Since the release of ChatGPT in November 2022, discussions about Generative Artificial Intelligence (GenAI) have dominated the discourse across Higher Education both nationally and internationally. The introduction of any new technology into the teaching space is often a catalyst for substantial changes in practice (McDonald et al., 2025). In the case of Gen AI, much effort has been expended in working to understand the pedagogical implications of its use from various perspectives, including impacts on academic integrity, assessment design, and the need for critical AI literacy for both staff and students.

Pedagogical decision making is challenging given the need to be cognisant of, and to balance the benefits and costs of GenAI use. Positive impacts on students’ creativity and self-efficacy (Wang, Sun, & Chen, 2023), and the potential for personalised learning experiences (Xu et al. 2023) sit (not always comfortably) alongside our still developing knowledge of the potential negative impacts on cognitive skills such as decision making (Heersmink 202).

The ever-growing corpus of research on Gen AI in education spaces has thus focused on impacts on student learning, and, in addition, the ways in which Gen AI is incorporated into curricula. Attention has also been directed towards inherent paradoxes of GenAI use, and the impact of polarising views (Lim et al., 2023). However, as an academic community, the contrasting views, perspectives, and research present a wicked problem. How does an educator accommodate contrasting views on the worth of Gen AI in their own practice, and deal with the inherent cognitive dissonance this may present? If, as the recent HEA National Forum Policy Framework states “it is a set of tools that, regardless of any individual professional or personal perspective, must be integrated thoughtfully into teaching and learning …..” (O’Sullivan et al., 2026, pg3), how is this understood and negotiated by educators in their day-to-day practice?

This study investigates how academics in an Irish HEI narrate their navigation of any perceived tensions in their use of GenAI in teaching, learning and assessment. Adopting a qualitative approach the study utilises the Rich Picture method (Bell, Berg, & Morse, 2016). As the method involves participants creating visual and symbolic representations of their lived experiences, it captures conceptual and emotional aspects relevant to the individual's experience in ways which may uncover the messy complexities inherent in the topic of study. Initial results will be presented focusing on academics from a range of disciplines and levels of experience

Bell, S., Berg, T., & Morse, S. (2016). Rich Pictures: Encouraging Resilient Communities (1st ed.). Routledge. https://doi.org/10.4324/9781315708393

Heersmink, R. (2024). Use of large language models might affect our cognitive skills. Nature Human Behaviour, 8, 805–806. https://doi.org/10.1038/s41562-024-01859-y

Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2) https://doi.org/10.1016/j.ijme.2023.100790

McDonald, N., Johri, A., Ali, A., & Hingle, A. (2024). Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines. arXiv. https://doi.org/10.48550/arXiv.2402.01659

O’Sullivan, J., Lowry, C., Woods, R., & Conlon, T. (2025). Generative AI in higher education teaching & learning: Policy framework. Higher Education Authority. https://doi.org/10.82110/073e-hg66

Wang, S., Sun, Z., & Chen, Y. (2023). Effects of higher education institutes’ artificial intelligence capability on students' self-efficacy, creativity and learning performance. Education and Information Technologies, 28(5), 4919–4939. https://doi.org/10.1007/s10639-022-11443-4

Xu, W., Meng, J., Raja, S. K. S., Priya, M. P., & Kiruthiga Devi, M. (2023). Artificial intelligence in constructing personalized and accurate feedback systems for students. International Journal of Modeling, Simulation, and Scientific Computing, 14(01), https://doi.org/10.1142/S1793962323410015