Informal Learning Emerges in Everyday Human-LLM Interaction

2026-07-20

AI Summary

This study addresses the risk that everyday interactions with large language models (LLMs) may lead to cognitive offloading, thereby hindering users’ own capability development. Grounded in learning sciences theory, the authors construct turn-level behavioral indicators to identify and quantify informal learning behaviors at scale for the first time, analyzing 128,569 real human–AI dialogue turns. The findings reveal that 31.9% of dialogue turns exhibit cognitive engagement, while 4.9% demonstrate deep constructive engagement. Notably, scaffolding-oriented assistant support significantly promotes deeper learning behaviors. This work advances AI evaluation beyond mere answer efficiency toward preserving users’ cognitive opportunities, offering an empirical foundation for designing human–AI interactions that foster learning.

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.

Problem

• informal learning
• cognitive offloading
• human-LLM interaction
• constructive engagement
• everyday AI use

Innovation

• informal learning
• human-LLM interaction
• cognitive engagement
• constructive engagement
• scaffolded support