Date
Publisher
arXiv
Simulated Students offer a valuable methodological framework for evaluating pedagogical approaches and modelling diverse learner profiles, tasks which are otherwise challenging to undertake systematically in real-world settings. Recent research has increasingly focused on developing such simulated agents to capture a range of learning styles, cognitive development pathways, and social behaviours. Among contemporary simulation techniques, the integration of large language models (LLMs) into educational research has emerged as a particularly versatile and scalable paradigm. LLMs afford a high degree of linguistic realism and behavioural adaptability, enabling agents to approximate cognitive processes and engage in contextually appropriate pedagogical dialogues. This paper presents a thematic review of empirical and methodological studies utilising LLMs to simulate student behaviour across educational environments. We synthesise current evidence on the capacity of LLM-based agents to emulate learner archetypes, respond to instructional inputs, and interact within multi-agent classroom scenarios. Furthermore, we examine the implications of such systems for curriculum development, instructional evaluation, and teacher training. While LLMs surpass rule-based systems in natural language generation and situational flexibility, ongoing concerns persist regarding algorithmic bias, evaluation reliability, and alignment with educational objectives. The review identifies existing technological and methodological gaps and proposes future research directions for integrating generative AI into adaptive learning systems and instructional design.
Study at a glance
- Question: How do large language models simulate student behavior, and how accurately do they support educational practices?
- Setting and data: Literature review of 100 studies (83 peer-reviewed papers and 17 preprints) published primarily between 2021 and 2025 across multiple databases.
- Method: Thematic literature review (systematic search using PRISMA framework)
Why it matters
Simulating students allows educators to test pedagogical strategies, curriculum design, and assessments safely. However, earlier rule-based systems lacked flexibility, and whether large language models can faithfully reproduce diverse human learning behaviors remains under investigation.
Key findings
- Large language models generate linguistically coherent interactions and basic learning trajectories, but their behavioral fidelity sits in an intermediate band inadequate for high-stakes settings.
- Current models struggle to capture spontaneous mistakes, long-term memory management, and genuine affective variability that characterize real learners.
- Prompt-based profiles and knowledge tracing enable simulated students to calibrate test items and support teacher training, but lack standardized validation metrics.
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