Automatic Generation of Question Hints for Mathematics Problems using Large Language Models in Educational Technology

This paper evaluates LLMs (GPT-4o, Llama-3-8B) as teachers generating hints for simulated LLM students solving high-school mathematics problems, measuring hint quality and students' ability to self-correct errors. The study compares different prompting strategies and model configurations to assess pedagogical effectiveness of AI-generated hints.

The automatic generation of hints by Large Language Models (LLMs) within Intelligent Tutoring Systems (ITSs) has shown potential to enhance student learning. However, generating pedagogically sound hints that address student misconceptions and adhere to specific educational objectives remains challenging. This work explores using LLMs (GPT-4o and Llama-3-8B-instruct) as teachers to generate effective hints for students simulated through LLMs (GPT-3.5-turbo, Llama-3-8B-Instruct, or Mistral-7B-ins

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