Informal Learning Emerges in Everyday Human-LLM Interaction
2026-07-20 • Human-Computer Interaction
Human-Computer InteractionComputers and Society
AI summaryⓘ
The authors studied over 128,000 conversations between people and large language models (LLMs) to see if users still learn while using AI, instead of just relying on it to get answers. They found that users showed some cognitive effort in about one-third of the interactions and deeper learning in about 5%. The study also showed that AI assistants that provide helpful guidance encourage more meaningful learning, depending on how and when support is given. Overall, the authors suggest that interactions with AI can support learning, not just do the thinking for users.
Large Language ModelsCognitive OffloadingInformal LearningCognitive EngagementConstructive EngagementScaffolded SupportHuman-AI InteractionLearning ScienceTurn-level BehaviourProblem-solving
Authors
Zixin Chen, Haotian Li, Ziang Xiao, Huamin Qu, Xing Xie
Abstract
As LLMs become increasingly capable of completing tasks for users, a central concern is that everyday AI use may become primarily cognitive offloading, eroding the opportunities through which people develop their own capabilities. We analyse large-scale human--LLM conversations to ask whether informal learning behaviors also emerge in this setting: whether users engage in exchanges in ways that preserve opportunities to learn. Across 128,569 naturalistic conversations, we translated learning-science constructs into turn-level behavioural signatures. Cognitive engagement, users' cognitive effort as reflected in the exchange, appeared in 31.9% of 491,685 user turns, whereas constructive engagement, the deepest observable form of learning-oriented engagement, appeared in 4.9%, showing that deeper sense-making was recurrent but selective. Our study further identifies factors associated with these forms of engagement. Scaffolded assistant support consistently marked richer constructive participation, with associations varying by user framing, task ecology, support form, timing and prior user state. Together, these findings show that everyday human--LLM interaction is not only answer delivery or cognitive offloading; it also contains measurable, selective and conditionally organized behavioural signatures of informal learning. They shift AI evaluation from answer-delivery efficiency toward the preservation of cognitive opportunities for users to reason, test ideas and construct understanding in the course of everyday problem-solving.