Staff development for generative artificial intelligence and collaborative learning using Iterationism as a theoretical framework - Nicholas Bowskill; David Hall; Melody Harrogate; Ebere Eziefuna; Ben Marler (Journal of Learning Development in Higher Education)
Generative artificial intelligence has confronted academic developers with the challenge of understanding new technologies and simultaneously providing authentic pedagogical support for academics who are also struggling to adapt. This empirical study responds to these challenges by reviewing a staff development workshop for generative AI and collaborative learning delivered to academics from various disciplines at the University of Derby, UK. This is an example of online academic lecturers working in ‘third space’ roles, providing professional development support for other academics on campus. A focus group was used immediately after the experiential workshop as a means of gathering empirical data. Findings show lecturers are concerned about AI, but classroom-based staff development workshops can provide useful third spaces for discussion and sharing good practice. Interestingly, AI prompts emerged as a way of making cognitive effort visible, and the article responds to this finding with Iterationism as an emergent theory for learning with generative AI. This reflects a process-oriented view of learning with these technologies. Beyond developing theory for generative AI and learning, we make four contributions to the literature on third spaces. They are (1) that online lecturers occupy and create third spaces across different modes; (2) that collaboration on applications of AI technologies can address relational tensions highlighted in third space research (Daza, Gudmundsdottir and Lund, 2021); (3) that AI can be understood as a third space for the way it feeds into discussions across students, academics, and external organisations; and (4) that we have developed theory from cross-modal third space practice.