Data-Driven Hints in Intelligent Tutoring Systems
Sutapa Dey Tithi, Kimia Fazeli, Dmitri Droujkov, Tahreem Yasir, Xiaoyi Tian, Tiffany Barnes

TL;DR
This paper reviews advancements in data-driven hint generation for intelligent tutoring systems, highlighting techniques like Hint Factory, Interaction Networks, and the integration of Large Language Models to improve hint timing and relevance.
Contribution
It introduces recent developments in data-driven hint generation, including the use of behavioral data and LLMs, advancing personalized and timely support in ITS.
Findings
Data-driven methods enable next-step and strategic hints.
Systems can determine optimal hint timing.
Integration of LLMs enhances hint relevance and adaptability.
Abstract
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).
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Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning · Innovative Teaching and Learning Methods · Online Learning and Analytics
