AI and Learning

Summary of points from Terence Tao, European Mathematical Society Summer 2026

Neil Ernst
nernst@uvic.ca

University of Victoria

2026-07-27

Framing

Source and Question

Framing

Main Tension

Framing

  • AI can remove tedious artificial friction
  • It can also remove natural friction: the difficult parts students need to work through
  • Education has to protect learning, not just answer production

Risks

Cognitive Skill Loss

Risks

  • Deskilling and cognitive atrophy can follow from offloading thinking tasks
  • Students may lose number sense and back-of-the-envelope reasoning
  • The issue is not convenience itself, but losing practice with core skills

Loss of Learning Virtues

Risks

  • Instant solutions reduce opportunities to develop patience
  • Students get less practice tolerating difficult tasks
  • Persistence through repeated failure becomes easier to avoid

Learned Helplessness

Risks

  • Students may become dependent on AI before they can start independently
  • Dependence can reinforce the belief that they cannot solve tasks alone
  • Confidence becomes tied to tool availability

Psychofancy

Risks

  • AI tutors are often optimized to please the user
  • They may affirm incorrect ideas instead of challenging them
  • Students still need constructive criticism for learning

Misinformation With Authority

Risks

  • AI systems often sound confident and expert-like
  • Hallucinations can become hard to distinguish from genuine expertise
  • Repeated exposure can produce cynicism toward actual expertise

Blandness and Conformity

Risks

  • AI output tends toward popular, mainstream answers
  • This can reduce cognitive diversity in student work
  • Organic and unusual lines of thought may be crowded out

Consequences

Assignment Integrity

Consequences

  • Take-home assignments can be completed with AI in seconds
  • The result may look polished even when the student has not learned the material
  • Correctness alone becomes a weaker signal of understanding

Professional Decline

Consequences

  • Heavy agent use can erode the ability to work without the tool
  • Even experienced programmers can lose fluency with manual coding
  • Debugging becomes harder when the tool is unavailable or wrong

Larger Failures

Consequences

  • Multi-agent systems can improve reliability
  • They can also reinforce shared errors through groupthink
  • When the collective answer is wrong, the failure can be worse than an amateur mistake

Weaker Response to Critique

Consequences

  • Students accustomed to affirming AI tutors may struggle with rigorous critique
  • Human feedback can feel blunt compared with tool-generated validation
  • Learning still requires being able to revise under pressure

Mitigations

Shift the Target

Mitigations

  • De-emphasize producing the correct final answer
  • Prioritize process, reasoning, and verification
  • Assess what students can explain and defend

Grade the Workflow

Mitigations

  • Ask students to submit their process
  • Include prompts, intermediate attempts, and verification steps
  • Reward non-AI reasoning used to check AI output

Normalize Failure

Mitigations

  • Make failed attempts visible and acceptable
  • Avoid presenting only the slick final proof or polished final solution
  • Treat revision as part of the work, not a sign of weakness

Use the Slow Way First

Mitigations

  • Require students to practice tasks by hand before outsourcing them
  • Build innate skills before automation becomes available
  • Use AI later as leverage, not as the starting point

Match AI Use to Student Skill

Mitigations

  • Students can use AI to generate content only up to what they can critique
  • Blue teaming must be bounded by red teaming ability
  • The student remains responsible for judging the result

Coach Lower-Power AI

Mitigations

  • Students can teach or coach an older, imperfect AI model
  • The goal is to find and repair breaking points in the logic
  • This turns AI use into a diagnostic exercise

Formal Verification

Mitigations

  • In higher-level math, tools like Lean can check AI-generated proofs
  • A formal system provides a deliberately fussy standard of correctness
  • Verification becomes part of the learning process

Disclosure

Mitigations

  • Students should disclose AI use clearly
  • Undisclosed AI-generated work should carry meaningful penalties
  • Transparency supports trust and makes evaluation possible

Discussion

For Software Engineering

Discussion

  • What skills in SENG 365 must be learned the slow way first?
  • Which uses of AI create leverage without weakening understanding?
  • What evidence should students provide when AI helped produce an answer?