Adjunct Intelligence: Ai and the future of Higher Education Stay ahead of the AI revolution transforming education with hosts Dale, tech enthusiast and AI Nerd, and Nick McIntosh, Learning Futurist. This weekly espresso shot delivers essential AI insights for educators, administrators, and learning professionals navigating the rapidly evolving landscape of higher education. Each episode brings you a concise rundown of breaking AI developments impacting education, followed by deep dives into cutting-edge research, emerging tools, and practical applications that Dale and Nick are implementing in their own work. From classroom innovations to institutional strategy, discover how AI is reshaping teaching, learning, and educational operations. Whether you're working in the classroom, on the the classroom a university lecturer, TAFE teacher, or simply passionate about the future of learning, "Adjunct Intelligence" equips you with the knowledge to transform disruption into opportunity. Business casual, occasionally humorous, but always informative.
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Episode #23
Lisanne Bainbridge Called This in 1983 - we have the rules already
Aug 9, 202627 minS2
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.
You Don't Have an AI-Proof Task, You Have a Lack of Imagination | Phill Dawson
Aug 2, 202647 minS22
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.
Senior Skills, Day One - Has AI re-specced the career ladder?
Jul 12, 202629 min
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.
Tools in a Loop: The Anatomy of an AI Agent, Explained From Inside a University Feat. Antony Tibbs
Jul 5, 202646 minS2
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.
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