Student Development Agent: Risk-free Simulation for Evaluating AIED Innovations

2025-10-10

AI Summary

Amid growing demands for proactive ethical and safety evaluation in AI in Education (AIED), this work addresses the challenge of predicting long-term impacts of educational interventions on students’ non-cognitive skills (e.g., motivation, self-regulation, social-emotional competencies) without real-world risks. We propose a large language model (LLM)-based student development agent framework that dynamically models heterogeneous student traits through role-based configuration, context-aware interaction, and multi-agent collaborative learning. Validated in the MAIC simulation environment, our agents generate developmental trajectories highly consistent with longitudinal empirical data (r > 0.85), outperforming baseline models significantly. This study constitutes the first systematic application of LLM-driven individualized development agents for pre-deployment validation in AIED, establishing a psychologically grounded, interpretable, and scalable virtual trial paradigm for educational AI.

Technology Category

• Multiagent Systems: Modeling other Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Learning Human Values and Preferences

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Agentic search

Abstract

In the age of AI-powered educational (AIED) innovation, evaluating the developmental consequences of novel designs before they are exposed to students has become both essential and challenging. Since such interventions may carry irreversible effects, it is critical to anticipate not only potential benefits but also possible harms. This study proposes a student development agent framework based on large language models (LLMs), designed to simulate how students with diverse characteristics may evolve under different educational settings without administering them to real students. By validating the approach through a case study on a multi-agent learning environment (MAIC), we demonstrate that the agent's predictions align with real student outcomes in non-cognitive developments. The results suggest that LLM-based simulations hold promise for evaluating AIED innovations efficiently and ethically. Future directions include enhancing profile structures, incorporating fine-tuned or small task-specific models, validating effects of empirical findings, interpreting simulated data and optimizing evaluation methods.

Problem

• Simulating student development to evaluate AIED innovations safely
• Predicting educational impacts without exposing real students to risks
• Validating LLM-based simulations against real student outcome data

Innovation

• Simulates student development using large language models
• Evaluates educational interventions without real students
• Validates predictions through multi-agent learning case study