Micro-level AI Feedback Features and Student Responses in Consecutive LLM Tutoring Interactions

2026-07-09

AI Summary

This study investigates how micro-level feedback features in AI tutoring—such as elaboration specificity, empathetic language, and response length—affect students’ immediate comprehension and confusion states during sequential dialogues. Analyzing 1,718 naturally occurring interaction sequences from the StudyChat dataset, the authors employ chi-square tests and generalized estimating equations (GEE) to model these effects. The findings reveal that specific elaborations significantly enhance subsequent student understanding and reduce re-confusion, demonstrating a positive learning impact. In contrast, longer responses are associated with decreased comprehension, while empathetic language shows no statistically significant effect. This work is the first to uncover the differential roles of micro-feedback within authentic, continuous tutoring dialogues, challenging conventional assumptions regarding the benefits of extended replies and empathetic expressions in educational AI systems.

Abstract

AI-assisted feedback research has shown that micro-level feedback features, such as concrete elaboration, affective language, and response length, are associated with learning outcomes. Existing studies have primarily examined these features using session- or task-level measures. We examine how feedback provided in one user-AI interaction is associated with student confusion and understanding in the immediately following interaction in a naturalistic tutoring setting. We focus on three micro-level features of AI feedback: concrete elaboration (analogies, comparison-based explanations, or worked examples), affective language (encouragement, empathy, or apology), and response length. We analyzed 16,851 conversational user-AI interactions from the StudyChat dataset, a naturalistic record of student interactions with an LLM tutor in an undergraduate AI course, and identified 1,718 cases in which students expressed confusion and continued to a subsequent interaction. Using chi-square tests and Generalized Estimating Equations (GEE), we found that concrete elaboration was associated with higher understanding and lower re-confusion in the student's next interaction. Empathetic language showed no significant association with either outcome, while longer responses were independently associated with lower understanding. These findings highlight the value of examining feedback across consecutive user-AI interactions and suggest that concrete elaboration may play an important role in supporting immediate student understanding.

Problem

• micro-level feedback
• student confusion
• consecutive interactions
• AI tutoring
• learning outcomes

Innovation

• micro-level feedback
• consecutive interactions
• concrete elaboration
• LLM tutoring
• student understanding