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

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Year : 2025 | Volume : 16 | 01 | Page :
    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

How to cite this article:
Mudit Kumar Verma. Exploring AI-Driven Student Performance Analysis as a Dimension of an AI-Powered Assessment & Feedback System: A Comprehensive Review. Current Trends in Information Technology. 2025; 16(01):-.
How to cite this URL:
Mudit Kumar Verma. Exploring AI-Driven Student Performance Analysis as a Dimension of an AI-Powered Assessment & Feedback System: A Comprehensive Review. Current Trends in Information Technology. 2025; 16(01):-. Available from: https://journals.stmjournals.com/ctit/article=2025/view=233243


References

  1. Clement T, Kemmerzell N, Abdelaal M, Amberg M. XAIR: a systematic metareview of explainable AI (XAI) aligned to the software development process. Machine Learning and Knowledge Extraction. 2023 Mar;5(1):78-108.
  2. Fahd K, Venkatraman S, Miah SJ, Ahmed K. Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature. Education and Information Technologies. 2022 Apr;27(3):3743-75.
  3. Yin S, Shang Q, Wang H, Che B. The analysis and early warning of student loss in MOOC course. InProceedings of the ACM Turing Celebration Conference-China 2019 May 17 (pp. 1-6).
  4. Hung JL, Wang MC, Wang S, Abdelrasoul M, Li Y, He W. Identifying at-risk students for early interventions—A time-series clustering approach. IEEE Transactions on Emerging Topics in Computing. 2015 Nov 26;5(1):45-55.
  5. Dai S, Dai W, Cheong J, Liang PP. FairGRPO: Fair Reinforcement Learning for Equitable Clinical Reasoning. arXiv preprint arXiv:2510.19893. 2025 Oct 22.
  6. Raza H. Ai-driven assessment: Reliability, bias, and ethical implications. AI EDIFY Journal. 2024;1(2):36-47.
  7. Xiaoyu Z, Tobias TC. Exploring the efficacy of adaptive learning technologies in online education: A longitudinal analysis of student engagement and performance. Int. J. Sci. Eng. Appl. 2023;12(12):28-31.
  8. Chen W, Zhao X, Shahabi H, Shirzadi A, Khosravi K, Chai H, Zhang S, Zhang L, Ma J, Chen Y, Wang X. Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree. Geocarto International. 2019 Sep 19;34(11):1177-201.
  9. Devlin J, Chang MW, Lee K, Toutanova K. Bert: Pre-training of deep bidirectional transformers for language understanding. InProceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) 2019 Jun (pp. 4171-4186).
  10. Jalal A, Mahmood M. Students’ behavior mining in e-learning environment using cognitive processes with information technologies. Education and Information Technologies. 2019 Sep 13;24(5):2797-821.
  11. Amrieh EA, Hamtini T, Aljarah I. Mining educational data to predict student’s academic performance using ensemble methods. International journal of database theory and application. 2016 Aug 31;9(8):119-36.
  12. Wilson D, Wright J, Summers L. Mapping patterns of student engagement using cluster analysis. Journal for STEM Education Research. 2021 Sep;4(2):217-39.
  13. Čep A, Bernik A, Tomičić I. Adaptive Learning Systems in Higher Education: Challenges, Trends, and Outcomes. InProceedings of the Future Technologies Conference 2026 (pp. 1- 17). Springer, Cham.
  14. Firdaus M, Jain U, Ekbal A, Bhattacharyya P. SEPRG: Sentiment aware emotion controlled personalized response generation. InProceedings of the 14th International Conference on Natural Language Generation 2021 Aug (pp. 353-363).
  15. Sun Y, Xu X. The Development of Personal Learning Environments in Higher Education. Routledge. http://dx. doi. org/10.4324/9781003285243; 2024.
  16. Perks S. AI could reduce teacher workload. Physics World. 2020 Aug 1;33(8):11.
  17. Gandhi N, Gopalan K, Prasad P. A Support Vector Machine based approach for plagiarism detection in Python code submissions in undergraduate settings. Frontiers in Computer Science. 2024 Jun 13;6:1393723.
  18. Yi M. Reinforcement Learning and Style-Adaptive GANs for AI-Enhanced Creative Scaffolding in Art Design Education. InProceedings of the 2025 3rd International Conference on Educational Knowledge and Informatization 2025 Jul 11 (pp. 167-171).
  19. Feng R, Zheng HJ, Gao H, Zhang AR, Huang C, Zhang JX, Luo K, Fan JR. Recurrent Neural Network and random forest for analysis and accurate forecast of atmospheric pollutants: a case study in Hangzhou, China. Journal of cleaner production. 2019 Sep 10;231:1005-15.
  20. Jokhan A, Sharma B, Singh S. Early warning system as a predictor for student performance in higher education blended courses. Studies in Higher Education. 2019 Nov 2;44(11):1900-11.
  21. Chávez Urbina JC, Valencia Chávez FA, Zambrano Hidalgo MC. Ethical Considerations in AI-Based Assessment Tools for Higher Education. Sinergia Académica. 2025 Oct 1;8(8).
  22. Davis M, Burgher KE. Predictive analytics for student retention: Group vs. individual behavior. College and University. 2013 Jul 1;88(4):63.
  23. Thaichon P, Quach S, editors. Artificial intelligence for marketing management. Routledge; 2023.
  24. O’Connor S, Leonowicz E, Allen B, Denis-Lalonde D. Artificial intelligence in nursing education 1: strengths and weaknesses. Nurs Times. 2023 Sep 11;119(10).
  25. Yoo KM, Park D, Kang J, Lee SW, Park W. GPT3Mix: Leveraging large-scale language models for text augmentation. InFindings of the Association for Computational Linguistics: EMNLP 2021 2021 Nov (pp. 2225-2239).
  26. Pierce WD, Cheney CD. Behavior analysis and learning: A biobehavioral approach. Routledge; 2017 Jun 14.
  27. Amrieh EA, Hamtini T, Aljarah I. Mining educational data to predict student’s academic performance using ensemble methods. International journal of database theory and application. 2016 Aug 31;9(8):119-36.
  28. Senanayake C, Asanka D. Rubric based automated short answer scoring using large language models (LLMs). In2024 international research conference on smart computing and systems engineering (SCSE) 2024 Apr 4 (Vol. 7, pp. 1-6). IEEE.
  29. Khamparia A, Pandey B. SVM and PCA based learning feature classification approaches for e-learning system. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT). 2018 Apr 1;13(2):32-45.
  30. Fragiadakis G, Diou C, Kousiouris G, Nikolaidou M. Evaluating human-ai collaboration: A review and methodological framework. arXiv preprint arXiv:2407.19098. 2024 Jul 9.
  31. Rodrigues RL, Ramos JL, Silva JC, Gomes AS. Discovery engagement patterns MOOCs through cluster analysis. IEEE Latin America Transactions. 2016 Dec 16;14(9):4129-35.
  32. Chinta SV, Wang Z, Yin Z, Hoang N, Gonzalez M, Quy TL, Zhang W. FairAIED: Navigating fairness, bias, and ethics in educational AI applications. arXiv preprint arXiv:2407.18745. 2024 Jul 26.
  33. Govea J, Ocampo Edye E, Revelo-Tapia S, Villegas-Ch W. Optimization and scalability of educational platforms: Integration of artificial intelligence and cloud computing. Computers. 2023 Nov 1;12(11):223.
  34. Asselman A, Khaldi M, Aammou S. Enhancing the prediction of student performance based on the machine learning XGBoost algorithm. Interactive Learning Environments. 2023 Aug 18;31(6):3360-79.
  35. Nikhil V, Annamalai R, Jayapal S. NLP-Driven Approaches to Automated Essay Grading and Feedback. InAdopting Artificial Intelligence Tools in Higher Education 2025 May 27 (pp. 99- 117). CRC Press.
  36. Ercikan K. Efficacy, Validity and Fairness Considerations in AI-Driven Assessments. Handbook for Assessment in the Service of Learning. 2025;1.
  37. Tang S, Peterson JC, Pardos ZA. Deep neural networks and how they apply to sequential education data. InProceedings of the third (2016) acm conference on learning@ scale 2016 Apr 25 (pp. 321-324).
  38. Afzaal M, Nouri J, Zia A, Papapetrou P, Fors U, Wu Y, Li X, Weegar R. Explainable AI for data-driven feedback and intelligent action recommendations to support students self- regulation. Frontiers in Artificial Intelligence. 2021 Nov 12;4:723447.
  39. Prinsloo P, Kaliisa R. Data privacy on the African continent: Opportunities, challenges and implications for learning analytics. British Journal of Educational Technology. 2022 Jul;53(4):894-913.
  40. Polyportis A. A longitudinal study on artificial intelligence adoption: understanding the drivers of ChatGPT usage behavior change in higher education. Frontiers in Artificial Intelligence. 2024 Jan 5;6:1324398.
  41. Owatari T, Shimada A, Minematsu T, Hori M, Taniguchi RI. Real-time learning analytics dashboard for students in online classes. In2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE) 2020 Dec 8 (pp. 523-529). IEEE.
  42. Hunt XJ, Kabul IK, Silva J. Transfer learning for education data. InProceedings of the ACM SIGKDD Conference, El Halifax, NS, Canada 2017 Aug (Vol. 17, pp. 3-2).
  43. Khedekar L, Bhide A, Chandak N, Bharadiya A, Bodhale Y, Chalke Y. Revolutionizing education: An AI-powered learning platform for the future. InAIP Conference Proceedings 2025 Oct 3 (Vol. 3325, No. 1, p. 040024). AIP Publishing LLC.
  44. Lee G, Shi L, Latif E, Gao Y, Bewersdorff A, Nyaaba M, Guo S, Liu Z, Mai G, Liu T, Zhai X. Multimodality of ai for education: Towards artificial general intelligence. IEEE Transactions on Learning Technologies. 2025 May 28.
  45. Arif S. Cross-cultural perspectives on AI in education: Case studies from global classrooms. AI EDIFY Journal. 2025 Mar 15;2(1):12-20.
Ahead of Print Subscription Review Article
Volume 16
01
Received 12/09/2025
Accepted 14/10/2025
Published 29/11/2025
Publication Time 78 Days
144

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