Volume: 3 Issue: Special Issue 1
Year: 2026, Page: 69-77, Doi: https://doi.org/10.70372/jeltp.v3.sp1.10
In outcome-oriented higher education, feedback from students is essential to ensure the teaching–learning (T-L) quality and continual improvement. The majority of Higher Education Institutions (HEIs) still rely on manual analysis, despite the fact that feedback can often be collected online. This leads to delayed actions and a lack of actionable insights. In order to solve this, we have integrated the Student LMS Portal to an AI-assisted feedback analysis system. This article presents the technical details and implementation of the proposed feedback analysis system. The system instantly generates quantitative ratings and AI-derived qualitative insights using Natural Language Processing (NLP) for sentiment and thematic analysis once processing 23 multiple-choice questions and one open-ended response. Timely pedagogical interventions and accreditation requirements are supported by automated dashboards, recurring reports, and action-taken documentation. This automation greatly lessens the administrative workload and improves the teaching-learning process's entire efficacy, efficiency, and transparency.
Keywords: AI-assisted feedback; educational analytics; LMS; NLP; quality assurance; student feedback system.
Aleksendrić, M., Batra, S., Palmer, R., & Ranjan, S. (2024). Full stack FastAPI, React, and MongoDB: fast-paced web app development with the FARM stack. Packt Publishing Ltd.
Çetin, U., & Gündoğmuş, Y. E. (2024, October). A Glimpse to Scalable LLM Architectures: Building Real-Time Sentiment Analyser with Kafka and FastAPI. In 2024 9th International Conference on Computer Science and Engineering (UBMK) (pp. 660-664). IEEE.
Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International journal of Technology Enhanced learning, 4(5-6), 318-331.
Datnow, A., & Hubbard, L. (2016). Teacher capacity for and beliefs about data-driven decision making: A literature review of international research. Journal of Educational Change, 17(1), 7-28.
Didolkar, A., & Lokulwar, P. (2025, January). Sentiment Detection in Financial News: A Deep learning Approach to Extreme and Moderate Classification. In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) (pp. 1-8). IEEE.
Jamil, F., & Hameed, I. A. (2023). Toward intelligent open-ended questions evaluation based on predictive optimization. Expert Systems with Applications, 231, 120640.
Kumar, D. UGC's New Faculty Appraisal Process: A Holistic Approach to Academic Performance Evaluation.
Mousavi, A., Mares, C., & Stonham, T. J. (2015). Continuous feedback loop for adaptive teaching and learning process using student surveys. International Journal of Mechanical Engineering Education, 43(4), 247-264.
Mohanty, A. (2025). Integrating Natural Language Processing With Expert Systems For Streamlined Evaluation Of Applications.
Norris, D., Baer, L., Leonard, J., Pugliese, L., & Lefrere, P. (2008). Action analytics: Measuring and improving performance that matters in higher education. EDUCAUSE review, 43(1), 42.
Saha, G. (2016). Implementation of total quality management (TQM) in educational institutions. International Education and Research Journal, 2(6), 63-64.
Shaik, T., Tao, X., Li, Y., Dann, C., McDonald, J., Redmond, P., & Galligan, L. (2022). A review of the trends and challenges in adopting natural language processing methods for education feedback analysis. IEEE Access, 10, 56720-56739.
Wilson, J., Pollard, B., Aiken, J. M., Caballero, M. D., & Lewandowski, H. J. (2022). Classification of open-ended responses to a research-based assessment using natural language processing. Physical Review Physics Education Research, 18(1), 010141.
Yeap, B. H. (2008). A new perspective for quality improvement in teaching and learning processes.
Yun, G., Lee, K. M., & Choi, H. H. (2025). Empowering student learning through artificial intelligence: A bibliometric analysis. Journal of Educational Computing Research, 62(8), 1822-1855.
Shashi Kant Dargar, K. Rajesh, Abha Dargar, C. Sivapragasam, Kushvanth Yalamanchi, Avulapati Giridhar. AI-Assisted Platform for Centralized Teaching and Learning Student Feedback Analysis. Journal of Effective Teaching and Learning Practices. 2026;3(Sp1):69-77