This paper explores how to design AI tutors that facilitate mathematical reasoning for junior-high students preparing for high-stakes exams, finding that students repurpose "answer-first" shortcuts and benefit from specific UI features.

In plain terms

Large Language Models (LLMs) are powerful but risk making students just get answers instead of truly understanding math, especially when preparing for important exams. Researchers developed AITutor, an interactive system designed to translate theoretical teaching methods into practical user interface features. They tested it with 12 junior-high students preparing for high-stakes exams (Zhongkao) using various methods, including a generative study, usability study, and field deployment. The study revealed that under time pressure, students resisted traditional Socratic dialogue (guided questioning) and instead used "answer-first" shortcuts as crucial checkpoints to diagnose their mistakes. They demonstrated that features like layered worked examples, step-linked visual grounding (connecting steps to visuals), and metacognitive scaffolding (prompts to reflect on thinking) effectively reduced the effort needed for students to repair their reasoning. The paper introduces a "Reasoning-Centered Product Loop," offering practical implications for designing AI that structurally supports the inspection, local repair, verification, and later recall of mathematical reasoning in real-world learning.