AUTOMATED OPINION-GIVING MODELS

Authors

  • Madaminov Shokhrukhbek Marufjon ugli Teacher at Andijan State Technical Institute

Keywords:

feedback, automated model, artificial intelligence, data analysis, educational technologies.

Abstract

This article theoretically analyzes automated models of the feedback process and highlights their potential applications in education, production, and business. Automated feedback systems reduce the human factor and provide fast and accurate analysis. The study presents existing technologies, algorithmic approaches, and recommendations for their practical application .

Downloads

Download data is not yet available.

References

Shute, VJ (2008). Focus on Formative Feedback. Review of Educational Research , 78(1), 153–189.

https://doi.org/10.3102/0034654307313795

Nicol, DJ, Thomson, A., & Breslin, C. (2014). Rethinking Feedback Practices in Higher Education: A Peer Review Perspective. Assessment & Evaluation in Higher Education , 39(1), 102–122.

https://doi.org/10.1080/02602938.2013.795518

Wang, Y., & Heffernan, N. (2013). The "Assistance" Model: Leveraging Machine Learning to Provide Adaptive Feedback. International Journal of Artificial Intelligence in Education , 23(4), 475–491.

https://doi.org/10.1007/s40593-013-0014-x

Turner, A., & De Raadt, M. (2013). Automated Feedback for Programming Assignments. Computer Science Education , 23(4), 313–337.

https://doi.org/10.1080/08993408.2013.846777

Lee, J., & Lee, J. (2015). IoT-Based Real-Time Monitoring and Feedback System for Manufacturing Processes. Journal of Manufacturing Systems , 37, 110–120.

https://doi.org/10.1016/j.jmsy.2015.06.004

Boude, D., & Molloy, E. (2013). Rethinking Models of Feedback for Learning: The Challenge of Design. Assessment & Evaluation in Higher Education , 38(6), 698–712.

https://doi.org/10.1080/02602938.2012.691462

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N. , ... & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems , 30, 5998–6008.

https://arxiv.org/abs/1706.03762

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT .

https://doi.org/10.18653/v1/N19-1423

Jurafsky, D., & Martin, JH (2021). Speech and Language Processing (3rd ed. draft).

https://web.stanford.edu/~jurafsky/slp3/

Kuhlthau, CC (1991). Inside the Search Process: Information Seeking from the User's Perspective. Journal of the American Society for Information Science , 42(5), 361–371.

https://doi.org/10.1002/(SICI)1097-4571(199106)42:5<361::AID-ASI6>3.0.CO;2-#

D'Mello, SK, & Graesser, A. (2012). AutoTutor and Affective AutoTutor: Learning by Talking with Cognitively and Emotionally Intelligent Computers that Talk Back. ACM Transactions on Interactive Intelligent Systems , 2(4), 23.

https://doi.org/10.1145/2395123.2395129

Buckingham Shum, S., & Crick, RD (2012). Learning Analytics. Educational Technology & Society , 15(3), 3–26.

https://www.j-ets.net/ETS/journals/15_3/1.pdf

Downloads

Published

2025-08-20

How to Cite

Madaminov Shokhrukhbek Marufjon ugli. (2025). AUTOMATED OPINION-GIVING MODELS. International Multidisciplinary Journal for Research & Development, 12(08). Retrieved from https://ijmrd.in/index.php/imjrd/article/view/3652

Issue

Vol. 12 No. 08 (2025): International Multidisciplinary Journal for Research & Development

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.