Abstract
Artificial Intelligence (AI) has been around for nearly a century, yet in recent years the rapid advancement and public access to AI applications and algorithms have led to increased attention to the role of AI in higher education. An equally important but overlooked topic is the study of AI teaching and learning in higher education. We wish to examine the overview of the study, pedagogical outcomes, challenges, and limitations through a systematic review process amidst the COVID-19 pandemic and public access to ChatGPT. Twelve articles from 2020 to 2023 focused on AI pedagogy are explored in this systematic literature review. We find in-depth analysis and comparison of work post-COVID and AI teaching and learning era is needed to have a more focused lens on the current state of AI pedagogy. Findings reveal that the use of self-reported surveys in a pre-and post-design form is most prevalent in the reviewed studies. A diverse set of constructs are used to conceptualize AI literacy and their associated metrics and scales of measure are defined based on the work of specific authors rather than a universally accepted framework. There remains work and consensus on what learning objectives, levels of thinking skills, and associated activities lead to the advanced development of AI literacy. An overview of the studies, pedagogical outcomes, and challenges are provided. Further implications of the studies are also shared. The contribution of this work is to open discussions on the overlooked topic of AI teaching and learning in higher education.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10639-024-12679-y/MediaObjects/10639_2024_12679_Fig1_HTML.png)
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
Data sharing does not apply to this article as no datasets were generated or analyzed during the current study.
References
Ali, M., & Abdel-Haq, M. K. (2021). Bibliographical analysis of artificial intelligence learning in Higher Education: is the role of the human educator and educated a thing of the past? Fostering Communication and Learning With Underutilized Technologies in Higher Education, 36–52.
Anderson, J., Rainie, L., & Luchsinger, A. (2018). Artificial intelligence and the future of humans. Pew Research Center, 10(12).
Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). No can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17(1), 1–12.
Biggs, J., & Tang, C. (2011). Train-the-trainers: Implementing outcomes-based teaching and learning in Malaysian higher education. Malaysian Journal of Learning and Instruction, 8, 1–19.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Chang, Y. S., Wang, Y. Y., & Ku, Y. T. (2023). Influence of online STEAM hands-on learning on AI learning, creativity, and creative emotions. Interact Learn Environ PG. https://doi.org/10.1080/10494820.2023.2205898.
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, 100002.
Clark, D. (2023). PedAIgogy – New Era of Knowledge and Learning Where AI changes Everything.
Covidence (2023). Covidence systematic review software. www.covidence.org.
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 1–22.
Cumming, G., & McDougall, A. (2000). Mainstreaming AIED into education. International Journal of Artificial Intelligence in Education, 11(2), 197–207.
Dignum, V. (2020). AI is multidisciplinary. AI Matters, 5(4), 18–21.
Fuller, C. M., Simmering, M. J., Atinc, G., Atinc, Y., & Babin, B. J. (2016). Common methods variance detection in business research. Journal of Business Research, 69(8), 3192–3198. https://doi.org/10.1016/j.jbusres.2015.12.008.
Gao, Z., Wanyama, T., & Singh, I. (2020). Project and practice centered learning: A systematic methodology and strategy to cultivate future full stack artificial intelligence engineers. Int. J. Eng. Educ, 36(6 PG-1760–1772), 1760–1772. https://www.scopus.com/inward/record.uri?eid=2s2.085096038454&partnerID=40&md5=b4778257adf34da504fdf1f97ebb7fe9NS.
Halic, O., Lee, D., Paulus, T., & Spence, M. (2010). To blog or not to blog: Student perceptions of blog effectiveness for learning in a college-level course. The Internet and Higher Education, 13(4), 206–213.
Herrington, J., & Oliver, R. (2000). An instructional design framework for authentic learning environments. Educational Technology Research and Development, 48(3), 23–48.
Hsu, T. C., & Chen, M. S. (2022). The engagement of students when learning to use a personal audio classifier to control robot cars in a computational thinking board game. Res Pract Technol Enhanc Learn, 17(1 PG-). https://doi.org/10.1186/s41039-022-00202-1.
Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100001.
Jaramillo, F., Locander, W. B., Spector, P. E., & Harris, E. G. (2007). Getting the job done: The moderating role of initiative on the relationship between intrinsic motivation and adaptive selling. Journal of Personal Selling and Sales Management, 27(1), 59–74.
Javed, R. T., Nasir, O., Borit, M., Vanhée, L., Zea, E., Gupta, S., Vinuesa, R., & Qadir, J. (2022). Get out of the BAG! Silos in AI Ethics Education: Unsupervised topic modeling analysis of global AI Curricula. Journal of Artificial Intelligence Research, 73, 933–965. https://doi.org/10.1613/jair.1.13550.
Jiang, L. (2021). Virtual reality Action interactive teaching Artificial Intelligence Education System. Complexity, 2021(PG-). https://doi.org/10.1155/2021/5553211.
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.
Keller, J. M. (1987). Development and use of the ARCS model of instructional design. Journal of Instructional Development, 10(3), 2–10.
Kim, S., Jang, Y., Choi, S., Kim, W., Jung, H., Kim, S., & Kim, H. (2021). Analyzing teacher competency with TPACK for K-12 AI education. KI-Künstliche Intelligenz, 35(2), 139–151.
Koh, J. H., Chai, L., Wong, C. S., & Hong, B., H.-Y (2015). Design thinking for education: Conceptions and applications in teaching and learning. Springer.
Kong, S. C., Cheung, W. M. Y., & Zhang, G. (2023). Evaluating an Artificial Intelligence Literacy Programme for developing University students’ conceptual understanding, literacy, empowerment and ethical awareness. Educational Technology and Society, 26(1), 16–30. https://doi.org/10.30191/ETS.202301_26(1).0002.
Korkmaz, Ö., & Xuemei, B. A. I. (2019). Adapting computational thinking scale (CTS) for Chinese high school students and their thinking scale skills level. Participatory Educational Research, 6(1), 10–26.
Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory into Practice, 41(4), 212–218. https://doi.org/10.1207/s15430421tip4104_2.
Lin, C. H., Wu, L. Y., Wang, W. C., Wu, P. L., & Cheng, S. Y. (2020). Development and validation of an instrument for AI-Literacy. 3rd Eurasian Conference on Educational Innovation (ECEI 2020).
Lin, X. F., Chen, L., Chan, K. K., Peng, S. Q., Chen, X. F., Xie, S. Q., Liu, J. C., & Hu, Q. T. (2022). Teachers’ perceptions of teaching sustainable Artificial Intelligence: A Design Frame Perspective. SUSTAINABILITY, 14(13 PG-). https://doi.org/10.3390/su14137811.
Martín-Núñez, J. L., Ar, A. Y., Fernández, R. P., Abbas, A., & Radovanović, D. (2023). Does intrinsic motivation mediate perceived artificial intelligence (AI) learning and computational thinking of students during the COVID-19 pandemic? Computers & Education, 4(PG-). https://doi.org/10.1016/j.caeai.2023.100128.
Meek, T., Barham, H., Beltaif, N., Kaadoor, A., & Akhter, T. (2016). Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review. 2016 Portland International Conference on Management of Engineering and Technology (PICMET), 682–693.
Miriyev, A., & Kovač, M. (2020). Skills for physical artificial intelligence. Nature Machine Intelligence, 2(11), 658–660.
Ng, D. T. K., Lee, M., Tan, R. J. Y., Hu, X., Downie, J. S., & Chu, S. K. W. (2022). A review of AI teaching and learning from 2000 to 2020. EDUCATION AND INFORMATION TECHNOLOGIES. https://doi.org/10.1007/s10639-022-11491-w.
Pinkwart, N. (2016). Another 25 years of AIED? Challenges and opportunities for intelligent educational technologies of the future. International Journal of Artificial Intelligence in Education, 26, 771–783.
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), 1–13.
Rizvi, S., Waite, J., & Sentance, S. (2023). Artificial Intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review. Computers and Education: Artificial Intelligence, 100145.
Shih, P. K., Lin, C. H., Wu, L. Y., & Yu, C. C. (2021). Learning ethics in AI—teaching non-engineering undergraduates through situated learning. Sustainability, 13(7), 3718.
Southworth, J., Migliaccio, K., Glover, J., Glover, J. N., Reed, D., McCarty, C., Brendemuhl, J., & Thomas, A. (2023). Developing a model for AI across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers & Education, 4, PG–. https://doi.org/10.1016/j.caeai.2023.100127.
Su, J., Guo, K., Chen, X., & Chu, S. K. W. (2023). Teaching artificial intelligence in K–12 classrooms: A scoping review. Interactive Learning Environments, 1–20.
Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A. (2021). Teaching machine learning in K–12 classroom: Pedagogical and technological trajectories for artificial intelligence education. Ieee Access : Practical Innovations, Open Solutions, 9, 110558–110572.
UNESCO (2023). 10 Innovative Learning Strategies For Modern Pedagogy. TeachThought Staff. https://policytoolbox.iiep.unesco.org/library/BHABKKH6.
Wong, M. K., Wu, J., Ong, Z. Y., Goh, J. L., Cheong, C. W. S., Tay, K. T., Tan, L. H. S., & Krishna, L. K. R (2019). Teaching ethics in medical schools: A systematic review from 2000 to 2018. Journal of Medical Education, 18, 226–250.
Xu, B. (2021). Artificial Intelligence Teaching System and Data Processing Method based on Big Data. Complexity, 2021(PG-). https://doi.org/10.1155/2021/9919401.
Yağcı, M. (2019). A valid and reliable tool for examining computational thinking skills. Education and Information Technologies, 24(1), 929–951.
Yi, Y. (2021). Establishing the concept of AI literacy. European Journal of Bioethics, 12(2), 353–368.
Yue, M., Jong, M. S. Y., & Dai, Y. (2022). Pedagogical design of K-12 artificial intelligence education: A systematic review. Sustainability, 14(23), 15620.
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), 1–27.
Zeide, E. (2019). Artificial intelligence in higher education: Applications, promise and perils, and ethical questions. Educause Review, 54(3).
Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025.
Acknowledgements
Not applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Not applicable.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Memarian, B., Doleck, T. Teaching and learning artificial intelligence: Insights from the literature. Educ Inf Technol 29, 21523–21546 (2024). https://doi.org/10.1007/s10639-024-12679-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-024-12679-y