Students’ Perspectives on Integrating Generative AI Tools into Teaching, Learning, and Assessment in Higher Education: A Case of the Royal University of Bhutan
DOI:
https://doi.org/10.70232/jrep.v3i2.153Keywords:
AI Tools, Higher Education, Benefits, ChallengesAbstract
This study explored students’ perspectives on the benefits and challenges of integrating Generative AI (GenAI) tools in teaching, learning, and assessment within five colleges under the Royal University of Bhutan (RUB). The study is timely and relevant as educators and students in Bhutan increasingly adopt GenAI tools, reflecting a broader global trend. The study outlines three primary objectives. First, it seeks students’ perspectives regarding the integration of GenAI tools in teaching, learning, and assessment in higher education, highlighting the GenAI tools used, the benefits they offer, and the challenges they may impose. Second, the study aspires to provide practical recommendations for educators and academics in higher education institutions for the effective integration of GenAI tools. Finally, the study aims to provide recommendations to relevant stakeholders and policymakers for the adoption or expansion of AI initiatives within the precincts of RUB colleges. This study employed a qualitative approach as qualitative research allows for a deeper understanding of human behaviour and experiences. A purposive sampling technique was used to ensure that participants were selected based on their potential to provide valuable insights relevant to the research objectives. Data were collected through semi-structured focus group interviews, with each group comprising six members (three male and three female). A total of 180 students participated across 30 focus group interviews conducted in five colleges, with six focus group interviews in each college. The data collected were transcribed, coded, and categorised into themes for analysis. To maintain confidentiality, the researchers used a systematic labelling system for both focus groups and individual students during the data analysis process, enabling a comprehensive and structured interpretation of the findings. The findings revealed key benefits of GenAI, such as providing quick and accessible information, fostering personalised learning, and offering emotional support. However, the findings also revealed challenges in terms of the reliability of AI-generated content, the potential hindrance to creativity and critical thinking, and the risk of social and emotional disconnect between students and their learning communities due to overreliance on GenAI. The study therefore recommends raising awareness and improving understanding of GenAI tools among students and establishing comprehensive policy frameworks to promote their ethical use across RUB colleges while upholding high standards of academic quality.
References
Akinwalere, S., & Ivanov, V. (2022). Artificial intelligence in higher education: Challenges and opportunities. Border Crossing, 12(1), 1–15. https://doi.org/10.33182/bc.v12i1.2015
Al-Badi, A., Khan, A., & Eid-Alotaibi. (2022). Perceptions of learners and instructors towards artificial intelligence in personalized learning. Procedia Computer Science, 201, 445-451. https://doi.org/10.1016/j.procs.2022.03.058
Amdan, M. A. B., Janius, N., Saidin, M. S. B., & Kasdiah, M. A. H. B. (2025). Impact of artificial intelligence in TVET and STEM education among higher learning students in Malaysia. Journal of Research in Mathematics, Science, and Technology Education, 2(1), 1–14. https://doi.org/10.70232/jrmste.v2i1.15
Atlas, S. (2023). ChatGPT for higher education and professional development: A guide to conversational AI. University of Rhode Island. https://digitalcommons.uri.edu/cba_facpubs/548
Baidoo-Anu, D., Asamoah, D., Amoako, I., & Mahama, I. (2024). Exploring student perspectives on generative artificial intelligence in higher education learning. Discover Education, 3(1), 98. https://link.springer.com/article/10.1007/s44217-024-00173-z
Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education?. International Journal of Educational Technology in Higher Education, 17, 1-12. https://link.springer.com/article/10.1186/s41239-020-00218-x
Bonde, L. (2024). A conceptual design of a generative artificial intelligence system for education. International Journal of Research and Innovation in Applied Science, 9(4), 457-469. https://doi.org/10.51584/IJRIAS.2024.904034
Bonsu, E., & Baffour-Koduah, D. (2023). From the consumers’ side: Determining students’ perception and intention to use ChatGPT in Ghanaian higher education. Social Science Research Network. http://dx.doi.org/10.2139/ssrn.4387107
Brandtzaeg, P. B., Skjuve, M., & Følstad, A. (2022). My AI friend: How users of a social chatbot understand their human–AI friendship. Human Communication Research, 48(3), 404-429. https://doi.org/10.1093/hcr/hqac008
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., & Askell, A. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901. https://doi.org/10.48550/arXiv.2005.14165
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1), 1-25. https://doi.org/10.1186/s41239-023-00408- 3
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), Article 43. https://doi.org/10.1186/s41239-023-00411-8
Chan, C. K. Y., & Tsi, L. H. (2024). Will generative AI replace teachers in higher education? A study of teacher and student perceptions. Studies in Educational Evaluation, 83, 101395. https://doi.org/10.1016/j.stueduc.2024.101395
Chasokela, D. (2025). Harnessing artificial intelligence: Transformative technologies in contemporary higher education. Journal of Computers for Science and Mathematics Learning, 2(1), 26-37. https://doi.org/10.70232/jcsml.v2i1.15
Chen, F. (2022). Human-AI cooperation in education: human in loop and teaching as leadership. Journal of Educational Technology and Innovation, 2(1). https://doi.org/10.61414/jeti.v2i1.34
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
Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2023). Artificial intelligence (AI) student assistants in the classroom: Designing chatbots to support student success. Information Systems Frontiers, 25(1), 161-182. https://doi.org/10.1007/s10796-022-10291-4
Cohen, L., Manion, L., & Morrison, K. (2018). Research Methods in Education (8th ed.). Routledge.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative and mixed method approaches (5th ed.). Sage Publications.
De Freitas, J., Uguralp, A. K., Uguralp, Z. O., & Stefano, P. (2024). AI companions reduce loneliness (Working Paper No. 24-078). Harvard Business School. https://www.hbs.edu/ris/Publication%20Files/24-078_a3d2e2c7-eca1-4767-8543-122e818bf2e5.pdf
Denzin, N. K., & Lincoln, Y. S. (2018). The Sage Handbook of Qualitative Research. Sage Publications.
Essel, H. B., Vlachopoulos, D., Tachie-Menson, A., Johnson, E. E., & Baah, P. K. (2022). The impact of a virtual teaching assistant (chatbot) on students’ learning in Ghanaian higher education. International Journal of Educational Technology in Higher Education, 19(1), 57. https://doi.org/10.1186/s41239-022-00362-6
Firat, M. (2023). What ChatGPT means for universities: Perceptions of scholars and students. Journal of Applied Learning and Teaching, 6(1), 1-22. https://doi.org/10.37074/jalt.2023.6.1.22
Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681–694. https://doi.org/10.1007/s11023-020-09548-1
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707. https://doi.org/10.1007/s11023-018-9482-5
Francis, N. J., Jones, S., & Smith, D. P. (2025). Generative AI in higher education: Balancing innovation and integrity. British Journal of Biomedical Science, 81, 14048. https://doi.org/10.1080/09674845.2025.14048
Ghotbi, N., Ho, M. T., & Mantello, P. (2022). Attitude of college students towards ethical issues of artificial intelligence in an international university in Japan. AI & Society, 37, 283–290. https://doi.org/10.1007/s00146-021-01168-2
Gillissen, A., Kochanek, T., Zupanic, M., & Ehlers, J. (2022). Medical students’ perceptions towards digitization and artificial intelligence: A mixed-methods study. Healthcare, 10(4), 723. https://doi.org/10.3390/healthcare10040723
Gozalo-Brizuela, R & Garrido-Merchan, E.C (2023). ChatGPT is not all you need. A state of the art review of large generative AI model. Cornell University. https://doi.org/10.48550/arXiv.2301.04655
Hao, Z., Miao, E., & Yan, M. (2021,December). Research on School Principals’ Willingness to Adopt Artificial Intelligence Education and Related Influencing Factors. In 2021 Tenth International Conference of Educational Innovation Through Technology (pp. 356-361). IEEE. https://doi.org/10.1109/EITT53287.2021.00076
Ho, A., Hancock, J., & Miner, A. S. (2018). Psychological, relational, and emotional effects of self-disclosure after conversations with a chatbot. The Journal of Communication, 68(4), 712–733. https://doi.org/10.1093/joc/jqy026
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., & Koedinger, K. R. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32, 504–526. https://doi.org/10.1007/s40593-021-00239-1
Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172. https://doi.org/10.1177/1094670517752
Ifenthaler, D., Majumdar, R., Gorissen, P., & Kuhn, J. (2024). Artificial intelligence in education: Implications for policymakers, researchers, and practitioners. Technology, Knowledge and Learning, 29, 1693–1710. https://doi.org/10.1007/s10758-024-09747-0
Iorliam, A., & Ingio, J. A. (2024). A comparative analysis of generative artificial intelligence tools for natural language processing. Journal of Computing Theories and Applications, 1(3), 311–325. https://doi.org/10.62411/jcta.9447
Khawaja, Z., & Bélisle-Pipon, J. C. (2023). Your robot therapist is not your therapist: Understanding the role of AI-powered mental health chatbots. Frontiers in Digital Health, 5, 1278186. https://doi.org/10.3389/fdgth.2023.1278186
Kim, J., Kang, S., & Bae, J. (2022). Human likeness and attachment effect on the perceived interactivity of AI speakers. Journal of Business Research, 144, 797-804. https://doi.org/10.1016/j.jbusres.2022.02.047
Kirschner, P. A., & Hendrick, C. (2020). How learning happens: Seminal works in educational psychology and what they mean in practice. Routledge.
Kong, S. C., Zhang, G., & Cheung, M. Y. (2022). Pedagogical delivery and feedback for an artificial intelligence literacy programme for university students with diverse academic backgrounds: Flipped classroom learning approach with project-based learning. Bulletin of the Technical Committee on Learning Technology, 22(1), 8-14. Retrieved from https://ieeecs-media.computer.org/tc-media/sites/5/2021/10/25204858/bulletin-tclt-2022-0101016.pdf
Kuleto, V., Mihoreanu, L., Dinu, D. G., Ilić, M. P., & Păun, D. (2022). Artificial intelligence, machine learning and extended reality: potential problem solvers for higher education issues. In Augmented Reality and Artificial Intelligence: The Fusion of Advanced Technologies (pp. 123-136). Springer Nature Switzerland.
Kumar, A. H. S. (2023). Analysis of ChatGPT tool to assess the potential of its utility for academic writing in the biomedical domain. BEMS Reports, 9(1), 24–30. https://doi.org/10.5530/bems.9.1.5
Kumar, R., & McGray, R. (2024). Global trends in generative AI adoption: A quantitative survey of postsecondary students. https://doi.org/10.21203/rs.3.rs-4449928/v1
Liew Xiu Jie, A., & Kamrozzaman, N. A. (2024). The challenges higher education students face in using artificial intelligence (AI) against their learning experiences. Open Journal of Social Sciences, 12(10). https://doi.org/10.4236/jss.2024.1210025
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2). https://doi.org/10.1016/j.ijme.2023.100790
Lin, H. H., & Chang, Y. S. (2020). The impact of learning motivation and learning interest on students’ academic achievement. International Journal of Educational Research, 8(2), 45–54.
Liu, Y., Yang, Z., Yu, Z., Liu, Z., Liu, D., Lin, H.,... & Shi, S. (2023). Generative artificial intelligence and its applications in materials science: Current situation and future perspectives. Journal of Materiomics, 9(4), 798-816. https://doi.org/10.1016/j.jmat.2023.05.001
Lubowitz, J. H. (2023). ChatGPT, An artificial intelligence chatbot, is impacting medical literature. Arthroscopy: The Journal of Arthroscopic & Related Surgery, 39(5), 1121-1122. https://doi.org/10.1016/j.arthro.2023.01.015
Lubowitz, J. H. (2023). Guidelines for the use of generative artificial intelligence tools for biomedical journal authors and reviewers. Arthroscopy: The Journal of Arthroscopic & Related Surgery, 40(3), 651-652. https://doi.org/10.1016/j.arthro.2023.10.037
Markauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez-Maldonado, R., Howard, S., & Siemens, G. (2022). Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI?. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100056
Marzuki, Widiati, U., Rusdin, D., Darwin, & Indrawati, I. (2023). The impact of AI writing tools on the content and organization of students’ writing: EFL teachers’ perspective. Cogent Education, 10(2), 2236469. https://doi.org/10.1080/2331186X.2023.2236469
Merriam, S. B. (2009). Qualitative research: A guide to design and implementation. Jossey-Bass.
Ministry of Education (2019). iSherig-2 Education ICT Master Plan 2019-2023. Ministry of Education.
Mogavi, R. H., Deng, C., Kim, J. J., Zhou, P., Kwon, Y. D., Metwally, A. H., Tlili, A., Bassanelli, S., Bucchiarone, A., Gujar, S., Nacke, L. E., & Hui, P. (2023). Exploring user perspectives on ChatGPT: Applications, perceptions, and implications for AI-integrated education. ArXiv. https://arxiv.org/abs/2305.13114
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w
Nguyen, K. V. (2025). The use of generative AI tools in higher education: Ethical and pedagogical principles. Journal of Academic Ethics, 23(3), 1435–1455. https://doi.org/10.1007/s10805-025-09425-x
Ooi, K. B., Tan, G. W. H., Al-Emran, M., Al-Sharafi, M. A., Capatina, A., Chakraborty, A., … Wong, L. W. (2025). The potential of generative artificial intelligence across disciplines: perspectives and future directions. Journal of Computer Information Systems, 65(1), 76–107. https://doi.org/10.1080/08874417.2023.2261010
Pelau, C., Dabija, D.-C., & Stanescu, M. (2024). Can I trust my AI friend? The role of emotions, feelings of friendship and trust for consumers’ information-sharing behavior toward AI. Oeconomia Copernicana, 15(2), 407–433. https://doi.org/10.24136/oc.2916
Peres, R., Schreier, M., Schweidel, D.,Soresu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice? International Journal of Research in Marketing, 40(2), 269-275. https://doi.org/10.1016/j.ijresmar.2023.03.001
Polyportis, A. (2024). A longitudinal study on artificial intelligence adoption: understanding the drivers of ChatGPT usage behavior change in higher education. Frontiers in Artificial Intelligence, 6, 1324398. https://doi.org/10.3389/frai.2023.1324398
Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1). https://doi.org/10.1186/s41039-017-0062-8
Popenici, S., Rudolph, J., Tan, S., & Tan, S. (2023). A critical perspective on generative AI and learning futures.: An interview with Stefan Popenici. Journal of Applied Learning and Teaching, 6(2), 311-331. https://doi.org/10.37074/jalt.2023.6.2.5
Pradhan, D. (2023, August 7). Navigating the future; Bhutan’s balancing act with AI and Tradition. Bhutan Broadcasting Service. https://www.bbs.bt/news/?p=190119
Ray, S. & Sikdar, D. P. (2024). AI-driven flipped classroom: Revolutionizing education through digital pedagogy. British Journal of Education Learning and Development Psychology, 7(2), 169-179. https://doi.org/10.52589/BJELDP-LTDJFLIH
Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3
Royal Civil Service Commission. (2019). His Majesty The King Granted an Audience to the Graduates of the Royal Institute of Management on the eve of their Convocation Ceremony. Retrieved from https://www.rcsc.gov.bt/wp-content/uploads/2019/08/kings-address.pdf
Samtse College of Education. (2022, December 12). Graduation ceremony for students. https://www.sce.edu.bt/?p=14389
Sevnarayan, K., & Potter, M. A. (2024). Generative Artificial Intelligence in distance education: Transformations, challenges, and impact on academic integrity and student voice. Journal of Applied Learning and Teaching, 7(1). https://doi.org/10.37074/jalt.2024.7.1.41
Shin, D., Park, S., Kim, E. H., Kim, S., Seo, J., & Hong, H. (2022). Exploring the effects of AI-assisted emotional support processes in online mental health communities. In CHI Conference on Human Factors in Computing Systems Extended Abstracts (CHI ‘22 Extended Abstracts) (pp. 1-7). ACM. https://doi.org/10.1145/3491101.3519854
Slimi, Z., & Carballido, B. V. (2023). Navigating the ethical challenges of artificial intelligence in higher education: An analysis of seven global AI ethics policies. TEM Journal, 12(2). https://doi.org/10.18421/TEM122-02
Sousa, A. E., & Cardoso, P. (2025). Use of generative AI by higher education students. Electronics, 14(7), 1258. https://doi.org/10.3390/electronics14071258
Subirats, L., Corral, A. P., & Fort, S. (2023). Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence. PLOS ONE, 18(2), e0282306. https://doi.org/10.1371/journal.pone.0282306
Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning & Teaching, 6(1), 1-10. https://doi.org/10.37074/jalt.2023.6.1.17
Sumakul, D. T. Y., Hamied, F. A., & Sukyadi, D. (2022, February). Students’ perceptions of the use of AI in a writing class. In 67th TEFLIN International Virtual Conference & the 9th ICOELT 2021 (TEFLIN ICOELT 2021) (52-57). Atlantis Press. https://doi.org/10.2991/assehr.k.220201.009
Temper, M., Tjoa, S., & David, L. (2025). Higher Education Act for AI (HEAT-AI): A framework to regulate the usage of AI in higher education institutions. Frontiers in Education, 10, 1505370. https://doi.org/10.3389/feduc.2025.1505370
Warschauer, M., Tseng, W., Yim, S., Webster, T., Jacob, S., Du, Q., & Tate, T. (2023). The affordances and contradictions of AI-generated text for writers of English as a second or foreign language. Journal of Second Language Writing, 62. https://doi.org/10.2139/ssrn.4404380
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995
Williamson, B., Eynon, R., Knox, J., & Davies, H. (2023). Critical perspectives on AI in education: Political economy, discrimination, commercialization, governance, and ethics. In K. Gulson, J. Komljenovic, & B. Williamson (Eds.), Handbook of Artificial Intelligence in Education (pp. 553-570). Edward Elgar Publishing.
Xia, Q., Chiu, T. K., Lee, M., Sanusi, I. T., Dai, Y., & Chai, C. S. (2022). A self-determination theory (SDT) design approach for inclusive and diverse artificial intelligence (AI) education. Computers & Education, 189, https://doi.org/10.1016/j.compedu.2022.104582
Yang, S. J. (2021). Precision education-A new challenge for AI in education. Journal of Educational Technology & Society, 24(1). http://index.j-ets.net/Published/24_1/ETS_24_1_08.pdf
Yang, S., & Bai, H. (2020). The integration design of artificial intelligence and normal students’ education. In Journal of Physics: Conference Series (Vol. 1453, No. 1, p. 012090). IOP Publishing. https://doi.org/10.1088/1742-6596/1453/1/012090
Yin, R. K. (2011). Qualitative research from start to finish. The Guilford Press.
Yin, Y., Jia, N., & Wakslak, C. J. (2024). AI can help people feel heard, but an AI label diminishes this impact. Proceedings of the National Academy of Sciences, 121(14). https://doi.org/10.1073/pnas.231911212
Young, J., Jawara, L. M., Nguyen, D. N., Daly, B., Huh-Yoo, J., & Razi, A. (2024). The role of AI in peer support for young people: A study of preferences for human- and AI-generated responses. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ‘24) (pp. 1-18). ACM. https://doi.org/10.1145/3613904.3642574
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Tshering Om Tamang, Sonam Daker, Bhupen Gurung, Ugyen Dorji
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
