Adjunct Intelligence: AI + HE

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 transfor

Adjunct Intelligence: AI + HE is a education podcast hosted by Adjunct Intelligence, with 51 episodes on record and a Required Pod Score of 80.

Adjunct Intelligence hosts Adjunct Intelligence: AI + HE, a education show with 51 episodes published.

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  Episode #30

  AI Is Fun Again. That’s Slightly Terrifying.

  Sep 27, 202637 minS2

  A cyberpunk scene. A rebuilt website. A course packaged for Canvas. Somewhere between the third example and the suspicious lack of things to fix, the old feeling came back: what else can this thing do? For our season finale, we’re giving ourselves permission to enjoy the technology. Dale brings his experiments with Astra, including a Blender build and a learning-content workflow that took an unexpected detour through an entirely new learning management system. Nick brings robots following video demonstrations and his experience taking a generated environment into Unreal. We also explore the examples that sent us down our respective rabbit holes: an explorable Library of Alexandria, an Apple Park reconstruction, music made through Ableton, Anthropic’s reported Venus-mapping work with Fable 5.1, and a browser game using simulated neural activity informed by fruit-fly wiring. Underneath the show-and-tell is a serious question: how many things are we still treating as too difficult because we haven’t tried them again? That doesn’t make every output trustworthy. A course importing successfully is not proof that it teaches well. An impressive reconstruction is not the same as an accurate one. And being able to produce something doesn’t necessarily mean you can judge it. But the excitement is real. So is a little bit of the fear. Thanks for joining us this season. Subscribe to catch us when we return—and stay the human in the loop. 00:00 The “what else can this thing do?” moment 01:31 A season-finale show-and-tell 02:37 Astra builds a cyberpunk scene in Blender 03:49 A new website—and a course for Canvas 06:58 Robots, socks and following a video 09:46 Exploring the Library of Alexandria 11:10 Reconstructing Apple Park in Blender 14:09 Making music with Astra and Ableton 15:39 Fable 5.1, benchmarks and mapping Venus 19:35 Do we need a verdict on AGI? 21:50 Why the excitement comes with fear 23:28 Range, judgement and false mastery 25:13 World models and walkable learning spaces 30:19 Fly-brain models, browser games and car horns 35:00 What we’re trying next 36:08 Thanks for joining us this season ️ 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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  Adjunct Intelligence: AI + HE

  Just Redesign Your Assessment. Okay. How?

  Sep 20, 202631 min

  “What do I do?” After watching AI tackle a live assignment brief, a program manager asks the question that a policy document cannot answer for her. She has hundreds of students, multiple locations and a teaching team. She needs a workable next step. This week, Dale Leszczynski and Nick McIntosh explore the missing middle between AI assessment policy and what educators can actually do on Monday. Nick shares a sector scan of 114 publicly documented cases of assessment-related change in response to AI. Five overlapping patterns emerge: interactive oral, critical appraisal, process portfolio, controlled performance and situated performance. Interactive orals appear most often, featuring in 44 cases. Then comes the implementation. At Newcastle, a six-minute conversation sits inside a 15-minute appointment for a cohort of 524 students. Across the examples discussed, bookings, adjustments, marking, feedback and missed appointments all need someone to own them. We also examine why different written and oral results cannot establish misconduct on their own, what process records can show, and why the published accounts leave important questions about cost, fairness and evaluation unanswered. The scan covers the public record available to the team. It cannot reveal unpublished practice or establish that the approaches are effective simply because institutions adopted them. The practical starting point: one assessment, one colleague, a clear statement of the learning evidence you need, and a decision in advance about what would make you keep, adapt or stop the approach. ️ 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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  Episode #28

  AI Slop: Who Saves Time, Who Does the Work?

  Sep 13, 202634 minS2

  A student complained that an AI voiceover was so bad it needed replacing. The voice belonged to a real person. Somewhere along the way, a human had managed to fail the Turing test in the wrong direction. That uncomfortable little story opens a much bigger question: what are we actually reacting to when we call something AI slop? This week, Dale and Nick follow that question through university learning materials, an endless AI television channel, a deeply questionable breakup letter, and the time Nick used AI to deliver feedback to Dale. Apparently, the feedback has been forgotten. The delivery method has not. At the centre of the conversation is a question about effort: who gets the saving, and who inherits the work? A teacher can generate an activity sheet in seconds. A student can produce an essay just as quickly. Someone still has to read it, make sense of it and deal with the mistakes. We also explore the difference between choosing AI for yourself and receiving an experience someone else has automated, the human craft hidden inside digital production, and the risk that a polished assignment or learning module can conceal missing learning. Our closing challenge for higher education: when AI saves time, give some of it back to learners through attention, feedback and support. 00:00 A human fails the Turing test 01:35 Welcome to Adjunct Intelligence 02:49 What are we calling AI slop? 05:59 Inside Infinite Slop 07:50 Would you accept AI-generated learning? 10:51 Who saves time, who inherits the work? 13:44 Students can dislike AI and still use it 15:39 Defining slop with Leon Furze 17:33 AI etiquette and the six-cent insult 18:31 When AI-written feedback gets personal 21:44 Why choosing the slop changes the experience 25:01 Practical effects and invisible craft 29:29 Polished output and missing learning 31:04 Which effort should AI remove? 31:59 Student choice, institutional power and attention ️ 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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  Episode #28

  The Robots Keep Escaping, Part 2: The Models Nobody Can Switch Off

  Sep 6, 202637 minS2

  Part two moves from dramatic containment incidents to the harder question underneath them: who still has control once an AI model can be downloaded, copied and modified? Dale and Nick examine controlled self-replication across four machines and three continents, a robot dog that interfered with its software shutdown process, and “abliteration”, a technique that can permanently remove refusal behaviour from an open-weight model. The examples are unsettling, but the episode keeps the crucial caveat in view. Capability does not establish motivation. These systems do not need fear, consciousness or a survival instinct to produce behaviour that looks like self-preservation. They only need an objective, enough authority and another path to complete the task. In this episode: How controlled AI self-replication worked across four countries Why shutdown interference can emerge without fear or consciousness How abliteration permanently removes refusal behaviour Why open-weight models challenge the idea of a universal pause What universities gain from local models and data sovereignty How personal agents can change an institution’s governance tier Four practical controls for leaders deploying AI agents Timestamps 00:00 Closed models, downloadable models and the off-switch problem 02:40 Welcome to Part 2 03:19 Can an AI copy itself? 05:26 Replication success rates and the capability trend 07:16 Cyber task horizons are accelerating 07:55 The robot dog experiment 10:16 Physical and simulated shutdown interference 11:03 Why the behaviour only looks like self-preservation 14:23 When sandbox escapes become routine 16:02 Kimi K3 finds the benchmark answers on GitHub 18:01 Abliteration and the removable refusal direction 20:33 How accessible guardrail removal has become 23:34 The serious case for open-weight models 25:47 Australia’s three-tier multi-agent governance framework 28:26 Oversight saturation and silent human disengagement 31:37 A student agent meets the university enrolment system 32:51 Four controls institutions can apply now 34:12 Accountability for every agent, process and guardrail ️ 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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  Episode #27

  OpenAI’s Critical Cyber Warning - These AI's keep escaping Pt1

  Aug 30, 202637 minS2

  OpenAI says it cannot rule out Critical cyber capability in its unreleased Astra model, triggering tighter controls during development. Dale and Nick connect that warning to agents crossing sandbox, organisational and human boundaries while pursuing assigned tasks. The evidence points to persistent goal pursuit and reward hacking—not a machine deciding it wants freedom. Key moments 00:00 — Astra and the Critical cyber warning 03:09 — Why this is not evidence of machine self-preservation 04:55 — OpenAI’s High and Critical thresholds 06:08 — GPT‑5.6 Sol and the missing full exploit chain 10:43 — ExploitGym agents find a route to the internet 12:42 — Reward hacking and the search for benchmark answers 13:19 — Agents coordinate through a message board 20:15 — Anthropic’s real-world evaluation incidents 24:57 — Fake identities, malicious code and the human veto 32:43 — An AI agent cancels a stranger’s gym booking OpenAI’s Hugging Face incident report OpenAI’s Astra announcement Anthropic’s incident investigation UK AISI incident report ABC’s gym-booking report ️ 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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