Experiential Learning to Support Digital and Artificial Intelligence Literacies in Postsecondary Education
DOI:
https://doi.org/10.21432/cjlt28998Keywords:
artificial intelligence literacy, ethics, experiential learning theory, digital literacy, generative artificial intelligenceAbstract
This mixed-methods study examined how experiential learning theory (ELT) can support the development of digital and artificial intelligence literacies in postsecondary education through the integration of generative artificial intelligence (GenAI) tools. Guided by ELT’s four-stage cycle, concrete experience, reflective observation, abstract conceptualisation, and active experimentation, this study explored how students engaged with GenAI to enhance their learning, critical thinking, and ethical awareness. Data were collected from 17 students and one instructor through surveys and semi-structured interviews. Descriptive and thematic analyses revealed that students initially identified as beginners in GenAI use, employing the technology primarily for functional tasks such as organizing information or conducting surface-level research. Through experiential engagement and guided reflection, students demonstrated growth in confidence, ethical understanding, and critical evaluation of AI-generated outputs. Instructor findings converged with student perspectives, emphasizing the value of scaffolded, reflective engagement for literacy development. The integration of quantitative and qualitative results underscored the effectiveness of experiential learning in a GenAI-designed course.
References
Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), Article ep429. https://doi.org/10.30935/cedtech/13152
Anuyahong, B., Rattanapong, C., & Patcha, I. (2023). Analyzing the impact of artificial intelligence in personalized learning and adaptive assessment in higher education. International Journal of Research and Scientific Innovation (IJRSI), X(IV), 88–93. https://doi.org/10.51244/ijrsi.2023.10412
Atlas, S. (2023). ChatGPT for higher education and professional development: A guide to conversational AI. DigitalCommons@URI. https://digitalcommons.uri.edu/cba_facpubs/548
Bekdemir, Y. (2024). The urgency of AI integration in teacher training: Shaping the future of education. Journal of Research in Didactical Sciences, 3(1), 37–41. https://doi.org/10.51853/jorids/15485
Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide (1st ed.). SAGE Publications.
Campello de Souza, B., Serrano de Andrade Neto, A., & Roazzi, A. (2024). The generative AI revolution, cognitive mediation networks theory and the emergence of a new mode of mental functioning: Introducing the Sophotechnic Mediation scale. Computers in Human Behavior: Artificial Humans, 2(1), Article 100042. https://doi.org/10.1016/j.chbah.2024.100042
Castro, R. A. G., Cachicatari, N. A. M., Aste, W. M. B., & Medina, M. P. L. (2024). Exploration of ChatGPT in basic education: Advantages, disadvantages, and its impact on school tasks. Contemporary Educational Technology, 16(3). https://doi.org/10.30935/cedtech/14615
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(43). https://doi.org/10.1186/s41239-023-00411-8
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Delanoy, N., & Keyhani, M. (2025). Fostering AI literacy: Implementing IBL and experiential learning in a novel generative AI course in higher education. In Archer-Kuhn, B., MacKinnon, S. & Beltrano, N. (Eds.), Applying inquiry-based learning in higher education across disciplines (1st ed.). Cambridge Scholars Publishing.
Dimari, A., Tyagi, N., Davanageri, M., Kukreti, R., Yadav, R., & Dimari, H. (2024). AI-based automated grading systems for open book examination system: Implications for assessment in higher education. In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS; pp. 1–7). IEEE. https://doi.org/10.1109/ICKECS61492.2024.10616490
Doolittle, P., Wojdak, K., & Walters, A. (2023). Defining active learning: A restricted systematic review. Teaching & Learning Inquiry, 11, 1–23. https://doi.org/10.20343/teachlearninqu.11.25
Floridi, L., & Cowls, J. (2021). A unified framework of five principles for AI in society. In L. Floridi (Ed.). Ethics, governance, and policies in artificial intelligence. Springer. https://doi.org/10.1007/978-3-030-81907-1_2
Healey, M., & Jenkins, A. (2000). Kolb’s experiential learning theory and its application in geography in higher education. Journal of Geography, 99(5), 185–195. http://dx.doi.org/10.1080/00221340008978967
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Khlaif, Z. N., Ayyoub, A., Hamamra, B., Bensalem, E., Mitwally, M. A. A., Ayyoub, A., Hattab, M. K., & Shadid, F. (2024). University teachers’ views on the adoption and integration of generative AI tools for student assessment in higher education. Education Sciences, 14(10), Article 1090. https://doi.org/10.3390/educsci14101090
Kim, M., & Adlof, L. (2024). Adapting to the future: ChatGPT as a means for supporting constructivist learning environments. TechTrends: Linking Research & Practice to Improve Learning, 68(1), 37–46. https://doi.org/10.1007/s11528-023-00899-x
Kolb, A. Y., & Kolb, D. A. (2017). Experiential learning theory as a guide for experiential educators in higher education. Experiential Learning & Teaching in Higher Education, 1(1), Article 7. https://nsuworks.nova.edu/elthe/vol1/iss1/7
Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice Hall.
Lacey, M. M., & Smith, D. P. (2023). Teaching and assessment of the future today: Higher education and AI. Microbiology Australia, 44(3), 124–126. https://doi.org/10.1071/MA23036
Lu, K., Pang, F., & Shadiev, R. (2021). Understanding the mediating effect of learning approach between learning factors and higher order thinking skills in collaborative inquiry-based learning. Educational Technology Research & Development, 69(5), 2475–2492. https://doi.org/10.1007/s11423-021-10025-4
Lu, O. H. T., Huang, A. Y. Q., Huang, J. C. H., Lin, A. J. Q., Ogata, H., & Yang, S. J. H. (2018). Applying learning analytics for the early prediction of students’ academic performance in blended learning. Journal of Educational Technology & Society, 21(2), 220–232. http://www.jstor.org/stable/26388400
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
Lyanda, J. N., Owidi, S. O., & Simiyu, A. M. (2024). Rethinking higher education teaching and assessment in-line with AI innovations: A systematic review and meta-analysis. African Journal of Empirical Research, 5(3), 325–335. https://doi.org/10.51867/ajernet.5.3.30
Matook, S., Wang, Y. M., Koeppel, N., & Guerin, S. (2021). Experiential learning in work-integrated learning (WIL) projects for metacognition: Integrating theory with practice. In ACIS 2021 Proceedings, Article 77. https://aisel.aisnet.org/acis2021/77
Pelletier, K., McCormack, M., Reeves, J., Robert, J., Arbino, N., Al-Freih, M., Dickson-Deane, C., Guevara, C., Koster, L., Sánchez-Mendiola, M., Bessette, L. S., & Stine, J. (2022). 2022 EDUCAUSE horizon report: Teaching and learning edition. EDUCAUSE. https://library.educause.edu/-/media/files/library/2022/4/2022hrteachinglearning.pdf
Ponce, O. A., & Pagán-Maldonado, N. (2015). Mixed Methods Research in Education: Capturing the Complexity of the Profession. International Journal of Educational Excellence, 1(1), 111–135. https://doi.org/10.18562/IJEE.2015.0005
Qureshi, B. (2023). ChatGPT in computer science curriculum assessment: An analysis of its successes and shortcomings. In M. D. Ventura & H. Yu (Chairs), ICSLT ‘23: Proceedings of the 2023 9th International Conference on E-Society, E-Learning and E-Technologies (pp. 7–13). ACM. https://doi.org/10.1145/3613944.3613946
Rasul, T., Nair, S., Kalendra, D., Robin, M., de Oliveira Santini, F., Ladeira, W., Sun, M., Day, I., Rather, R. A., & Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching, 6(1), 41–56. https://search.informit.org/doi/10.3316/informit.T2025102700000390990155131
Schön, D. A. (1992). The reflective practitioner: How professionals think in action (pp. 21–49). Taylor & Francis Group.
Slimi, Z. (2023). The impact of artificial intelligence on higher education: An empirical study. European Journal of Educational Sciences, 10(1), 17–33. https://doi.org/10.19044/ejes.v10no1a17
Slotnick, R. C., & Boeing, J. Z. (2025). Enhancing qualitative research in higher education assessment through generative AI integration: A path toward meaningful insights and a cautionary tale. New Directions for Teaching and Learning, 2025(182), 97–112. https://doi.org/10.1002/tl.20631
Sporrong, E., McGrath, C., & Cerratto Pargman, T. (2024). Situating AI in assessment—An exploration of university teachers’ valuing practices. AI and Ethics, 5, 2381–2394. https://doi.org/10.1007/s43681-024-00558-8
Turner, L., Hashimoto, D. A, Vasisht, S., & Schaye, V. (2024). Demystifying AI: Current state and future role in medical education assessment. Academic Medicine, 99(4S), 42–47. https://doi.org/10.1097/acm.0000000000005598
Weng, X., Xia, Q., Gu, M., Rajaram, K., & Chiu, T. K. F. (2024). Assessment and learning outcomes for generative AI in higher education: A scoping review on current research status and trends. Australasian Journal of Educational Technology, 40(6), 37–55. https://doi.org/10.14742/ajet.9540
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
Xia, Q., Weng, X., Ouyang, F., Lin, T. J., & Chiu, T. K. F. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. International Journal of Educational Technology in Higher Education, 21(1), Article 40. https://doi.org/10.1186/s41239-024-00468-z
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), Article 39. https://doi.org/10.1186/s41239-019-0171-0
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