Teacher agency and generative artificial intelligence: teaching in higher education as a responsive, cultural activity
Year: 2025
Type: article
Abstract: The widespread adoption of Generative Artificial Intelligence tools by students poses a challenge to teachers in Higher Education. This study aimed to explore the nature of teacher agency in a setting where students were making extensive use of Large Language Models. The study was conducted in a research-intensive university in the UK, adopting a sequential mixed methods research design. It found that challenges entailed in university teaching can helpfully be framed in terms of a relationship between the agency of teachers and the agency of students, even as this relationship is subject to cultural influences. Given a reflexive basis for agency, this framing underscores the importance of dialogue between teachers and students, even as the uncertainty generated by Large Language Models undercuts the conditions which make the dialogue possible. The study also highlights the limitations of existing perspectives on higher education pedagogy that see the relation between teaching and learning as something primarily determined by structural considerations. It offers an original conceptualisation of higher education pedagogy that sees teacher agency as incorporating a collective response to shifts in the epistemic agency of students – in a fashion that accounts for the affordances of contemporary digital tools and expertise in their use.
Source: Learning Media and Technology
Authors: Peter Kahn, Mark Carrigan, Paul Smith, Lisa Murtagh, Ruirui Liu, Fangtong Song
Institution: University of Manchester
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
Funder: University of Manchester
OpenAlex ID: W4415324582