"Help Me, But Don't Watch Me": Intervention Timing and Privacy Boundaries for Process-Aware AI Tutors

2026-02-03

AI Summary

This study addresses a central challenge in the design of AI tutoring systems: how to provide timely learning support while respecting students’ autonomy and privacy boundaries. Through a survey of 330 Chinese middle school students, it offers the first empirical insights into K–12 learners’ preferences regarding intervention timing and data usage in process-aware AI tutoring. The findings reveal that students favor gradual, hint-based interventions over direct answers and exhibit high acceptance of using problem-solving step data, yet express sensitivity toward monitoring of attention and behavioral metrics. Building on these results, the study proposes design principles that balance timely assistance, learner autonomy, and privacy protection, thereby offering critical guidance for developing interaction mechanisms in generative AI–based mathematics tutoring systems.

Abstract

The use of generative AI (genAI) tools as informal tutors is becoming increasingly prevalent among secondary school students in mathematics learning. In many schools, individualized instructional support is limited, and one-on-one human tutoring remains costly in most learning contexts. GenAI has the potential to provide timely, on-demand help to students when teachers or tutors are not available. However, there are still few studies that examine students'preferences for AI tutor support that enhances autonomous learning. We investigated learner expectations for AI tutoring through a survey with secondary school students in China (Grades 7-11; N=330). Students generally preferred support that preserves learner autonomy (e.g., time to think, hints over direct answers), expressed mixed or cautious preferences between human and AI tutors, and held nuanced views of proactive intervention, valuing adaptivity but also worrying about annoyance and autonomy. Privacy boundaries were uneven: many accepted sharing problem steps and error patterns, while willingness dropped for more sensitive signals such as attention or behavior. Our findings offer learner-centered insights for designing AI tutors that balance timely intervention with student agency, and personalization with perceived boundaries in a K-12 context.

Problem

• process-aware AI tutors
• intervention timing
• privacy boundaries
• learner agency
• proactive support

Innovation

• process-aware AI tutoring
• proactive intervention
• privacy boundaries
• learner agency
• generative AI in education