Abstract:

The use of generative artificial intelligence (GenAI) and Large Language Model (LLM) tools in higher education has gained a lot of attention in the media since late 2022. Much of this attention has been about the use of tools like ChatGPT and Google Bard as a way for students to cheat on assessments. This paper takes an alternative approach and explores how tools like ChatGPT and Google Bard can be used to enhance teaching and learning in a higher education context.

A literature review is presented that highlights ways in which these tools can be used to enhance teaching and learning with these including the generation of hypothetical scenarios; the development of skills that are needed in industry (Chan & Hu, 2023; Ivanov & Soliman, 2023) the importance of considering the context of students (Sun & Holt, 2022; Connolly et al., 2022); and the potential for these tools to generate feedback for students (Sung et al., 2023).

Interviews were conducted with seven lecturers teaching courses in a range of subjects fron undergraduate to post graduate level about how they have used GenAI and LLM tools in their teaching with the goal of enhancing teaching and learning and the extent to which this was achieved from the point of view of the lecturers. The activities that these lecturers used the GenAI tools for were mapped onto a revised Bloom’s Taxonomy of Learning (Anderson & Krathwohl, 2001). The subjects being taught included Law, Education, Finance, English, Mathematics and Entrepreneurship with the lecturers being from four institutions.

The ensuing analysis, discussion and conclusions demonstrate ways in which GenAI tools like Chat GPT and Google Bard can be used to enhance teaching and learning in higher education in ways that are consistent with the literature and that can potentially be applied to other disciplines. Possibilities for extensions to this research are identified.

References:
[1] Anderson, L., & Krathwohl, D. (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. Longman.
[2] Chan, C. K. Y., & Hu, W. (2023). Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education. arXiv preprint arXiv:2305.00290.
[3] Connolly, A. J., Mutchler, L. A., & Rush, D. E. (2022). Teaching Tip: Socio-Cultural Learning to Increase Student Engagement in Introduction to MIS. Journal of Information Systems Education, 33(2), 113.
[4] Hayes, A. (2023). “Conversing” with Qualitative Data: Enhancing Qualitative Sociological Research through Large Language Models (LLMs).
[5] Ivanov, S., & Soliman, M. (2023). Game of algorithms: ChatGPT implications for the future of tourism education and research. Journal of Tourism Futures, 9(2), 214-221.
[6] Nguyen, T., Cao, L., Nguyen, P., & Nguyen, P. (2023). Capabilities, Benefits, and Role of ChatGPT in Chemistry Teaching and Learning in Vietnamese High Schools.
[7] Sun, X., & Holt, D. (2022). Student engagement and voice in higher education: students’ perceptions. Journal of Learning Development in Higher Education, (23).
[8] Sung, G., Guillain, L., & Schneider, B. (2023). Can AI help teachers write higher quality feedback? Lessons learned from using the GPT-3 engine in a makerspace course. In Proceedings of the 17th International Conference of the Learning Sciences-ICLS 2023, pp. 2093-2094. International Society of the Learning Sciences.

Keywords:

Information Technology Education, Computer Science Education, Generative AI, Large Language Models, Chat GPT, Google Bard, Teaching and Learning.