ARTIFICIAL INTELLIGENCE AND THE FUTURE OF HIGHER EDUCATION: PROSPECTS, CHALLENGES, AND QUALITY ENHANCEMENT STRATEGIES
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Keywords

higher education
artificial intelligence
educational quality
teaching and learning
student outcomes
student engagement

How to Cite

LEKOTA, T., & AJANI, O. A. (2026). ARTIFICIAL INTELLIGENCE AND THE FUTURE OF HIGHER EDUCATION: PROSPECTS, CHALLENGES, AND QUALITY ENHANCEMENT STRATEGIES. Humanities and Social Sciences, 33(1), 85-99. https://doi.org/10.7862/rz.2026.hss.06

Abstract

This study reviews literature on the impact of artificial intelligence (AI), in relation to the quality of higher education. The synthesis of evidence across disciplines has enabled this study to examine whether AI enhances learning outcomes or poses a risk to academic standards. Literature found demonstrates that there are many potential advantages of using AI, including personalised learning, adaptive instruction, increased support for teachers, and efficient administration. In addition, the literature highlights potential challenges associated with the use of AI in higher education, such as data privacy, algorithmic bias, unequal access to technology, and the need for educator training in AI use. Therefore, understanding both the potential advantages and disadvantages of using AI in higher education will enable institutions to develop appropriate policies and procedures for managing AI in their respective contexts.

https://doi.org/10.7862/rz.2026.hss.06
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References

Alghamdi, A. (2022). Artificial intelligence in education as a means to achieve sustainable development in accordance with the pillars of the Kingdom’s Vision 2030: A systematic review. International Journal of Higher Education, 11(4), 80. https://doi.org/10.5430/ijhe.v11n4p80

Bearman, M., Ryan, J., Ajjawi, R. (2022). Discourses of artificial intelligence in higher education: A critical literature review. Higher Education, 86(2), 369–385. https://doi.org/10.1007/s10734-022-00937-2

Belur, J., Tompson, L., Thornton, A., Simon, M. (2021). Interrater reliability in systematic review methodology: Exploring variation in coder decision making. Sociological Methods and Research, 50(3), 837–865. https://doi.org/10.1177/0049124118799372

Bozkurt, A., Karadeniz, A., Baneres, D., Guerrero Roldán, A. E., Rodríguez, M. E. (2021). Artificial intelligence and reflections from educational landscape: A review of AI studies in half a century. Sustainability, 13(2), 800. https://doi.org/10.3390/su13020800

Chaka, C. (2023). Fourth Industrial Revolution: A review of applications, prospects, and challenges for artificial intelligence, robotics and blockchain in higher education. Research and Practice in Technology Enhanced Learning, 18, 2. https://doi.org/10.58459/rptel.2023.18002

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

Chu, H. C., Tu, Y. F., Yang, K. H. (2022). Roles and research trends of artificial intelligence in higher education: A systematic review of the top 50 most cited articles. Australasian Journal of Educational Technology, 38(3), 22–42. https://doi.org/10.14742/ajet.7526

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Deng, H., Jia, W., Chai, D. (2022). Discussion on innovative methods of higher teacher education and training based on new artificial intelligence. Security and Communication Networks, 2022, Article 3899413. https://doi.org/10.1155/2022/3899413

Garidzirai, R., Garidzirai, R. (2021). Together but not together: Challenges of remote learning for students amid the COVID 19 pandemic in rural South African universities. Research in Social Sciences and Technology, 6(3), 213–226. https://doi.org/10.46303/ressat.2021.39

Gough, D., Oliver, S., Thomas, J. (Eds.). (2017). An introduction to systematic reviews (2nd ed.). Sage.

Gupta, S., Chen, Y. (2022). Supporting inclusive learning using chatbots? A chatbot led interview study. Journal of Information Systems Education, 33(1), 98–108.

Hannan, E., Liu, S. (2021). AI: A new source of competitiveness in higher education. Competitiveness Review: An International Business Journal, 33(2), 265–279. https://doi.org/10.1108/CR-03-2021-0045

Kharbat, F. F., AlShawabkeh, A., Woolsey, M. L. (2020). Identifying gaps in using artificial intelligence to support students with intellectual disabilities from education and health perspectives. Aslib Journal of Information Management, 73(1), 101–128. https://doi.org/10.1108/AJIM-02-2020-0054

Kim, C., Bennekin, K. N. (2016). The effectiveness of volition support (VoS) in promoting students’ effort regulation and performance in an online mathematics course. Instructional Science, 44(4), 359–377. https://doi.org/10.1007/s11251-015-9366-5

Koç Januchta, M. M., Schönborn, K. J., Roehrig, C., Chaudhri, V. K., Tibell, L. A. E., Heller, C. (2022). Connecting concepts helps put main ideas together: Cognitive load and usability in learning biology with an AI enriched textbook. International Journal of Educational Technology in Higher Education, 19(1), 11. https://doi.org/10.1186/s41239-021-00317-3

Liang, J. C., Hwang, G. J., Chen, M. R. A., Darmawansah, D. (2021). Roles and research foci of artificial intelligence in language education: An integrated bibliographic analysis and systematic review approach. Interactive Learning Environments. https://doi.org/10.1080/10494820.2021.1958348

Lucena, F., Díaz, I., Reche, M., Rodríguez, J. (2019). Artificial intelligence in higher education: A bibliometric study on its impact in the scientific literature. Education Sciences, 9(1), 51. https://doi.org/10.3390/educsci9010051

Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L. A. (2015). Preferred reporting items for systematic review and meta analysis protocols (PRISMA P) 2015 statement. Systematic Reviews, 4(1), 1. https://doi.org/10.1186/2046-4053-4-1

Mospan, N., Melnychenko, O., Sisoieva, S. (2022). Emergency higher education digital transformation: Ukraine’s response to the COVID 19 pandemic. Information Technologies and Learning Tools, 89(3), 90–104. https://doi.org/10.33407/itlt.v89i3.4827

Ouatik, F., Fadli, H., El Gorari, A., Mohadab, M. E. L., Raoufi, M. (2021). E learning and decision making system for automating students’ assessment using remote laboratory and machine learning. Journal of E Learning and Knowledge Society, 17(1), 90–100. https://doi.org/10.20368/1971-8829/1135285

Ouyang, F., Zheng, L., Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893–7925. https://doi.org/10.1007/s10639-022-10925-9

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo Wilson, E., McDonald, S., Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71

Popenici, S. A. D., 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), 22. https://doi.org/10.1186/s41039-017-0062-8

Rutner, S. M., Scott, R. A. (2022). Use of artificial intelligence to grade student discussion boards: An exploratory study. Information Systems Education Journal, 20(4), 4–18.

Salas Pilco, S. Z., Yang, Y. (2022). Artificial intelligence applications in Latin American higher education: A systematic review. International Journal of Educational Technology in Higher Education, 19(1), 21. https://doi.org/10.1186/s41239-022-00326-w

Saldaña, J. (2021). The coding manual for qualitative researchers (4th ed.). Sage.

Shukla, A. K., Janmaijaya, M., Abraham, A., Muhuri, P. K. (2019). Engineering applications of artificial intelligence: A bibliometric analysis of 30 years (1988–2018). Engineering Applications of Artificial Intelligence, 85, 517–532. https://doi.org/10.1016/j.engappai.2019.06.010

Singh, S. V., Hiran, K. K. (2022). The impact of AI on teaching and learning in higher education technology. Journal of Higher Education Theory and Practice, 22(13). https://doi.org/10.33423/jhetp.v22i13.5514

Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers and Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008

Venkatesh, V., Morris, M. G., Davis, G. B., Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Winkler Schwartz, A., Bissonnette, V., Mirchi, N., Ponnudurai, N., Yilmaz, R., Ledwos, N., Siyar, S., Azarnoush, H., Karlik, B., Del Maestro, R. F. (2019). Artificial intelligence in medical education: Best practices using machine learning to assess surgical expertise in virtual reality simulation. Journal of Surgical Education, 76(6), 1681–1690. https://doi.org/10.1016/j.jsurg.2019.05.015

Yang, A. C. M., Chen, I. Y. L., Flanagan, B., Ogata, H. (2021). Automatic generation of cloze items for repeated testing to improve reading comprehension. Educational Technology and Society, 24(3), 147–158.

Yu, W. (2021). Artificial intelligence for the development of university education management. Frontiers in Educational Research, 4(1). https://doi.org/10.25236/fer.2021.040120

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

Zhang, J. (2023). Impact of artificial intelligence on higher education in the perspective of its transformation application. Lecture Notes in Education Psychology and Public Media, 2(1), 822–830. https://doi.org/10.54254/2753-7048/2/2022483

Zhang, Z., Xu, L. (2022). Student engagement with automated feedback on academic writing: A study on Uyghur ethnic minority students in China. Journal of Multilingual and Multicultural Development. https://doi.org/10.1080/01434632.2022.2102175

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