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

Jianxiao Jiang, Yu Zhang

TL;DR

This work addresses the challenge of evaluating AI-enabled educational designs without risking real students by proposing a large language model (LLM)–based Student Development Agent that simulates long-term developmental trajectories under varied learning environments. The approach combines a structured input-output framework, a general education categorization, empirical findings integration, and iterative prompting to produce self-evolving predictions of both learning behaviors and developmental dimensions. Validation in a multi-agent MAIC case study shows competitive predictive performance against baselines, particularly for non-cognitive outcomes, while highlighting the ethical and practical value of risk-free evaluation. Collectively, the framework offers a scalable, transparent, and adaptable platform for informing AIED design, policy, and future methodological advances in educational research.

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.

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

TL;DR

This work addresses the challenge of evaluating AI-enabled educational designs without risking real students by proposing a large language model (LLM)–based Student Development Agent that simulates long-term developmental trajectories under varied learning environments. The approach combines a structured input-output framework, a general education categorization, empirical findings integration, and iterative prompting to produce self-evolving predictions of both learning behaviors and developmental dimensions. Validation in a multi-agent MAIC case study shows competitive predictive performance against baselines, particularly for non-cognitive outcomes, while highlighting the ethical and practical value of risk-free evaluation. Collectively, the framework offers a scalable, transparent, and adaptable platform for informing AIED design, policy, and future methodological advances in educational research.

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.

Paper Structure

This paper contains 56 sections, 5 equations, 5 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Motivation
  3. Related Work
  4. Research Questions
  5. Research Design
  6. Design Framework of Student Development Agent
  7. Simulation Approach of Student Development Agent
  8. Categorization & Value Assignment
  9. Empirical findings acquisition
  10. Prompt Construction
  11. Learning Environments
  12. Endowment Dimensions
  13. Developmental Dimensions
  14. Actions
  15. Empirical findings
  16. ...and 41 more sections

Figures (5)

  • Figure 1: A Design Framework of Student Development Agent
  • Figure 2: Simulation approach of student development agents
  • Figure 3: Process of constructing general education categorization
  • Figure 4: Simulation process of student agents on the MAIC platform
  • Figure 5: Distribution and paired t-test results between development prediction results from four methods and corresponding true values