Sunny Yu, Myra Cheng, Ahmad Jabbar, Ilia Sucholutsky, Katherine M. Collins, Dan Jurafsky, Robert D. Hawkins (2026) — Stanford University / Princeton University. Proceedings of the 48th Annual Meeting of the Cognitive Science Society (CogSci 2026).
This preregistered large-scale study (N = 1,237) investigates whether people are well-calibrated in estimating the time savings from AI assistance on simple cognitive tasks. The key finding is a speedup illusion: participants accurately predict how long they'll take independently but significantly underestimate how long they'll take with AI assistance — despite actual completion times being equivalent between independent and AI-assisted conditions. Notably, this bias is AI-specific; the same miscalibration does not appear when participants imagine help from another human. A critical dissociation emerges between time and effort: participants report lower subjective effort with AI even when completion times are identical, suggesting subjective experience drives AI adoption more than objective efficiency.
The findings complement the companion paper on the efficiency-gain-illusion-ai-overreliance (same authors, arXiv:2605.22687) which found people underestimate their rate of AI usage and overestimate benefits on simple tasks. Together, these studies reveal a dual miscalibration that could entrench inefficient over-reliance patterns: users choose AI believing it saves time and effort, when for simple tasks it may do neither. This has direct implications for educational settings — students using AI for simple cognitive work (arithmetic, basic writing, spell-checking) may cognitively offload without actual efficiency gains, reducing learning transfer through diminished deliberate practice. Presented at CogSci 2026, the work bridges cognitive science and metacognition research with practical design implications for AI tools in learning environments.
Related Pages
- ai-assistance-reduces-persistence: Causal evidence (N=1,222) that brief AI assistance reduces persistence and impairs unassisted performance — rapid emergence of over-reliance effects
- cognitive-offloading — Companion paper examining AI offloading patterns
- over-reliance — Demonstrates misaligned AI efficiency expectations
- efficiency-gain-illusion-ai-overreliance — Paired paper on efficiency miscalibration in simple tasks
- metacognition — Users fail to accurately predict AI-assisted vs independent completion times
- genai-performance-vs-learning — Related illusion: perceived vs actual cognitive benefit from AI
- ai-learning-transfer — Speedup illusion may reduce deliberate practice and transfer
- student-cheat-sheets-make-or-take — Students choose between self-created and instructor-provided cheat sheets based on trust, personaliz
Citation
APA: Sunny Yu, Myra Cheng, Ahmad Jabbar, Ilia Sucholutsky, Katherine M. Collins, Dan Jurafsky, Robert D. Hawkins (2026). Cognitive offloading and the speedup illusion in human-AI interaction. arXiv:2605.23177. Proceedings of the 48th Annual Meeting of the Cognitive Science Society (CogSci 2026).
- digital-literacy-illusion — Digital literacy illusion confirms AI overestimation in secondary students
- llm-reasoning-traces-metacognition — Processing fluency account of trace-induced overconfidence
- ai-productivity-moderation — Learning curve factor explains when AI productivity gains fail