"Help Me, But Don't Track Me": Intervention Timing and Privacy Boundaries for Process-Aware AI Tutors
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arXiv
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| Format: | Preprint |
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2026
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| author | Li, Jane Hanqi Zhang, Yuhong Liu, Jiaqi Jung, Tzyy-Ping Eguchi, Amy |
| author_facet | Li, Jane Hanqi Zhang, Yuhong Liu, Jiaqi Jung, Tzyy-Ping Eguchi, Amy |
| contents | As generative AI (GenAI) tools are increasingly used as informal tutors for mathematics learning, future systems may become more proactive and process-aware in deciding when and how to offer support. Yet such support raises an important design tension: help that is timely may also feel interruptive or overly monitoring. To inform the design of process-aware AI tutors, we surveyed 330 secondary school students in China (Grades 7--11) about their preferred tutoring behaviors, attitudes toward proactive intervention, and acceptable use of learning-process data. We found three design-relevant patterns. First, students preferred autonomy-preserving support, such as hints over direct answers. Second, they favored graduated proactive support over constant interruption, preferring small hints first and stronger assistance only as needed. Third, they drew clear privacy boundaries around learning-process data: students were comfortable with problem-solving steps and mistake patterns, but substantially less comfortable with attention- or behavior-related signals. Together, these findings offer early empirical guidance for designing AI tutors that balance timely support with learner agency, and personalization with perceived privacy boundaries in K-12 contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06178 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | "Help Me, But Don't Track Me": Intervention Timing and Privacy Boundaries for Process-Aware AI Tutors Li, Jane Hanqi Zhang, Yuhong Liu, Jiaqi Jung, Tzyy-Ping Eguchi, Amy Human-Computer Interaction As generative AI (GenAI) tools are increasingly used as informal tutors for mathematics learning, future systems may become more proactive and process-aware in deciding when and how to offer support. Yet such support raises an important design tension: help that is timely may also feel interruptive or overly monitoring. To inform the design of process-aware AI tutors, we surveyed 330 secondary school students in China (Grades 7--11) about their preferred tutoring behaviors, attitudes toward proactive intervention, and acceptable use of learning-process data. We found three design-relevant patterns. First, students preferred autonomy-preserving support, such as hints over direct answers. Second, they favored graduated proactive support over constant interruption, preferring small hints first and stronger assistance only as needed. Third, they drew clear privacy boundaries around learning-process data: students were comfortable with problem-solving steps and mistake patterns, but substantially less comfortable with attention- or behavior-related signals. Together, these findings offer early empirical guidance for designing AI tutors that balance timely support with learner agency, and personalization with perceived privacy boundaries in K-12 contexts. |
| title | "Help Me, But Don't Track Me": Intervention Timing and Privacy Boundaries for Process-Aware AI Tutors |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.06178 |