INSIDE the Student's Mind: Jointly Modeling Latent Reasoning and Action in LLM Student Simulators

The research introduces INTERNAL STUDENT DIALOGUE (INSIDE), a framework designed to enhance Large Language Model (LLM) student simulators. Unlike previous models that only replicate observable actions, INSIDE fine-tunes LLMs to simulate students' internal reasoning processes, aligning with Bloom's Taxonomy across cognitive, affective, and action dimensions. This advancement is particularly relevant for educational applications, such as evaluating tutoring systems, where understanding the 'why' behind student actions is critical.

Why it matters

This development addresses a critical limitation in AI-driven simulation, enabling more accurate and nuanced models of human behavior, particularly in educational contexts. For leadership, this means more reliable testing and development of educational tools and strategies, moving beyond superficial behavioral replication to understanding motivational and cognitive underpinnings.

Key insights

• Existing LLM-based simulators often reproduce observable actions but lack the capability to capture underlying reasoning.
• The gap in understanding underlying reasoning is significant in educational applications, especially for evaluating tutoring systems.
• INSIDE is a student modeling framework that fine-tunes LLMs to simulate both student actions and their internal thought processes.
• The framework generates internal dialogue grounded in Bloom's Taxonomy, covering cognitive, affective, and action dimensions.
• Models are fine-tuned using paired 'think traces' and actions to achieve deeper simulation fidelity.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.10492