Recovering Stranded Discrimination in Knowledge Tracing: Per-Item Bias Correction via Empirical-Bayes Shrinkage
arXiv:2606.14123v1 Announce Type: cross Abstract: Deployed knowledge-tracing models are typically frozen after training, yet systematic per-item logit bias arises, from limited per-item expressivity in backbone architectures and from post-deployment shifts in item properties, degrading prediction quality. Global post-hoc calibrators such as Platt scaling, temperature scaling, and isotonic regression improve probability estimates but leave discriminative ability, as measured by AUC, unchanged. This AUC invariance is a structural consequence of monotone score-only transforms; recovering the stra
The proliferation of deployed AI models in real-world settings highlights the limitations of 'frozen' models, necessitating new techniques to maintain performance and fairness over time.
Improving the accuracy and fairness of deployed AI models, particularly in educational or assessment-focused applications, is crucial for trustworthy and effective AI systems.
This research proposes a method to correct per-item bias in knowledge-tracing models, enhancing their discriminative ability and predictive quality post-deployment.
- · AI model developers
- · Educational technology platforms
- · Students/Users of AI assessment tools
- · Researchers in AI fairness and robustness
- · AI systems with unaddressed post-deployment bias
More accurate and reliable AI performance over time in dynamic environments.
Increased trust and broader adoption of AI systems that adapt and self-correct.
Reduced societal harms and biases propagated by outdated or uncalibrated AI models, particularly in sensitive applications.
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Read at arXiv cs.AI