gen-aiQ6to verify
Lee et al. 2025 (Microsoft Research) — GenAI confidence inversely predicts critical thinking effort in knowledge work
In a survey of 319 knowledge workers describing 936 GenAI-assisted work tasks, higher self-reported confidence in the GenAI tool predicted less critical thinking effort, while higher self-reported self-confidence predicted more critical thinking. Qualitatively, GenAI reallocated critical effort away from direct task execution and toward verification, response integration, and stewardship of machine output.
Self-reported critical thinking effort regressed on (a) confidence in GenAI tool, (b) self-confidence — across knowledge-worker tasksHigher confidence-in-GenAI → less critical thinking; higher self-confidence → more critical thinking. Exact regression coefficients / effect sizes not extracted to verification.
- Sample
- N = 319 knowledge workers describing 936 GenAI-assisted tasks
- Methodology
- Survey + qualitative coding of free-text task descriptions; mixed-methods analysis of the confidence-vs-effort relationship.
What this means
- The strongest non-programming empirical anchor for the 'cognitive redistribution, not deskilling' synthesis: AI does not remove cognitive effort, it redirects it toward verification + integration + stewardship.
- Maps directly onto the programming-specific findings (Prather et al. — illusion of competence; Shihab et al. — brownfield shift to prompt-view-implement; Qiao et al. — performance improvement without comprehension gain).
- The confidence-direction effect (trust in tool reduces own effort; trust in self increases it) is a measurable calibration variable that any 6-24 month panel study must instrument.
Source
Microsoft Research (working paper) · Hao-Ping (Hank) Lee & and Microsoft Research / collaborator team · 2025-01 · peer-reviewed
Context
- What came before
- Pre-2025 GenAI productivity literature focused on completion-time deltas and self-reported satisfaction; explicit measurement of cognitive-effort redistribution was rare.
- What comes next
- Verify exact regression coefficients in the primary source. Extend to the AHI Part V research-frontier discussion of calibration failure modes. Pair with the programming-specific Prather / Shihab / Qiao findings.
- Where this lands
- Encyclopedia Part I §1.3 (methodology gap — cognitive redistribution); Part II (workforce — how AI changes knowledge work); Part V (research frontier — calibration failure modes).