Application of GenAI in Outcome-Based Education in Teaching Learning Processes

Authors

  • Snigdharani Panda * Department of Computer Science and Engineering, GIFT Autonomous, Bhubaneswar, Odisha-752054, India.
  • Pritiprava Mishra Department of Computer Science and Engineering, GIFT Autonomous, Bhubaneswar, Odisha-752054, India.

https://doi.org/10.22105/opt.v3i1.106

Abstract

An example of a revolutionary shift in higher education in India is the integration of Generative Artificial Intelligence (GenAI) into the Outcome-Based Education (OBE) framework. National organizations like All India Council for Technology and Education (AICTE), National Board of Accrediation (NBA), and National Assessment and Accrediation Council (NAAC) for accreditation and quality assurance encourage Indian engineering colleges to use learner centred and data-driven methods for learning and continuous improvement of students. Every stage of the educational cycle, from curriculum design to evaluation and documentation, could benefit from the use of GenAI technologies, such as Large Language Models (LLM), content generation systems, and adaptive learning platforms. In addition to providing applications for teaching, assessment, research, and accreditation, the paper addresses at how GenAI technologies complement and enhance the AICTE-NBA-NAAC ecosystem in higher education. This paper is also provides some of the major issues with data integrity, ethical considerations, and faculty awareness and suggests ways to advance responsible AI use in higher education.

Keywords:

Generative artificial intelligence , Large language models, Outcome-based education, Accreditation, Higher education

References

  1. [1] NBA. (2020). Accreditation manual for undergraduate engineering programs (Tier-I). https://www.nbaind.org/files/UG - Tier I Manual.pdf
  2. [2] Spady, W. G. (1994). Outcome-based education: critical issues and answers. ERIC. https://eric.ed.gov/?id=ED380910
  3. [3] AICTE. (2018). Model curriculum for undergraduate degree courses in engineering & technology. New Delhi: All India council for technical education. https://www.scribd.com/document/674869107/Aicte-Eee-2018
  4. [4] Panda, S., & Mishra, P. (2026). A framework for applying generative AI in outcome based teaching learning systems. Engineering and technology journal, 11(2), 9099–9104. https://doi.org/10.47191/etj/v11i02.37
  5. [5] Bozkurt, A., & Sharma, R. C. (2023). Challenging the status quo and exploring the new boundaries in the age of algorithms: Reimagining the role of generative AI in distance education and online learning. Asian journal of distance education, 18(1), 1–8. https://doi.org/10.5281/zenodo.7755273
  6. [6] Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education--where are the educators? International journal of educational technology in higher education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
  7. [7] Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education promises and implications for teaching and learning. Center for Curriculum Redesign. https://discovery.ucl.ac.uk/id/eprint/10139722/
  8. [8] Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
  9. [9] Bittencourt, I. I., Chalco, G., Santos, J., Fernandes, S., Silva, J., Batista, N., … & Isotani, S. (2024). Positive artificial intelligence in education (P-AIED): A roadmap. International journal of artificial intelligence in education, 34(3), 732–792. https://psycnet.apa.org/doi/10.1007/s40593-023-00357-y
  10. [10] Phung, T. N., Do, D. C., Nguyen, T. T., Nguyen, V. S., Nguyen, T. V., & Le, D. N. (2026). An integrated framework for outcome based education and AI supported blended learning in curriculum redesign and intelligent training management. Discover computing, 29(1), 196. https://doi.org/10.1007/s10791-026-10088-y
  11. [11] Kim, T. K. (2024). Integrating learning analytics, AI, and STEM education: A comprehensive review. International journal of research in stem education, 6(2), 61–72. https://doi.org/10.33830/ijrse.v6i2.1745
  12. [12] Zhang, R., Zhou, J., Hai, T., Zhang, S., Iwendi, M., Biamba, C., & Anumbe, N. (2022). Quality assurance awareness in higher education in China: Big data challenges. Journal of cloud computing, 11(1), 56. https://doi.org/10.1186/s13677-022-00321-6?urlappend=%3Futm_source%3Dresearchgate.net%26utm_medium%3Darticle
  13. [13] Luckin, R., & Holmes, W. (2016). Intelligence unleashed: An argument for AI in education. UCL Knowledge Lab. https://discovery.ucl.ac.uk/id/eprint/1475756/
  14. [14] Nguyen, V. T. (2025). Artificial intelligence in education: A systematic review of ChatGPT applications in english as a foreign language teaching and learning. Journal of science and technology, 8(S2), 193–201. https://doi.org/10.55401/d9hxr235
  15. [15] Jeet, K. S., Qadir, S. S. & Afzal, B. S. (2025). Bridging the digital divide in indian education system: It’s progress and challenges in 21st century. International journal of recent advances in multidisciplinary topics, 6(11), 36–39. https://doi.org/10.65138/ijramt.2025.v6i11.3156
  16. [16] Rajaee, N., Junaidi, E., Taib, S. N. L., Salleh, S. F., & Munot, M. A. (2013). Issues and challenges in implementing outcome based education in engineering education. International journal for innovation education and research, 1(4), 1–9. https://doi.org/10.31686/ijier.vol1.iss4.121
  17. [17] Swamy, K. V., Aouthu, S., & others. (2025). Mapping engineering course outcomes to program outcomes using an intelligent framework. Journal of engineering education transformations, 38(2), 188–192. https://doi.org/10.16920/jeet/2025/v38is2/25023
  18. [18] Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives: Complete edition. Addison Wesley Longman, Inc. http://eduq.info/xmlui/handle/11515/18824
  19. [19] Ifenthaler, D., Mah, D. K., & Yau, J. Y. K. (2019). Utilising learning analytics for study success: Reflections on current empirical findings. In Utilizing learning analytics to support study success (pp. 27–36). Springer. https://doi.org/10.1007/978-3-319-64792-0_2
  20. [20] Gupta, N., & George, A. (2024). Digital personal data protection Act, 2023: Charting the future of India’s data regulation. In Data governance and the digital economy in Asia (pp. 34–53). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9781003505723-3/digital-personal-data-protection-act-2023-nidhi-gupta-ammu-george
  21. [21] NAAC. (2020). Manual for self-study report affiliated/constituent colleges. https://dcedu.in/wp-content/uploads/2017/08/ssr_naac.pdf

Downloads

Published

2026-03-12

Issue

Vol. 3 No. 1 (2026): OPT

Section

Articles

How to Cite

Panda, S. ., & Mishra, P. . (2026). Application of GenAI in Outcome-Based Education in Teaching Learning Processes. Optimality, 3(1), 19-27. https://doi.org/10.22105/opt.v3i1.106

Similar Articles

21-29 of 29

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)