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Parallel Sessions: 14.20 - 15.05

Take a look at the speaker abstracts to decide which parallel session you would like to attend. Choose on the day.

Running from 14.20-15.05 in L1

Abstract

Academic Writing with AI: A Practical Introduction for Researchers   

This interactive session explores ethical and practical dimensions of using AI tools to support academic research writing. It looks at ways to integrate AI responsibly at different stages of the research writing process, from planning and drafting to refining and revising. Participants will consider how AI can enhance clarity and structure, and how researchers can maintain their own authorship and voice while aligning with the University’s academic integrity guidance. Drawing on examples from the Academic Writing with AI course, the session considers how to ensure ethical and effective AI use in research communication. Attendees will leave with practical suggestions to engage with AI as a constructive partner in the writing process, improving efficiency, precision, and reflective practice. 

Sam McIlroy Bio

Sam McIlroy is Director of the Centre for Academic Communication in English, specialising in English for Academic Purposes (EAP) and academic literacies. He holds degrees from Cambridge, Manchester, and Nottingham, and is a Senior Fellow of the Higher Education Academy. Sam has taught in Spain, Turkey, China, Kazakhstan, and Colombia, and previously worked at UCL, Nottingham, and Oxford. His work focuses on academic writing, curriculum design, and transnational collaboration, including partnerships with the Open University of China and Wuhan University. His research interests include genre pedagogy, task-based language teaching, and discourse analysis. 

Running from 14.20-15.05 in L2

ABSTRACT

Teaching during the Cognitive Revolution: What will learning and teaching look like in the age of AI? 

The emergence of Large Language Models (LLMs) has completely upset not only what we can expect of a computer but also how we view the role of technology in the process of learning. Now we have machines that can help at all stages of the learning process - they can not only help us manage cognitive load but also completely remove it. This talk will explore the roles AI can play in the learning process both from the perspective of the learner and the educator. It will explore the role of the LLM-powered tool not only as a productivity assistant, peer reviewer, or tutor but also as a toolmaker.   

Dominik Lukeš bio 

Dominik Lukeš is AI Consultant at the AI Competency Centre at the University of Oxford. He founded the Reading and Writing Innovation Lab at Oxford in 2021, focusing on technology-enhanced reading and writing practices. He authored the Oxford report "Beyond ChatGPT: State of AI in Education 2023" and developed an AI Task Evaluation framework for educational contexts. His background includes computational linguistics and natural language processing since the 1990s, with research interests in metaphor, discourse analysis, and language education. He publishes the AI in Academic Practice Newsletter on LinkedIn. 

Running from 14.20-15.05 in L3

Abstract

Navigating AI and Intellectual Property: from Protection to Commercialisation

This session will offer a practical overview of key intellectual property considerations surrounding AI. It will provide a concise introduction to IP and forms of IP protection, followed by an exploration of how these apply to AI technologies, including the patentability of AI-related inventions. The session will also highlight case studies demonstrating successful commercialisation of AI, and attendees will engage in interactive group work to analyse a use case and apply the concepts discussed. 

Running from 14.20-15.05 in L4

Abstract

Beyond the model: considering the equality dimensions of AI use in research

AI increasingly influences how research is designed, conducted, and interpreted. This shifts labour, judgement, and authority in ways that can strengthen research — or quietly introduce hidden gaps. One area where this becomes particularly visible is equality: whose data is present or absent, which voices are amplified or smoothed over, and how research findings travel into wider use. 

In this interactive workshop, we invite participants to apply an “equality stress-test” to their research practice. Rather than offering technical debiasing methods, we focus on three questions that cut across disciplines: 

  1. Who is missing from the data underpinning the AI tools we use — and does that absence shape or skew our claims? 

  1. What forms of human judgement or nuance might be reduced or reshaped in exchange for efficiency? 

  1. If AI-driven findings are applied at scale, travelling into policy or practice, who benefits — and are there specific groups who might be inadvertently disadvantaged? 

The session combines a short framing presentation with structured discussion, drawing on participants’ own research contexts. Attendees will leave better equipped to identify structural gaps in their adoption of AI, and to integrate more reflexive, context-sensitive thinking into their research practice.