The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers
As Bainbridge [7] noted, a key irony of automation is that by mechanising routine tasks and leaving exception-handling to the human user, you deprive the user of the routine opportunities to practice their judgement and strengthen their cognitive musculature, leaving them atrophied and unprepared when the exceptions do arise.
In response, research has begun looking closely at how different activities are impacted by GenAI and the extent to which cognitive offloading [8] occurs, and whether this may be an undesirable thing. Some work has focused, for instance, on studying the effects of GenAI use on memory (e.g., [1, 106]) and on creativity (e.g., [28, 100]). Moreover, design research has also been developing interventions that improve the ability of people to think in certain ways (e.g., [24]). We review these lines of work in Section 2.
Lines of inquiry this paper opens 24
Research framings built by reading the notes related to this paper — the questions it feeds into.
How does offloading cognition to AI assistance affect learning retention?- Can metacognitive feedback reduce how much people offload thinking to AI?
- Do younger people show different cognitive offloading patterns with AI tools?
- How does cognitive offloading from AI differ from offloading to search engines?
- Can metacognitive feedback prompts reduce students' reliance on AI assistance for answers?
- Can forcing functions be redesigned to help low-motivation participants too?
- Does intervention friction in decision support reduce cognitive immersion for users?
- How does AI hallucination contribute to students losing critical thinking skills?
- Can interventions addressing AI hallucination and bias prevent cognitive offloading?
- Do students recognize when AI tutoring reduces their independent learning?
- Does the crutch effect from AI tutoring fade over time?
- Does extended reliance on AI erode the skills needed to oversee it?
- Why do decision-support systems reduce overreliance without improving team performance?
- What automation failure modes threaten generative AI applications in new fields?
- How does continuous feedback help users understand GenAI system behavior?
- What are the four ironies of automation that GenAI reproduces?
- Is fixing GenAI productivity a design problem or a model capability problem?
- What dimensions should shape how GenAI interacts with users?
- How does generative AI adoption vary across different industries?
- Why did earlier Bick, Blandin and Deming surveys underestimate genAI adoption rates?
- What design changes help keep GenAI complementary rather than competitive?