Beyond Predictive Accuracy: A Critical Synthesis of AI-Powered Student Performance Modeling and the Problem of Algorithmic Personalization in Education
Keywords:
Artificial intelligence in education, student performance prediction, intelligent tutoring systems, algorithmic fairness, learning analyticsAbstract
The integration of artificial intelligence into educational settings has produced a proliferation of student performance prediction models and personalized tutoring systems, yet the scholarly community has paid insufficient attention to the epistemological and ethical tensions that accompany these technical advances. This paper presents a critical conceptual synthesis of the literature on AI-powered student performance modeling and personalized tutoring, examining not only what these systems can do but also what assumptions they embed about learning, knowledge, and the role of algorithmic judgment in educational settings. Drawing on a systematic analysis of empirical studies published between 2019 and 2025, we identify three core tensions that recur across the literature: the tradeoff between predictive accuracy and model interpretability, the conflict between individualized adaptation and equitable access, and the unresolved relationship between algorithmic recommendations and pedagogical authority. Our analysis reveals that while hybrid deep learning architectures and multimodal data integration have significantly improved prediction performance, these gains often come at the cost of transparency and fairness. Moreover, the dominant framing of personalization in the literature conflates statistical individualization with genuine pedagogical responsiveness. We propose a framework for responsible AI integration in education that prioritizes human centered design, critical algorithmic literacy, and equity oriented implementation. The paper concludes by identifying priority areas for future research, including longitudinal studies of AI tutoring effects on diverse learner populations and the development of fairness aware modeling approaches that do not sacrifice interpretability.References
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