ai-enhanced faculty feedback system for improving teaching effectiveness
Kumari R,Lavanya J,Manju V, Abinaya K
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 01 April,2026 Pages:2080-2087
Abstract
Faculty evaluation is essential for maintaining teaching quality and improving student learning outcomes in higher education institutions. Traditional feedback systems mainly rely on numerical rating scales and manual analysis, which often fail to capture detailed student opinions and actionable insights. This paper proposes an AI-Enhanced Faculty Feedback System that integrates machine learning and natural language processing techniques to improve evaluation accuracy and reliability. The system combines structured rating-based assessment with automated sentiment analysis of open-ended student comments to identify emotional tone, key themes and recurring suggestions. Analytical results are presented through visualization dashboards to support faculty self-improvement and administrative decision-making. The proposed approach ensures transparency, confidentiality and fairness while minimizing bias, thereby promoting continuous enhancement of teaching effectiveness.
Kewords
Artificial Intelligence, Natural Language Processing, Teaching Effectiveness, Data Analytics
Reference
[1] D. D. Dsouza, Deepika, D. P. Nayak, k E. J. Machado and N. D. Adesh, “Sentimental Analysis of Student Feedback using Machine Learning Techniques,” International Journal of Recent Technology and Engineering, vol. 8, no. 1 Special Issue 4,pp.986.991,Jun.2019,doi:10.1109/IJRTE.2019. [2] K. M. L. Santillan, “Streamlining Faculty Evaluations: A Web-Based System for Enhanced Efficiency and Data-Driven Insights,” Journal of Innovative Technology Convergence, vol. 6, no. 2, pp. 41–48, Jun. 2024, doi:10.69478/JITC2024v6n002a04. [3] S. A. Patil, K. P. Adhiya and G. K. Patnaik, “Sentiment Analysis of Students’ Feedback Using Lexicon Based Method and Hybrid Machine Learning Method,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 12s, pp. 137–145, 2024, doi:10./IJISAE2024v12n12s. [4] Sowmiya Baskar, “A Comprehensive Review on Techniques in Sentiment Analysis for Improving Teaching and Learning through Students’ Feedback, ”Next Generation Computing Systems and Technologies, vol. 1, no. 1, pp. 11–17, Oct.2025,doi:10.62762/NGCST.2025.927566. [5] Smitha B. Anjan Prasad and R. P. Kumar Nakka, “Supervised Sentiment Analysis of Indirect Qualitative Student Feedback for Unbiased Opinion Mining,” Engineering Proceedings, vol. 59, no. 1, p. 15, Dec.2023,doi:10.3390/engproc202305901. [6] J. Heer, M. Bostock and V. Ogievetsky, “A Tour through the Visualization Zoo,” Communications of the ACM, vol. 53, no. 6, pp. 59–67, Jun.2010,doi:10.1145/1743546.1743567. [7] E. Cambria and B. White, “Jumping NLP Curves: A Review of Natural Language Processing Research,” IEEE Computational Intelligence Magazine, vol. 9, no. 2, pp. 48–57, May2014,doi:10.1109/MCI.2014.2307227. [8] A. Onan, “Aspect-Based Sentiment Analysis Using Machine Learning and Deep Learning Approaches,” IEEE Access, vol. 8, pp. 150728–150744, Aug.2020,doi:10.1109/ACCESS.2020.3016533. [9] S. M. Mohammad, “Sentiment Analysis: Automatically Extracting Opinions from Text,” ACM Computing Surveys, vol. 52, no. 4, pp. 1.36,Aug.2020,doi:10.1145/3383445. [10] M. Liu, Y. Liu and Q. Wang, “Sentiment Analysis of Student Feedback Using Machine Learning Techniques,” International Journal of Educational Technology in Higher Education, vol. 16, no. 1, pp. 1–18, Mar. 2019, doi:10.1186/s41239-019-0149-2. [11] R. S. Baker and P. S. Inventado, “Educational Data Mining and Learning Analytics,” in Learning Analytics, New York, NY, USA: Springer, pp. 61–75, 2014, doi:10.1007/978-1-4614-3305-7_4. [12] C. D. Manning, P. Raghavan and H. Schütze, “Introduction to Information Retrieval,” Cambridge University Press, Cambridge, 2008, doi:10.1017/CBO9780511809071. [13] M. Chen, S. Mao and Y. Liu, “Big Data: A Survey,” Mobile Networks and Applications, vol. 19, no. 2, pp. 171.209,Apr.2014,doi:10.1007/s11036-013-0489-0. [14] A. Anderberg and A. E. Jones, “Automated Analysis of Course Evaluations Using Text Mining,” Journal of Educational Data Mining, vol. 11, no. 2, pp. 1–23, Jun. 2019, doi:10.5281/zenodo.3554654. [15] S. Aggarwal and C. Zhai, “Mining Text Data,” Springer, New York, 2012, doi:10.1007/978-1-4614-3223-4. [16] K. Ravi and V. Ravi, “A Survey on Opinion Mining and Sentiment Analysis: Tasks, Approaches and Applications,” Knowledge-Based Systems, vol. 89, pp. 14–46, Nov. 2015,doi:10.1016/j.knosys.2015.06.01 [17] B. Liu, “Sentiment Analysis and Subjectivity,” in Handbook of Natural Language Processing, 2nd ed., Boca Raton, FL, USA: CRC Press, pp. 627–666,2010,doi:10.1201/9781420085938-c29.
