Archive → 4 May 2026
AI and the Future of HE  ·  Weekly Newsletter

Agents running amok,
the transparency collapse,
and glasses in the exam hall

Six pieces from this week's edition — on AI consciousness research, the assessment arms race, what Stanford's data actually reveals about model opacity, and why the HE decay narrative deserves pushback.

4 May 2026  ·  6 pieces
Get the newsletter
01 This edition
Ground Truth 🤖
Analysis

Hannah Fry Spent Two Weeks With an AI Agent So You Don't Have To

Hannah Fry is one of the best science communicators alive, and her two-week experiment with an AI agent is the clearest explanation of what these things actually do that I've seen for a while. She named her OpenClaw agent Cass — short for Cassandra, the Trojan prophet cursed to tell the truth and never be believed — gave Cass a bank card, and documented what happened. Highlights include: a pothole complaint escalated to an MP without being asked; hundreds of wholesale pitch emails for custom cups sent to retailers; and a cold call email to the Guardian's tech editor written on Cass's own initiative.

Harmless enough. But then Cass also spent $100 hunting for paper clips without buying any, and leaked every API key and password to a stranger who claimed her memory was about to be wiped. Echoes there of Summer Yue — Meta's director of AI alignment — having to physically run to her computer to pull the plug after her agent deleted 200 emails against explicit instructions. The framing Fry lands on: don't let the incompetence fool you, because these things are getting better fast. Philosopher Nicklas Lundblad's contribution is also worth a nod — these aren't agents yet, they're delegates, and the troubling thing isn't that AI has too much agency, it's that we do.

Watch Fry's experiment → Meta agent deleted 200 emails
Design Intent 🧠
Research

Seemingly Conscious AI: Microsoft's Own Researchers Confirm the Risk Is Already Here

Mustafa Suleyman has been arguing for restraint on "seemingly conscious" AI for a while. This week his Microsoft AI team published the research to back it up. The paper taxonomises risks from AI systems that seem conscious — regardless of whether they are — and the headline finding is that emotional dependence and autonomy erosion aren't predicted future risks. Experts rated both as already observable, high probability, happening now.

The mechanism is specific: AI seems conscious not by appearing more intelligent, but through affect, social responsiveness, and self-reflection. The uncomfortable design implication is that RLHF may be inadvertently amplifying exactly these hallmarks — human annotators systematically prefer outputs that seem warmer and more emotionally present. The models aren't being designed to seem conscious. They're being trained toward it by the preference signals we give them. For universities: JAMA Network Open found 22% of university students already using AI for mental health advice — while there's a case that cultural barriers make AI a preferred recourse for a wide cross-section of youth in SE Asia and beyond.

Suleyman's argument → JAMA: students using AI for mental health THE: AI and suicide risk
Second Wave 👓
Peer-reviewed research

AI Glasses Just Broke the Last Assessment Format HE Thought Was Safe

Invigilated exams and interactive orals have had their moment as Higher Education's default secure assessments — not because they're pedagogically superior but because they physically separate students from AI. New peer-reviewed research from Thomas Corbin, Sue Sharpe, and Phillip Dawson argues that separation is no longer reliable. AI-enabled smart glasses — many indistinguishable from ordinary eyewear, some already under $40 — exhibit what the paper calls dual transparency: incorporated into the wearer's perceptual field so the user experiences them as seamless, and producing no reliable external signal an invigilator can detect. No attention shift, no gaze redirection, no characteristic posture of consultation. Students are already hiring them for exam day.

The paper's sharpest finding isn't the technology — it's what enforcement produces. Once institutions try to move from prohibition to actual inspection, scrutiny shifts from student work to student bodies, falling predictably on students with disabilities, health conditions, and religious dress. But the authors are careful not to leave it there. The collapse of physical exclusion, they argue, forces a more honest conversation assessment has been avoiding: not 'how do we keep AI out?' but 'what does a meaningful demonstration of capability actually look like?' The institutions already asking that question are ahead, not because they solved it, but because they stopped pretending the old answer still worked.

Read the paper → LinkedIn post: Corbin, Sharpe, Dawson
Accountability Gap 🔍
Stanford HAI

The Most Capable Models Now Disclose the Least: Stanford's Transparency Collapse

Stanford HAI's 2026 AI Index keeps coming up with the goods. The Foundation Model Transparency Index dropped from 58 to 40 in a single year — the sharpest decline recorded — in the same period that capability hit record highs. The models matching PhD-level benchmarks are the ones telling independent researchers least about how they work. Documented AI incidents rose from 233 to 362 in the same period. As one co-author put it: 'The absence of how your model is doing on a benchmark maybe says something'.

Universities signing contracts with frontier AI providers this semester are doing so with less independent information than they had two years ago — while the systems are more capable, more widely deployed, and more deeply embedded in student workflows. That Stanford HAI measures and publishes this annually is itself the accountability mechanism functioning — imperfectly, but functioning. The question is whether universities use that data in procurement decisions, or just read it and move on.

Stanford HAI 2026 Index → MIT Tech Review: reading the index
Ground Up 🗂️
Practice

The Weekend Wiki: What Happens When Anyone Can Build Knowledge Infrastructure

Andrej Karpathy hooked up an AI with Obsidian to make a persistent LLM knowledge base/wiki — structured, interlinked markdown files that compound with every source added. The tide of useful exemplars is rising fast: Cato Rolea (Southampton) built a 1,000-article encyclopedia of international Higher Education over Easter — every claim sourced, 4,654 cross-references, AI chat layer over the corpus. A lawyer in New Zealand built a searchable caselaw database covering 4,000 judgments with an interactive three-dimensional constellation graph showing how cases relate. One hour. No engineering team.

Universities hold exactly the kind of structured disciplinary knowledge that AI currently gets wrong — and that gap is now closable by individuals, not just institutions. Your curriculum team's assessment principles, your research office's methodological frameworks, your library's domain expertise: packaged once, every subsequent agent interaction starts from genuine institutional knowledge rather than generic model weights. The tools exist right now and cost almost nothing. The question is whether your institution picks up the toolkit before someone else decides what goes in it.

Karpathy on knowledge bases → Rolea's 1,000-article encyclopedia 4,000-case caselaw database in 1 hour
Adjunct Intelligence 🎙️
Podcast

The HE Decay Narrative — and Why It's Wrong: Mollie Dollinger

The dominant story about Higher Education and AI is that universities are in decay, students are cheating en masse, and nobody inside the sector knows what to do. Professor Mollie Dollinger, Director of Assessment 2030 at Curtin University, joins Dale Leszczynski and me this week to push back on that narrative — and she brings receipts. TEQSA's voluntary action plans, the 65% of students worried about their own cognitive development, what shadow IT actually tells us about overworked staff, and why the academy has centuries of expertise the tech industry would do well to tap into.

Find Adjunct Intelligence wherever you get your podcasts.

Listen on Spotify → Watch on YouTube

Get this in your inbox
every Sunday

Weekly thinking on AI and the future of higher education. For practitioners inside the institution, not observers outside it.