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The Evidence Base on AI in K-12: A 2026 Review

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Data-Driven Hints In Intelligent Tutoring Systems

Authors
Sutapa Dey Tithi,
Kimia Fazeli,
Dmitri Droujkov,
Tahreem Yasir,
Xiaoyi Tian,
Tiffany Barnes
Date
Publisher
arXiv
Link
https://arxiv.org/abs/2603.07311v1
This chapter explores the evolution of data-driven hint generation for intelligent tutoring systems (ITS). The Hint Factory and Interaction Networks have enabled the generation of next-step hints, waypoints, and strategic subgoals from historical student data. Data-driven techniques have also enabled systems to find the right time to provide hints. We explore further potential data-driven adaptations for problem solving based on behavioral problem solving data and the integration of Large Language Models (LLMs).
What is the application?
Learning – Student Support
Who is the user?
Student
Who age?
Middle School (6-8),
High School (9-12),
Post-Secondary
Why use AI?
Efficiency,
Outcomes – Other Academic,
Outcomes – Differentiation
Study design
Descriptive – Product Development