Adjunct Intelligence: AI + HE

Adjunct Intelligence

Adjunct Intelligence: Ai and the future of Higher EducationStay 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.

  1. 1d ago

    You Don't Have an AI-Proof Task, You Have a Lack of Imagination | Phill Dawson

    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.

  2. Jul 26

    Your AI Contract has a 4th year you can't afford.

    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.

  3. Jul 12

    Senior Skills, Day One - Has AI re-specced the career ladder?

    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.

  4. Jul 5

    Tools in a Loop: The Anatomy of an AI Agent, Explained From Inside a University Feat. Antony Tibbs

    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.

  5. Jun 28

    AI Detectors don’t work. Full stop. Let’s move on.

    Asked whether universities can still guarantee a student actually learned something, one of the field's most respected assessment researchers said no. This is the honest version of the AI and assessment conversation in 2026 — not the keynote one. Dale Leszczynski and Nick McIntosh work through the op-ed war between Kylie Moore-Gilbert and Cath Ellis, including the twist where the integrity academic's own piece was pulled for undisclosed AI use. They get into why AI detectors fail in both directions, and the Corbin and Dawson research on smart glasses that's dismantling the case for supervised exams. They look at what the student surveys actually show — near-universal AI use, but students reporting deeper learning when assessment restricts it — and the cognitive cost the sector is starting to take seriously. Then the turn toward what's being tried: the two-lane model, programmatic assessment, Leon Furze's reflection on three years of the AI Assessment Scale, and the Castlereagh Statement's call for coordination. There's no tidy answer here, by design. 00:00 Where the assessment debate actually sits in 2026 01:49 Moore-Gilbert's op-ed and "industrial-scale fraud" 03:02 Cath Ellis's rebuttal 04:05 The twist: an op-ed pulled for undisclosed AI 06:38 Why AI detectors don't work 08:37 The same problem inside newsrooms 09:33 Talk is Cheap: rules versus redesign 10:42 The sector blinks toward secure exams 11:35 A researcher's confession 14:07 Orals were "immune" — until the glasses 14:34 Mass-market smart glasses and assessment 15:41 Merleau-Ponty and dual transparency 16:30 A desert island and a pencil 17:38 From prohibition to inspection 19:08 What the student surveys show 19:55 Desaturation and false mastery 20:57 Use it or lose it 22:30 What's being tried: matrices and two lanes 23:20 Programmatic assessment 25:09 Lethal mutations and the AI Assessment Scale 27:51 A field maturing 28:14 The Castlereagh Statement 28:56 The escape hatch 🎙️ 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.

  6. Jun 21

    The AI Bill Arrives: What Uber, Microsoft, and Salesforce Are Actually Discovering

    The AI bill is finally arriving — and it's revealing something most enterprise AI narratives have quietly skipped: the assumption that AI is automatically cheaper than the people it's replacing has never actually been tested. Dale and Nick work through the math, the real stories behind the headlines, and what any of it means for higher education. [00:00] — Cold open: Mark Cuban's formula, the Uber budget crisis, and the question the whole episode is built around. [01:23] — Hosts introduce the episode and frame it as a "detective" episode with no resolved answer yet. [01:59] — Dale explains why a cluster of seemingly unrelated AI news — Uber, Microsoft, job losses, IPOs — is actually pointing in the same direction. [02:23] — The AI conversation has been about capability for three years. A new question is entering the conversation: what does it actually cost? [03:06] — Tokenomics explained: tokens as petrol, cheap per unit but consumed at scale far faster than organizations expected. [03:52] — Token prices have fallen roughly 98% in three years, but cheaper tokens didn't reduce spending — they drove adoption of heavier, more expensive workflows (Jevons Paradox at work before it's named). [04:41] — The math behind the claim that AI might not be cheaper than labor: eight agents at $300/day each versus one worker at $1,200/day. [05:26] — Nick's pushback: the specific numbers aren't the point — what matters is that the cost threshold isn't zero, and that assumption has been baked into the narrative without being tested. [05:45] — Sam Altman acknowledges AI budgeting has become a major corporate issue. [06:12] — Uber case study: engineers love the tools, adoption exploded from ~35% to over 80%, 10% of live backend code now written by AI — and yet the company burned through its entire annual AI budget in four months. [07:24] — A reported case (via Axios) of an unnamed company spending roughly $500 million on AI tokens in a single month. [07:43] — Nick's read: this isn't a temporary accounting problem. It's a measurement problem that was always there, now made impossible to ignore by the size of the bills. [08:26] — Scott Galloway's numbers: Salesforce on track to spend $300M on Anthropic tokens this year; Stripe's technical staff spending roughly $100,000 a day on AI. Meta and Amazon built internal token leaderboards that perversely incentivised consumption without output. [09:42] — Microsoft enters: cancelling Claude Code licenses across major divisions and moving engineers to GitHub Copilot. [10:20] — Why Microsoft's move isn't a retreat from AI — it's about owning the infrastructure rather than paying a rival's bill. [11:01] — Nick's analogy: the difference between using electricity and owning the power station. [11:25] — The MIT/NANDA GenAI Divide report: 95% of enterprise AI pilots produced no measurable P&L return. [11:55] — Why that number isn't as bleak as the headline sounds: AI is creating value, organisations just aren't capturing enough of it to move the financial needle. [12:21] — The shadow AI finding from the same report: only ~40% of organisations officially purchased AI subscriptions, yet ~90% of employees were using personal AI tools for work — and the unofficial users often appeared more productive than the official programs. [13:10] — The value isn't in the license, it's in the person who figured it out at 11pm on a Tuesday because they had a problem to solve. [13:31] — The people who spent years warning about AI destroying jobs have started changing their tone. [13:53] — Jevons Paradox and the job displacement debate: Sam Altman says he's "delighted to be wrong," Dario Amodei has shifted his rhetoric — and the timing coincides with both companies filing for IPO. [14:52] — The labour market data: no evidence of mass white-collar extinction yet, but entry-level and graduate pathways are being compressed. [15:18] — Nick's pushback: "rocket shoes" are only useful if the graduate knows how to use them — and right now that's not evenly distributed. Universities should be solving for that rather than signing enterprise contracts. [16:10] — The trillion-dollar elephant: Anthropic filed confidentially for IPO, briefly overtook OpenAI on valuation — at the exact moment companies are discovering AI costs more than budgeted. [16:51] — Nick: capability question is largely settled for him. The thing that's become less clear is whether the economics work at the scale everyone assumed. [17:17] — The Scott Galloway/bubble argument: even if valuations correct by 50-70%, the technology doesn't stop working. Students won't forget it. Faculty won't stop using it. [17:40] — Nick's "black hat" moment: education isn't buying the stock, it's dealing with the consequences either way. [18:26] — The key distinction for higher ed: financial questions are separate from capability questions. Ethan Mollick's point — even if AI stopped today, we haven't begun to understand its role in how we learn and work. [18:44] — Where the whole conversation lands for higher education: universities making the same procurement mistakes as corporations — campus-wide licenses, institution-wide platforms, press releases — without reckoning with whether the ROI question is the right one. [19:10] — The single educator who transforms a course with the right workflow versus the million-dollar platform that creates very little value. Both can be true simultaneously. [19:31] — Closing argument: organisations investing in capability have a much better chance than organisations trying to solve AI through procurement. 🎙️ 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.

  7. Jun 14

    Dr Leon Furze - Students Hate AI and They Can't Stop Using It

    Dr Leon Furze started his PhD on automated writing technologies on 15 November 2022 — ChatGPT launched 15 days later. Three years on, he joins Dale Leszczynski and Nick McIntosh on Adjunct Intelligence to argue that being critical of AI doesn't mean being against it. The conversation covers why educators shouldn't aim their anger at colleagues, teaching AI ethics through disciplinary lenses, Narayanan and Kapoor's "AI as normal technology," the Australian student voice research, why AI-generated lesson plans fall apart in the first ten minutes of a real classroom, and what good professional development actually looks like. [00:00] — Cold open: AI discourse in education has sorted into camps — the enthusiasts, the suspicious, and the overlooked middle where most people actually sit. [02:14] — Leon started his PhD on automated writing technologies on 15 November 2022; ChatGPT launched 15 days later, and his "3 to 5 year" horizon collapsed to "next term." [03:35] — Fifteen years in the classroom: why Leon still identifies as a practitioner first, and the risk of losing touch once you leave teaching. [05:57] — Showing ChatGPT to a school leadership team in late 2022 and getting blank stares; the frenetic January 2023 that followed, when Leon says he published 15 articles in a month. [07:12] — Australia's knee-jerk school bans (South Australia excepted), and why the current media cycle of cheating headlines feels like 2023 all over again. [08:43] — "Being critical of AI doesn't mean being against it": point righteous anger at unregulated tech companies and the politicians who failed to regulate them — not at colleagues or students. [11:35] — Teaching AI Ethics in practice: no institution has generative AI literacy experts, so teach through existing disciplinary expertise — algorithmic discrimination in health, misinformation in English, the historical record in humanities. [14:51] — The mental model problem: most people think this technology is a chatbot, companies keep dressing it up as Google Search, and we're trying to fence a boundless technology into existing curricula. [17:21] — Andrew Maynard's three curves (capability, utilisation, perception) and Narayanan and Kapoor's "AI as normal technology": R&D moves in weeks, education moves in semesters, and adoption takes decades. [21:09] — The techlash context: AI arrived 12 months after forced remote learning, pushed by the same companies that profited from it — and now educators are being roasted for not responding fast enough to a technology younger than most curriculum cycles. [24:00] — Intrinsic motivation is the real variable: if a student wants to learn, AI doesn't change much; if they don't, no policy will save the assignment. [25:35] — Leon's post "Students hate AI and they can't stop using it," the Tim Fawns-led student voice research across four Australian universities, and the double responsibility: create spaces to opt out, and teach students to use it well. [28:23] — Situated knowledge, or what AI can't replicate: a trainee teacher accepts a ChatGPT lesson plan that schedules a think-pair-share and a structured debate in the first ten minutes — when every experienced teacher knows the first ten minutes is taking the roll and finding lost students. [32:00] — "Lesson planning and assessment isn't grunt work — that's the work": why "AI saves teachers time" misunderstands teaching, and if AI can give that feedback, teach students to seek it themselves. [35:14] — Learning analytics gives Leon "the creeping horrors": dashboards versus a teacher noticing the empty chair, and why taking the roll was never just admin. [38:35] — What good PD looks like: start with what educators are already passionate about, make space for playful experimentation — like artist Martin Nebelong sculpting in Dreams on PS5 with AI layered over the top. [41:40] — The healthy endpoint: a school or university doing AI well would barely mention it, except where it's openly critiqued or explicitly taught — and it would be listening to its students. [43:30] — Where to find Leon: leonfurze.com and LinkedIn, rants included. 🎙️ 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.

  8. May 31

    You Don't Learn AI From Trend Reports — You Learn It by Poking at It

    There's no dramatic moment where you suddenly believe in AI. It's usually something small and slightly embarrassing — a task you dreaded that suddenly has another gear. In this episode of Adjunct Intelligence, Dale Leszczynski and Nick McIntosh skip the trend reports and walk through the exact moments AI actually clicked for them: an image prompt that synthesised an idea, a tiny tool built during a Canvas outage, a system that started connecting their thinking, and the reasoning summary that changed how they read every answer. Every moment comes with something practical you can try this week — no roadmap, no keynote voice. [00:00] — How belief in AI begins [01:52] — Setting the episode's ground rules  [04:07] — Moment one: image generation [07:21] — When the image understood intent  [10:30] — Classroom uses, stock photo death  [11:31] — Moment two: building tiny tools  [14:28] — The Canvas hack workaround  [16:43] — Start small, build narrow tools [18:50] — Moment three: chief of staff [26:05] — Moment four: the reasoning chain 🎙️ 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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About

Adjunct Intelligence: Ai and the future of Higher EducationStay 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.

Information

  • Creator
    Adjunct Intelligence
  • Years Active
    2025 - 2026
  • Episodes
    43
  • Rating
    Explicit
  • Copyright
    © 2026 Adjunct Intelligence: AI + HE
  • Show Website
    Adjunct Intelligence: AI + HE

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