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arXiv · 2610.26411

Exploring Visual Access and Proactive AI Tutoring for Handwritten Mathematics

Nachiket Kotalwar·Conrad Borchers·Shubham Gandhi·Partha Sarathi Purkayastha·David Yaron·Carolyn P. Rosé
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Abstract

Handwritten work captures mathematical reasoning that learners may not express in typed or voice-dictated messages. We built a multimodal AI tutoring platform in which a phone camera gives the tutor a live view of learners' work on paper. In a randomized online study, 117 adults solved calculus problems with Text-Reactive, Visual-Reactive, or Visual-Proactive assistance. Analysis of 2,140 tutor messages showed that visual access shifted assistance toward checking ongoing work, while proactivity enhanced error identification and directive hints. Learners valued feedback grounded in their writing; those who received proactive help rated it as supportive, and about a third wanted it more often. These findings inform when tutors should intervene and what they should address. We release the platform, prompts, and instruments, together with 255,984 timestamped images of handwritten work linked to tutoring conversations, intervention decisions, and learning outcomes, to support multimodal tutoring systems that preserve the affordances of handwritten work while providing adaptive feedback.

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Nachiket Kotalwar, Conrad Borchers, Shubham Gandhi, Partha Sarathi Purkayastha, David Yaron, Carolyn P. Rosé. 2026-10-19. Exploring Visual Access and Proactive AI Tutoring for Handwritten Mathematics. https://arxiv.org/abs/2610.26411

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