Regulating the AI Tutor: Intentions, Help-Seeking, and Self-Regulated Learning in Adolescent GenAI Use

2026-06-07

AI Summary

This study investigates how adolescents regulate their use of generative artificial intelligence (GenAI) during authentic learning tasks, particularly focusing on the real-time behavioral mechanisms underlying self-regulated learning and higher-order help-seeking. Analyzing 1,616 dialogue turns between 98 ninth-grade students and a web-based AI math tutor powered by Mistral-Large, alongside pre- and post-test scores, learning needs, and cognitive load reports, the research develops a turn-level coding framework integrating self-regulated learning and help-seeking theories, augmented by two GenAI-specific dimensions: agency toward AI and cognitive vigilance. Findings reveal that students predominantly issued instrumental requests and rarely engaged in explicit monitoring or evaluation. Moreover, post-test performance significantly declined, with higher extraneous cognitive load predicting lower learning gains after controlling for prior knowledge.

Abstract

Generative AI (GenAI) tools are now common learning companions for adolescents, yet how they regulate their use during authentic learning tasks remains poorly understood. Self-regulated learning (SRL) and high-level help-seeking (HS) are commonly proposed as safeguards against passive or shortcut-oriented use, but most empirical studies focus on aggregate learning outcomes rather than these moment-to-moment processes during AI-supported learning. This work-in-progress examines open-ended conversational data from 98 Grade-9 students across three German Gymnasium schools, who used a web-based Mistral-Large tutor to prepare a curriculum-aligned mathematics skill before an exam. Alongside chat logs (1,616 turns; 808 student turns), we collected pre-post domain knowledge, pre-chat learning needs, and self-reported cognitive load. We propose a turn-level codebook combining theory-driven SRL and HS constructs with two LLM-specific inductive codes (agency over the AI; epistemic vigilance), and report preliminary AI-coded results. Although students overwhelmingly selected scaffolded support before the chat, their interactions were dominated by instrumental requests with almost no explicit monitoring or evaluation. Post-test performance was significantly lower than pre-test, and higher extraneous cognitive load predicted lower post-test scores after controlling for prior knowledge. We discuss how these patterns can support hybrid human-AI analysis of interaction patterns and inform scaffolds for more agentic and epistemically proactive GenAI use.

Problem

• Generative AI
• Self-Regulated Learning
• Help-Seeking
• Adolescent Learning
• AI Tutor

Innovation

• self-regulated learning
• help-seeking
• generative AI
• epistemic vigilance
• agency over AI