] — Defining agents: tools, loops
[05:35] — From chatbot to agent
[08:18] — The harness explained simply
[12:08] — Power tools for educators
[20:44] — Deskilling and evaluative judgment
[29:08] — Agents inside the LMS
[34:37] — The lethal trifecta
[40:54] — Ambition over efficiency
[43:38] — Homework: try one safely

🎙️ 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.