Leveraging artificial intelligence to enhance teaching and learning in higher education: Promoting quality education and critical engagement
Open accessAbstract
This scoping review explores the role of Artificial Intelligence [AI] in enhancing teaching and learning in higher education, focusing on improving quality education and encouraging critical engagement. A thorough screening process led to the selection of 64 relevant, high-quality studies. Data from each article, including research goals, methods, findings, and ethical considerations, were systematically analysed to provide a well-rounded understanding of AI's impact on education. The review covers research from 2010 to 2024, examining how AI technologies like machine learning, natural language processing, and adaptive learning systems are being used in education. It highlights key benefits, such as personalised learning, better teaching strategies, and more efficient administrative processes. However, it also tackles challenges like data privacy, ethical concerns, and the digital divide. Using the Technology Acceptance Model and Self-Directed Learning theory as frameworks, the review looks at what influences the adoption and effectiveness of AI in higher education. While AI has the potential to significantly improve educational quality by providing tailored learning and fostering critical thinking, its success relies on overcoming ethical issues, ensuring fair access, and boosting digital literacy for both educators and students. The study emphasises the need for collaboration between educators, policymakers, and tech developers to make the most of AI’s potential while safeguarding the rights of all involved. It also offers recommendations for future research and practical steps to ensure AI is used responsibly and effectively in higher education.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 30
- Ally, M., Grimus, M., & Ebner, M. (2019). Preparing for the digital university: a review of the history and current state of distance, blended, and online learning. Journal of Computing in Higher Education, 31(3), 548- 567. https://doi.org/10.1007/s12528-019-09208-w · DOI
- Asio, J.M.R. (2024). Artificial intelligence (AI) literacy and academic performance of tertiary level students: a preliminary analysis. Social Sciences, Humanities and Education Journal (SHE Journal), 5(2), 309 -321.
- Baker, R., & Smith, L. (2019). Enhancing learning through personalized learning systems. AI and Education, 2(1), 15-28.
- Bozkurt, A., & Sharma, R. (2020). Bridging the digital divide in education. Educational Technology Research and Development, 68(4), 1439-1456. https://doi.org/10.1007/s11423-020-09744-3 · DOI
- Brown, T., & Green, D. (2020). The impact of artificial intelligence on assessments in higher education. Journal of Educational Technology Systems, 48(2), 222-241. https://doi.org/10.1177/0047239520920799 · DOI
- Florida, R., Mellander, C., & King, K. (2018). The role of metros in the global AI revolution. Economic Geography, 94(5), 589-607. https://doi.org/10.1080/00130095.2018.1444982. · DOI
- Gupta, S., & Chen, H. (2022). The role of AI-driven platforms in fostering student engagement. Journal of Interactive Learning Environments, 30(1), 142-158. https://doi.org/10.1080/10494820.2020.1789674. · DOI
- Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. EDUCAUSE Review.
- Hossain, M. S., Alamri, A., & Al-Rubaish, A. M. (2022). Use of AI-driven chatbots in higher education: A case study. Education and Information Technologies, 27(1), 497-515. https://doi.org/10.1007/s10639-021-10592-4. · DOI
- Hossain, M. S., Alkhalaf, S., & Van-Hoof, H. B. (2022). Understanding the adoption of smart educational technologies: E-learning in Saudi Arabia. Journal of Computer Assisted Learning, 38(4), 1022-1033. https://doi.org/10.1111/jcal.12567. · DOI
- Ifenthaler, D., & Schumacher, C. (2023a). AI in education: Challenges to pedagogy and learning. Educational Technology Research and Development, 71(1), 5-23. https://doi.org/10.1007/s11423-022-10010-x. · DOI
- Ifenthaler, D., & Schumacher, C. (2023b). Perceptions of AI in educational scenarios. Journal of Research on Technology in Education, 55(1), 1-17.
- Johnson, L., & Kumar, S. (2019). AI-driven analytics in higher education administration. Journal of Educational Administration and History, 51(1), 22-33. https://doi.org/10.1080/00220620.2018.1448153. · DOI
- Knowles, M. S., Holton III, E. F., & Swanson, R. A. (2014). The adult learner: The definitive classic in adult education and human resource development. Elsevier.
- Maphalala, M. C., & Ajani, O. A. (2024). Teaching and learning of computer science in higher education: a self-directed learning perspective. In C. Bosch, L. Goosen, & J. Chetty (Eds.), Navigating computer science education in the 21st century (pp. 89-111). IGI Global. https://doi.org/10.4018/979-8-3693-1066-3. · DOI
- ch005 · DOI
- Nguyen, T., & Walker, A. D. (2020). A review of the use of intelligent tutoring systems in education. Studies in Educational Evaluation, 66, 100849. https://doi.org/10.1016/j.stueduc.2020.100849. · DOI
- Okonkwo, C. G., & Ade-Ibijola, A. (2021). AI in higher education administration: A revolution in governance. Education and Management Engineering, 11(1), 16-24.
- 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., McGuinness, L. A.,Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P. & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj. · DOI · Paper page
- n71 · DOI · Paper page
- Patel, S., & Jain, P. (2019). Understanding educational technology through AI. Educational Technology & Society, 22(3), 47-59.
- Ramachandran, R., Zhang, X., & Kumar, P. (2023a). Adaptive learning: The future of education. Educational Research Review, 31, 100364. https://doi.org/10.1016/j.edurev.2020.100364. · DOI
- Ramachandran, R., Zhang, X., & Kumar, P. (2023b). The impact of intelligent tutoring systems on learning outcomes. Computers & Education, 160, 104092. https://doi.org/10.1016/j.compedu.2020.104092. · DOI
- Ramachandran, R., Zhang, X., & Kumar, P. (2023c). Addressing ethical concerns in the use of AI in education. Educational Technology Research and Development, 71(2), 531-548. https://doi.org/10.1007/s11423-021-10029-8. · DOI
- Seymour, W., Smith, G., & Ajani, T. A. (2020). Bridging the gap: Ensuring access to AI technologies. Journal of Computer Assisted Learning, 36(5), 654-665. https://doi.org/10.1111/jcal.12456. · DOI
- Sharma, P., Lee, H., & Gupta, A. (2020). Intelligent tutoring systems using artificial intelligence: A review. International Journal of Learning Technology, 15(2), 116-140.
- Siemens, G., & Gasevic, D. (2017). Artificial intelligence in education: Ethical considerations. Educational Technology Research and Development, 65(4), 841-850. https://doi.org/10.1007/s11423-017-9509-0. · DOI
- Smith, L., & Jones, F. (2018). Using AI to streamline educational administration. Journal of Educational Administration, 56(2), 177-192. https://doi.org/10.1108/JEA-03-2017-0028. · DOI
- Tang, S., & Sivanathan, A. (2021). Adaptive learning technologies in higher education: A comparative review. Educational Technology & Society, 24(1), 12-28.
- Williams, D., & Roberts, T. (2022). Artificial intelligence in higher education: Comprehensive insights and future directions. British Journal of Educational Technology, 53(4), 675-690. https://doi.org/10.1111/bjet.13159. · DOI
Cited by
Sorted by citations
Šević, N. P., Šević, A., Slijepčević, M., & Krstić, J. (2025) · Online Journal of Communication and Media Technologies · doi:10.30935/ojcmt/17246 · Cited by 20 · see citing works
The emergence of artificial intelligence in the higher education
Ajani, O. A., Akintolu, M., & Afolabi, S. (2024) · International Journal of Research in Business and Social Science (2147-4478) · doi:10.20525/ijrbs.v13i8.3507 · Cited by 23 · see citing works
Self-citationExploring the impact of AI on employee self-competence performance key variables and outcomes
Amayreh, A., Ta’Amnha, M. A., Magableh, I. K., Al-Mahrouq, M. H., & Alfaiza, S. A. (2025) · Discover Sustainability · doi:10.1007/s43621-025-01438-9 · Cited by 11 · see citing works
“The instrument of individual competency performance was adapted from [11].”
Enablers, Barriers, and Pedagogical Strategies in Diverse Learning Environments
Ajani, O. A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1000-8.ch001 · Cited by 7 · see citing works
Self-citationLeveraging Existing Trained AI Models for Enhanced Interview Preparation
Gupta, S. (2025) · Journal of Computer Science and Technology Studies · doi:10.32996/jcsts.2025.7.7.52 · Cited by 5 · see citing works
Al-Sowaidi, B., & Clarke, A. (2025) · Education and human development · doi:10.5772/intechopen.1012099 · Cited by 4 · see citing works
Lim, T. P., & Teh, W. C. (2026) · Discover Education · doi:10.1007/s44217-026-01219-0 · Cited by 9 · see citing works
Ullah, R. S., Asghar, A., Ali, M. I., & Jan, R. (2024) · Review of Education Administration and Law · doi:10.47067/real.v7i4.393 · Cited by 3 · see citing works
Teachers perspectives on artificial intelligence applications in Turkish language teaching
SARITİKEN, H. (2024) · Journal of Pedagogical Sociology and Psychology · doi:10.33902/jpsp.202433205 · Cited by 2 · see citing works
AI and the Future of Higher Education: A Guide for Policymakers
Shirazi, B. N., & Safavi, A. (2025) · doi:10.1109/icelet66022.2025.11025372 · Cited by 2 · see citing works
Kushwah, S., Khaldi, Z., Ganich, S. E., Fadilah, E. R., Ravichandra, B. V., & Attri, R. K. (2025) · International Conference on Science Technology Engineering and Mathematics (ICONSTEM) · doi:10.1109/iconstem65670.2025.11374559 · Cited by 2 · see citing works
“It highlights such significant benefits as personalised learning, the better teaching practices and more efficient administrative processes [5].”
RetractedGiam, N. M. (2026) · Journal of Artificial Intelligence and Technological Development · doi:10.59324/jaitd.2026.2(3).01 · Cited by 2 · see citing works
Alshehri, O. A. O., Lamouchi, A., Abdellatif, M. S., Al-Dosari, M. N. A., Mekheimer, M., & Nemt-allah, M. A. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1830103 · Cited by 2 · see citing works
“AI-critical pedagogic approaches have proposed specific instructional strategies—counter-prompting, error detection tasks, and reflective questioning—to embed critical engagement with AI outputs as a habitual classroom practice (Ajani et al., 2025; Alqarni, 2024; Kassenkhan et al., 2025).”
Mayasari, E. D., Rofiah, K., & Irvan, M. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-95627-0_21 · Cited by 1 · see citing works
Rajamanickam, L., Riskhan, B., Tshering, U., et al. (2025) · doi:10.1109/icmctc62214.2025.11196713 · Cited by 1 · see citing works
“Similarly, [3] identify faculty resistance as a critical barrier to technology adoption.”
InfluentialAI in Universities: The Good, the Bot, and the Ugly TruthsPreprint
Adewusi, M. A., Ainebyoona, C., Odekeye, O. T., & Ngoma, M. (2025) · Research Square · doi:10.21203/rs.3.rs-6883202/v1 · Cited by 4 · see citing works
Generative Artificial Intelligence Practices for Doctoral Student Engagement
Orr, L., Burton, K., Blakely, J., & Harrison, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5092-9.ch003
Artificial Intelligence in Academic Research
Han, P.-C. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2397-8.ch015 · Cited by 1 · see citing works
Educational Use of Artificial Intelligence in Doctor of Health Sciences Programs
Zipp, G., Gunther, C. B., Kuperman, D., Moore, J., & Rainchuso, L. (2025) · Journal of Innovation in Health Sciences Education · doi:10.46409/003.ossd3735
Yakubu, I., & Nuru, R. A. (2025) · Faculty of Natural and Applied Sciences Journal of Mathematics and Science Education · doi:10.63561/fnas-jmse.v6i3.901
“As a result, institutional operations are hampered, resource utilization is suboptimal, and key stakeholders—including students and staff—experience reduced service quality (Ajani, Gamede & Matiyenga, 2025).”
Transforming Curriculum Design with AI Learning Analytics in the University Context
Eskandarzadeh, A., & Gogia, Y. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7729-2.ch004
Matiyenga, T. C., & Khoalenyane, N. B. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-3872-9.ch004
Self-citationMahdi, R., Keykha, A., Kaliisa, R., & Darabi, F. (2025) · Journal of Medical Education Development · doi:10.61882/edcj.18.4.119
Martis, R. R., Pai, M. S., Mahadeva, R., Mathias, E. G., Ramoo, V., Dewi, Y. S., Arulappan, J., Nagasampige, M. H., & R, R. U. (2025) · Pain Management Nursing · doi:10.1016/j.pmn.2025.11.025 · Cited by 1 · see citing works
Enhancing Student Engagement With AI
Asrifan, A., Rismawati, R., Sulaiman, I., Abdullah, A., & Noni, N. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3000-6.ch007
Esmaeilian, B., Kushagra, K., Honey, U., et al. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7416-1.ch003
Velasco-Gómez, A. (2026) · Journal of Hunan University Natural Sciences · doi:10.55463/issn.1674-2974.53.3.4
Jimoh, I. B., ElAlfy, A., Al-Kwifi, O. S., et al. (2026) · Journal of Knowledge Management · doi:10.1108/jkm-11-2025-1684
Generative AI for Inclusive Learning and Emotional Well-Being
Atef, R. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6571-8.ch008
Impact of AI-augmented pedagogy on education quality at Hail University
Alshaie, F. S., Ahmed, H. M. S., Rashed, R. Q. G., Al-Refaei, A. A.-A., & Babiker, A. A. M. (2026) · International Journal of ADVANCED AND APPLIED SCIENCES · doi:10.21833/ijaas.2026.06.008
Mean, V. (2026) · Interdisciplinary Educational Technology · doi:10.71176/interedtech/18931
“Consequently, many higher education institutions have adopted AI not only for teaching and learning but also for administrative efficiency (Ajani et al., 2025).”
From Restriction to Empowerment: Responsible AI Governance in Higher Education
Akib, A. H. B. S. M., Intisar, M. A., Ahmed, M. S., et al. (2026) · ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) · doi:10.1145/3811242.3819102
Algorithmic Jurisprudence and Humanistic Governance Architectures for AI in Pedagogical Ecologies
Alam, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5213-8.ch021
Morris, G. R. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-7872-5.ch001
A Systematic Review of the Areas of Classroom Management and Curriculum Enhancement.
Sohail, T. B., Nazir, F., Sabahat, N., Zaib, M., & Qamar, N. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-3663-1.ch003
Vanhée, L., Boström, E., Lithner, J., Andersson, C., Palmberg, B., Winberg, M., Svahn, O., & Palm, T. (2026) · IEEE Access · doi:10.1109/access.2026.3733324
“…political, and cultural dimensions through improving the effectiveness of education in achieving high-quality learning and subsequent outcomes [3], [138], increasing the accessibility of learning to broader populations (e.g., by alleviating personal, linguistic, geographical, cultural, and…”
Isaee, H., Barjesteh, H., & Vijayaratnam, P. (2026) · Discover Education · doi:10.1007/s44217-026-02151-z
“In second language acquisition (SLA), PP has encouraged researchers to move away from deficit-oriented perspectives centered mainly on anxiety and instead examine strengths-based constructs such as enjoyment, optimism, resilience, and well-being [2, 45].”
Abebe, A., Geremew, Y., & Muluye, T. (2026) · Discover Education · doi:10.1007/s44217-026-02183-5
“Artificial intelligence (AI) has emerged as a transformative energy in contemporary education, influencing teaching and learning, assessment, student support, and institutional processes across different educational contexts [2, 13, 21].”
Perceptions of AI-assisted learning and learner development among Moroccan secondary school students
Kandirou, R., Youssfi, S. E., Atlassi, M. E., Dahman, A., & Hammoumi, M. M. E. (2026) · Discover Education · doi:10.1007/s44217-026-02109-1
- Authors
- Ajani, O. A., Gamede, B., & Matiyenga, T. C.
- Affiliations
- University of Zululand (ZA)
- Corresponding author
- Ajani, Oluwatoyin A.
- Venue
- Journal of Pedagogical Sociology and Psychology
- Publisher
- Journal of Pedagogical Research
- Published
- 2024-10-20
- ISSN
- 2687-3788 (ISSN-L)
- OpenAlex
- W4403610861
- Cited by
- 43
- References
- 0
- Citations by year
- 2026: 21 · 2025: 19 · 2024: 3
- FWCI
- 16.22
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Journal of Pedagogical Sociology and Psychology (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
- Keywords
- artificial intelligence in education, higher education teaching and learning, quality education, critical engagement, personalized learning, adaptive learning systems, data privacy, AI ethics, digital divide, digital literacy, technology acceptance model, AI adoption in education, responsible AI use
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review