GenAI in the Disciplines

This is a hybrid event taking place simultaneously on the university campus and via Microsoft Teams.

The room for the event is GE 5.16 in the Grosvenor East Building on the All Saints Campus of Manchester Metropolitan University, Oxford Road, Manchester, M15 6BG. There is a campus map here.

The MS Teams link for joining the event online will be available to all registered participants.

This talk will launch the URKI Metascience funded project, “GenAI in the Disciplines” led by Dr Niall Curry. The project is a timely endeavour, designed to interrogate research practices across the Social Sciences and forge a path for critical, nuanced, and disciplinarily situated approaches to engaging (or not) with Generative AI (GenAI) in research process.

This project builds on a growing body of work that forms part of a critical debate surrounding the role of GenAI across the Social Sciences. One key strand of this debate centres around the potential costs of the apparent efficiency of GenAI tools, with social scientists seeking to determine whether it is possible to conduct research that responsibly incorporates GenAI while sustaining research quality. Attempting to shed some light on this issue, studies across the academy have investigated the affordances of GenAI’s predictive power to fill gaps in research, automate transcription processes, annotate and analyse images, and support and automate prevalent and labour-intensive social scientific approaches, such as numerical and text analysis. For some, the expediency of GenAI is unmatched, while for others, questions of quality, ethics, transparency, and replicability undermine this expediency. Moreover, as GenAI develops apace and new questions of its application emerge, the challenge of determining the affordances of GenAI will likely become even greater, owing in part to the disparate perspectives on what constitutes responsible GenAI use in the Social Sciences. Looking forward, these views must be reconciled to support the development of more nuanced and situated research practices.

Yet, developing such a consensus for the Social Sciences is a challenge. The Social Sciences are home to a plurality of disciplines and their disciplinary and sub-disciplinary perspectives will shape their use of GenAI. This means that any resolution surrounding GenAI use cannot take the form of a simple one-size fits all ruling. It must, instead, consider disciplinary and sub-disciplinary variation. In this talk, I set a roadmap for a project designed to unpack GenAI use across the social sciences with a focus on Applied Linguistics. Specifically, the talk spotlights some of the key ideas emerging from my work in this area, addressing questions of GenAI literacy, ethics, knowledge-making, and the relevance of large language models for Applied Linguistics research. Through this exploration of emergent key issues, I reflect on the ‘goodness of the fit’ of GenAI for our research activity and consider the research areas in which the application of GenAI may be a) ineffective, b) antithetical to our research agenda, or c) pose some opportunity for research and knowledge-making.