Development of a Conceptual Framework for Artificial Intelligence in User Experience-Based Digital Learning Systems at Indonesian Universities

Rina Firmansyah; Agus Setiawan; Dian Hakim

Authors

Rina Firmansyah Universitas Negeri Malang Author
Agus Setiawan Universitas Negeri Malang Author
Dian Hakim Universitas Negeri Jakarta Author

Keywords:

Artificial intelligence, User experience, Digital learning, Higher education, AI literacy, Human-centered AI

Abstract

This study aims to develop a conceptual framework for artificial intelligence in user experience-based digital learning systems at Indonesian universities. The study responds to the growing use of AI in higher education, where students and lecturers increasingly rely on AI tools for learning support, academic writing, content exploration, and digital interaction, while institutional policies and user-centered system design remain limited. This research used a qualitative approach with an exploratory case study design. Data were collected through semi-structured interviews, limited observation, and documentation involving students, lecturers, LMS administrators, information technology staff, and academic policymakers selected through purposive sampling. The findings reveal six main themes: AI as a tool for personalized and self-directed learning, UX as a determinant of AI acceptance, trust and transparency in AI-supported learning, AI literacy as a core academic requirement, institutional readiness and digital governance, and the need for a UX-based AI conceptual framework. These findings show that meaningful AI integration requires more than technical adoption. It requires usable systems, ethical guidance, human control, institutional support, and critical AI literacy. This study contributes to human-centered AI and digital learning literature by offering a conceptual understanding of AI integration based on user experience in Indonesian higher education. The study recommends that universities develop responsible AI policies, integrate AI features into LMS platforms, and provide AI literacy training for lecturers and students. Further research may test the framework across different institutional contexts.

References

Ahmad, M., & Wilkins, S. (2025). Purposive sampling in qualitative research: A framework for the entire journey. Quality & Quantity, 59, 1461–1479. https://doi.org/10.1007/s11135-024-02022-5

Ahmed, S. K. (2024). The pillars of trustworthiness in qualitative research. Journal of Medicine, Surgery, and Public Health, 2, Article 100051. https://doi.org/10.1016/j.glmedi.2024.100051

Ahmed, S. K., Mohammed, R. A., Nashwan, A. J., Ibrahim, R. H., Abdalla, A. Q., Mizyed, O., & Khidhir, R. M. (2025). Using thematic analysis in qualitative research. Journal of Medicine, Surgery, and Public Health, 6, Article 100198. https://doi.org/10.1016/j.glmedi.2025.100198

Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating e-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/j.chb.2019.08.004

Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the e-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25, 5261–5280. https://doi.org/10.1007/s10639-020-10219-y

Braun, V., & Clarke, V. (2023). Toward good practice in thematic analysis: Avoiding common problems and be(com)ing a knowing researcher. International Journal of Transgender Health, 24(1), 1–6. https://doi.org/10.1080/26895269.2022.2129597

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, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, Article 100002. https://doi.org/10.1016/j.caeai.2020.100002

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, Article 22. https://doi.org/10.1186/s41239-023-00392-8

Darmono, Setiawan, R. J., & Ma’ruf, K. (2025). Determining factors influencing Indonesian higher education students’ intention to adopt artificial intelligence tools for self-directed learning management. European Journal of Educational Research, 14(3), 805–828. https://doi.org/10.12973/eu-jer.14.3.805

Hamid, S. M., Mannong, A. B. M., Saiful, S., & Irawan, N. (2025). Integrating artificial intelligence (AI) into EFL in higher education: Challenges and opportunities for Indonesian teachers and students. AL-ISHLAH: Jurnal Pendidikan, 17(3). https://doi.org/10.35445/alishlah.v17i3.7717

Junnier, F. (2024). Action and understanding in the semi-structured research interview: Using CA to analyse European research scientists’ attitudes to linguistic disadvantage. Journal of English for Academic Purposes, 69, Article 101355. https://doi.org/10.1016/j.jeap.2024.101355

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274

Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M. D., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), Article 10424. https://doi.org/10.3390/su131810424

Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3, Article 100101. https://doi.org/10.1016/j.caeai.2022.100101

Lim, W. M. (2025). What is qualitative research? An overview and guidelines. Australasian Marketing Journal, 33(2), 199–229. https://doi.org/10.1177/14413582241264619

Markauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez-Maldonado, R., Howard, S., Tondeur, J., de Laat, M., Buckingham Shum, S., Gašević, D., & 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, Article 100056. https://doi.org/10.1016/j.caeai.2022.100056

Maslov, I., Nikou, S., & Hansen, P. (2021). Exploring user experience of learning management system. The International Journal of Information and Learning Technology, 38(4), 344–363. https://doi.org/10.1108/IJILT-03-2021-0046

McKim, C. (2023). Meaningful member-checking: A structured approach to member-checking. American Journal of Qualitative Research, 7(2), 41–52. https://doi.org/10.29333/ajqr/12973

Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, Article 100041. https://doi.org/10.1016/j.caeai.2021.100041

Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, Article 100020. https://doi.org/10.1016/j.caeai.2021.100020

Ouyang, F., & Zhang, L. (2024). AI-driven learning analytics applications and tools in computer-supported collaborative learning: A systematic review. Educational Research Review, 44, Article 100616. https://doi.org/10.1016/j.edurev.2024.100616

Pisica, A. I., Edu, T., Zaharia, R. M., & Zaharia, R. (2023). Implementing artificial intelligence in higher education: Pros and cons from the perspectives of academics. Societies, 13(5), Article 118. https://doi.org/10.3390/soc13050118

Rahiem, M. D. H. (2020). The emergency remote learning experience of university students in Indonesia amidst the COVID-19 crisis. International Journal of Learning, Teaching and Educational Research, 19(6), 1–26. https://doi.org/10.26803/ijlter.19.6.1

Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., Iahad, N. A., Abdulla, A. A., Akopyan, A., Segura, M. W. A., & AlHumaid, J. (2025). Higher education students’ perceptions of ChatGPT: A global study of early reactions. PLOS ONE, 20(2), Article e0315011. https://doi.org/10.1371/journal.pone.0315011

Schlunegger, M. C., Zumstein-Shaha, M., & Palm, R. (2024). Methodologic and data-analysis triangulation in case studies: A scoping review. Western Journal of Nursing Research, 46(8), 611–622. https://doi.org/10.1177/01939459241263011

Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction, 36(6), 495–504. https://doi.org/10.1080/10447318.2020.1741118

Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, Article 124167. https://doi.org/10.1016/j.eswa.2024.124167

Wicaksana, A. D., Juliansyah, R., Lubis, M. A., Fami, A., & Wahyoedi, B. (2024). Measuring the user experience of LMS CLASS-IPB using the User Experience Questionnaire method. Journal of Information Systems and Informatics, 6(2), 851–864. https://doi.org/10.51519/journalisi.v6i2.731

Yadav, D. (2022). Criteria for good qualitative research: A comprehensive review. The Asia-Pacific Education Researcher, 31(6), 679–689. https://doi.org/10.1007/s40299-021-00619-0