Leveraging generative artificial intelligence to simulate student learning behavior

TL;DR

This paper investigates using large language models to simulate student learning behaviors, aiming to improve educational research and curriculum design by creating realistic virtual student models based on demographic and interaction data.

Contribution

It demonstrates the feasibility of LLMs for detailed student behavior simulation, moving beyond prediction to replicating complex learning patterns and interactions.

Findings

Simulated outcomes align with actual student demographics.

More assessment history improves simulation realism.

Strong correlation between behaviors and course interactions.

Abstract

Student simulation presents a transformative approach to enhance learning outcomes, advance educational research, and ultimately shape the future of effective pedagogy. We explore the feasibility of using large language models (LLMs), a remarkable achievement in AI, to simulate student learning behaviors. Unlike conventional machine learning based prediction, we leverage LLMs to instantiate virtual students with specific demographics and uncover intricate correlations among learning experiences, course materials, understanding levels, and engagement. Our objective is not merely to predict learning outcomes but to replicate learning behaviors and patterns of real students. We validate this hypothesis through three experiments. The first experiment, based on a dataset of N = 145, simulates student learning outcomes from demographic data, revealing parallels with actual students concerning various demographic factors. The second experiment (N = 4524) results in increasingly realistic simulated behaviors with more assessment history for virtual students modelling. The third experiment (N = 27), incorporating prior knowledge and course interactions, indicates a strong link between virtual students' learning behaviors and fine-grained mappings from test questions, course materials, engagement and understanding levels. Collectively, these findings deepen our understanding of LLMs and demonstrate its viability for student simulation, empowering more adaptable curricula design to enhance inclusivity and educational effectiveness.