The honest summary first: the evidence is genuinely mixed, some of the strongest studies are already aging, and nobody has the long-term data. What follows is what the corpus actually supports — and where it doesn't.
The Session 2 parade of horribles, seen through the evidence, is better read as three kinds of susceptibility. The columns are not about bad students. They are about how a jagged system meets a human mind inside a social institution.
| AI jaggedness | Human susceptibility | Social susceptibility |
|---|---|---|
| Bias | Over-reliance | Existential / power concentration |
| Hallucination | Cognitive offloading | Bad-actor empowerment |
| Jagged intelligence / stupidity | Cognitive surrender | Systemic de-skilling |
| Sycophancy | Psychological attachment | Labor losses |
| Prompt-injection vulnerability | Illusion of understanding | Environmental cost |
| Sandbagging / gaming | Plagiarizing | Slop-ification |
Each links into the full synthesis. Read these as well-supported tendencies, not laws.
In an EEG essay study, 83% of ChatGPT-assisted writers couldn't quote their own just-written essay (vs. 11% otherwise); neural connectivity and sense of ownership fell with support. Effort dropped across every Bloom category in a 319-worker survey.
Self-confidence is protective; confidence in the AI is corrosive. Professionals rated AI “equally helpful” on tasks where its real benefit ranged from large to zero — they could not feel the difference.
AI reliably lifts weaker performers and can actively depress the strongest — flattening the top of the distribution. Drops of up to twenty percentile points among the best students once AI is allowed.
When AI builds scaffolding the human then transforms, a legal RCT found no atrophy and better later unaided work. When AI produces the final artifact, cognitive debt appears. Same students — different sequence.
Tutoring is one of the single largest uses of the world's most-used AI. ~90% of students used it for homework within two months of launch; a UK study slipped fully-AI work past markers with a 97% miss rate.
Reasoning models reduce effort as problems get harder, can't follow a supplied algorithm at scale, can't reliably notice what is missing, and don't revise hypotheses against disconfirming evidence. This is teachable content, not a footnote.
“Joint ability” is statistically separate from “solo ability.” The strongest predictor of getting good help from AI is theory of mind — modelling what the machine knows and how to clarify for it. It varies moment to moment, so it can be trained.
Hold Session 2's question in view: what are we trying to teach, and is the struggle still happening? Then ask whether AI obviates a struggle you believe is necessary.
The convergence across very different institutions is striking: build unaided skill first, introduce AI second, make verification of AI output the new assessed skill, drop detection enforcement in favour of disclosure and redesign, and treat “working with AI” as a separable, trainable competency.
Academic-honesty norms, at their most defensible, are about honesty — not about turning a writing process into a test of willpower. Ask what the rule is for before asking how to enforce it. And note the research question hiding inside the classroom one: what about AI use in our own research?
On detection itself, the conventional and expert wisdom runs from unreliable to impossible. There are now some more reliable products, but it is a cat-and-mouse game, and false positives are career-damaging.