CURIOBOT

CURIOBOT framework uses LLM tutoring dialogues to operationalize curiosity-driven learning interventions

Researchers introduce CURIOBOT, a conversational tutoring framework that implements Berlyne's collative variables (novelty, complexity, conflict, uncertainty) as adaptive linguistic interventions via LLMs. Across 270 tutoring conversations, curiosity-oriented prompting strategies produced up to 2.4x more exploratory conversational turns under fixed time budgets. The study also introduces a learner-centered evaluation framework measuring exploratory questioning, conversational agency, and productive struggle. Results suggest curiosity functions as a partially independent interaction-level mechanism, and that LLM-mediated dialogue can serve as a scalable experimental platform for studying language's effect on cognition.