Exploring AI-Driven Student Performance Analysis as a Dimension of an AI-Powered Assessment and Feedback System: A Comprehensive Review

Year : 2026 | Volume : 16 | Issue : 01 | Page : 24 31
    By

    Mudit Kumar Verma,

  1. Assistant Professor, School of Education, BBD University, Lucknow, Uttar Pradesh, India

Abstract

The rapid proliferation of artificial intelligence (AI) in educational technology has heralded a paradigmatic transformation in assessment methodologies, transitioning from static, summative evaluations to dynamic, data-driven systems that emphasize continuous formative feedback. This comprehensive review interrogates AI-driven student performance analysis as a cardinal dimension of AI-powered assessment and feedback systems (AI-PAFS), synthesizing findings from forty-five rigorously curated open-access empirical studies published between 2015 and 2024. Employing a methodological lens, the study elucidates the comparative efficacies of supervised, unsupervised, and reinforcement learning models in academic performance prediction and delineates their integration with natural language processing (NLP) frameworks to generate automated, adaptive, and contextually nuanced feedback. The analysis reveals a discernible shift from deterministic, rule-based algorithms to sophisticated, explainable AI (XAI) systems that prioritize transparency, fairness, and ethical accountability. Furthermore, the review identifies key applications of AI in early warning systems, adaptive learning trajectories, and automated grading mechanisms, all of which augment educators’ capacity for timely and targeted pedagogical interventions. Through bibliometric trend analysis, the paper traces the temporal evolution of AI applications in education, culminating in contemporary concerns surrounding algorithmic bias, data privacy, scalability, and human–AI collaboration. It posits that the future of AI in education lies not merely in automation, but in the development of equitable, interpretable, and ethically aligned systems that synergize computational precision with human pedagogical wisdom. The paper concludes by outlining critical research imperatives and policy considerations essential for realizing the transformative potential of AI-PAFS in fostering inclusive and learner-centric educational ecosystems.

Keywords: Artificial intelligence, AI-powered assessment and feedback system, student performance, deep learning, algorithmic

[This article belongs to Current Trends in Information Technology ]

How to cite this article:
Mudit Kumar Verma. Exploring AI-Driven Student Performance Analysis as a Dimension of an AI-Powered Assessment and Feedback System: A Comprehensive Review. Current Trends in Information Technology. 2025; 16(01):24-31.
How to cite this URL:
Mudit Kumar Verma. Exploring AI-Driven Student Performance Analysis as a Dimension of an AI-Powered Assessment and Feedback System: A Comprehensive Review. Current Trends in Information Technology. 2025; 16(01):24-31. Available from: https://journals.stmjournals.com/ctit/article=2025/view=236627


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Regular Issue Subscription Review Article
Volume 16
Issue 01
Received 12/09/2025
Accepted 14/10/2025
Published 29/11/2025
Publication Time 78 Days
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