Enhanced autoregressive value-added model integrating generative AI ethical optimization for fair high school teacher evaluation
DOI:
https://doi.org/10.55056/etq.1164Keywords:
value-added model, autoregressive knowledge tracing, high school teacher evaluation, educational equity, generative artificial intelligence ethicsAbstract
Artificial intelligence (AI) is increasingly integrated into educational contexts, yet systematic frameworks that propose and empirically validate fair value-added models (VAM) for high school teacher evaluation remain scarce. This study introduces and validates the IBI-VAM framework, which integrates autoregressive knowledge tracing, Heckman selection bias correction, and generative AI ethical optimization to improve the predictive accuracy, fairness, and trustworthiness of teacher contribution estimates. Using longitudinal panel data from a non-elite public high school in eastern China (N = 1,876 students, 6 semesters, 9 subjects), the framework was empirically validated. Results showed a 12% improvement in AUC compared with the OLS baseline (0.83 vs. 0.72), bias reduction to below 3%, ranking stability (Spearman's r = 0.92), and a 0.5-point increase on the trust scale (p < 0.05). Monte Carlo simulations (1,000 iterations) and Kolmogorov-Smirnov tests indicated small average distributional differences between the Chinese sample and simulated Western high school distributions (KS D = 0.060, p > 0.05), with IBI-VAM bias estimates exhibiting an average absolute relative deviation of approximately 5% from those obtained under moment-matched Western high-school distributional assumptions, providing preliminary evidence for cross-cultural applicability.
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Data Availability Statement
Due to privacy restrictions and ethical guidelines approved by the school's institutional review board, the raw dataset cannot be made publicly available in order to protect student anonymity and comply with data minimization principles. The processed anonymized dataset, full replication code, supplementary materials (including all figures, behavioral log derivations, additional diagnostic charts, sensitivity analyses, fairness metrics, and ethics documents), and anonymized data excerpts have been permanently archived on Zenodo and are accessible via the following DOI: https://doi.org/10.5281/zenodo.17114987.
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Copyright (c) 2026 Xia Yang
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Accepted 2026-07-02
Published 2026-09-20
