Jenny Lawrence, Oxford Brookes
Sue Beckingham, Sheffield Hallam
Peter Hartley, Edge Hill
Stephen Powell, Independent
Generative AI (GenAI) has rapidly evolved from a specialist application to a ubiquitous technology, dominating public debate about the impact of computers. Despite its potential to enhance creativity and efficiency, GenAI raises concerns about colonial thinking, exclusivity, security, reliability, and societal and environmental impact. However, we cannot ignore its power and potential. Its increasing use in public and private organisations necessitates the development of GenAI skills as an essential graduate attribute. Higher Education Institutions (HEIs) have a responsibility to ensure ethical GenAI use, fostering digital literacy and integrity while considering digital poverty and data security.
While multiple guidance documents for the use of Gen AI in education have been published based on principles rather than practice. HE leaders, teachers and educational developers across the sector need practical, evidence-based guidance for embedding GenAI in teaching, learning, and assessment.
With this in mind we invited contributions through the SEDA Maillist and compiled a text which explores the integration of GenAI in Higher Education Institutions (HEIs). This text, in the SEDA/Routledge Focus series, is published May, 2024: ‘Using Generative AI Effectively in Higher Education: Sustainable and Ethical Practices for Learning, Teaching and Assessment . It includes evaluated and evidently effective strategies for building GenAI capability, developing GenAI literacy, curriculum design, and new approaches to assessment for a GenAI-enabled world. Each chapter includes a student or stakeholder appraisal of the work outlined. The text covers 4 themes, with contributions from across a number of international Anglophone HEIs:
Building GenAI Capability: Saunders, Coulby and Lindsay (University of Liverpool, UK) highlight GenAI’s role in enhancing practices and the need to formulate new policies. Davis (Oxford Brookes University, UK) emphasises the need for inclusive teaching and support to address academic integrity challenges as GenAI becomes prevalent.
Developing GenAI Literacy: Bedford, Kim, and Qin (University of New South Wales, Australia) harness GenAI’s potential to support autonomous student learning, particularly for English as Additional Language students. A collaboration of scholars from universities in Australia, Canada, and the UK (Newell, Fitzgerald, Hall, Mills, Beynen, May, Mason and Lai) examine how equitable GenAI integration can address educational disparities. Hemsworth, Walker and Evans (Sheffield Hallam University, UK) explore the intersection between GenAI tools and academic writing, recommending strategies to support undergraduates using GenAI tools for academic writing.
Curriculum Design for a GenAI-enabled World: Farrell (South East Technological University, Ireland) focuses on creative approaches to student engagement in their music degree, utilizing GenAI. Young, Burr and Kumar (University College London, UK) suggest that the potential of GenAI text-to-image generation remains underexplored, particularly in medical education. Reimers and Myers (University of London, UK) use GenAI in group roleplay activities for health sciences students. Hatley and Penny (Manchester Metropolitan University, UK) integrate GenAI into their curriculum to enhance authenticity verification and authentic assessment practices.
Assessment in a GenAI-enabled World: Powell and Forsyth (Manchester Met, UK and Lund University, Sweden) review the fundamentals of assessment and feedback to discuss the implications of GenAI for assessment design and management. Upsher, Heard, Yalcintas, Pearson and Findon (King’s College, London, UK) critically examines the challenges, ethics, and opportunities of meaningfully incorporating GenAI into the context of authentic assessment. Smith and Francis (Sheffield Hallam and Cardiff,UK) describe assessment designs where students are led through the creation process, starting with the identification of relevant content, and GenAI is built into the assessment design by assessing the process.
The text closes with a reflection on the fast emergence of Gen AI and considers future developments and how the HE Sector might respond (Beckingham, Lawrence, Powell and Hartley).
We hope the text will prove useful for those of us adapting and adopting Gen AI in teaching, learning and assessment in HE.
The text is available to SEDA members with a 20% discount – use the ‘SEDA’ code at the Routledge online store. We will introduce the text at the Oxford Brookes University conference ‘Academic Ambition for Social Justice’ on the 19th June, 2024.
Acknowledgements
With grateful thanks to our contributing authors who worked swiftly and efficiently between our opening the call in July 2023 to final submissions in January 2024.
Sue Beckingham is an Associate Professor Learning and Teaching at Sheffield Hallam University, UK. She is a National Teaching Fellow, Senior Fellow HEA, Fellow of SEDA and Visiting Fellow at Edge Hill University.
Jenny Lawrence is Professor of Higher Education and Director of the Oxford Brookes Centre for Academic Enhancement and Development, UK. She is also Senior Fellow of SEDA, Principal and National Teaching Fellow.
Stephen Powell is a freelance Higher Education consultant based in New Zealand and Principal Fellow HEA.
Peter Hartley is a freelance Higher Education consultant, National Teaching Fellow, and Visiting Professor at Edge Hill University, UK.
References and list of contributions to the collection:
Saunders, S., Coulby, C. & Lindsay, R., 2024. Pedagogy and policy in a brave new world: A case study on the development of generative AI literacy at the University of Liverpool. In: S. Beckingham, J. Lawrence, S. Powell, and S. Hartley, ed., Using AI effectively in HE: sustainable and ethical practices for teaching, learning and assessment, 1st ed. [ebook] pp.11-20.
Davis, M., 2024. Supporting inclusion in academic integrity in the age of GenAI. In: S. Beckingham, J. Lawrence, S. Powell, and S. Hartley, ed., Using AI effectively in HE: sustainable and ethical practices for teaching, learning and assessment, 1st ed. [ebook] Routledge: UK, pp.21-32.
Bedford, J., Kim, M. & Qin, J.C., 2024. Confidence enhancer, learning equalizer, and pedagogical ally: Exploring GenAI for students with English as an additional language. In: S. Beckingham, J. Lawrence, S. Powell, and S. Hartley, ed., Using AI effectively in HE: sustainable and ethical practices for teaching, learning and assessment, 1st ed. [ebook] Routledge: UK, pp.33-41.
Newell, S. et al., 2024. Integrating GenAI in higher education: Insights, perceptions, and a taxonomy of practice. In: S. Beckingham, J. Lawrence, S. Powell, and S. Hartley, ed., Using AI effectively in HE: sustainable and ethical practices for teaching, learning and assessment, 1st ed. [ebook] Routledge: UK, pp.42-53.
Hemsworth, K., Walker, A. & Evans, J., 2024. ‘Understood the assignment’: A UX-led investigation into student experiences of GenAI. In: S. Beckingham, J. Lawrence, S. Powell, and S. Hartley, ed., Using AI effectively in HE: sustainable and ethical practices for teaching, learning and assessment, 1st ed. [ebook] Routledge: UK, pp.54-66.
Farrell, H., 2024. Re-imagining student engagement in an AI-enhanced classroom: Strategies and practices. In: S. Beckingham, J. Lawrence, S. Powell, and S. Hartley, ed., Using AI effectively in HE: sustainable and ethical practices for teaching, learning and assessment, 1st ed. [ebook] Routledge: UK, pp.67-73.
Burr, P., Kumar, A. & Young, T., 2024. The potential of AI text-to-image generation in medical education: The educator and students’ perspective. In: S. Beckingham, J. Lawrence, S. Powell, and S. Hartley, ed., Using AI effectively in HE: sustainable and ethical practices for teaching, learning and assessment, 1st ed. [ebook] Routledge: UK, pp.74-81.
Reimers, S. & Myers, L., 2024. Using generative AI agents for scalable roleplay activities in the health sciences. In: S. Beckingham, J. Lawrence, S. Powell, and S. Hartley, ed., Using AI effectively in HE: sustainable and ethical practices for teaching, learning and assessment, 1st ed. [ebook] Routledge: UK, pp.82-86.
Hatley, N. & Penny, P., 2024. Embracing GenAI in education: A path towards authentic assessment. In: S. Beckingham, J. Lawrence, S. Powell, and S. Hartley, ed., Using AI effectively in HE: sustainable and ethical practices for teaching, learning and assessment, 1st ed. [ebook] Routledge: UK, pp.87-96.
Powell, S. & Forsyth, R., 2024. Generative AI and the implications for authentic assessment. In: S. Beckingham, J. Lawrence, S. Powell, and S. Hartley, ed., Using AI effectively in HE: sustainable and ethical practices for teaching, learning and assessment, 1st ed. [ebook] Routledge: UK, pp.97-105.
Upsher, R. et al., 2024. Embracing generative AI in authentic assessment: Challenges, ethics, and opportunities. In: S. Beckingham, J. Lawrence, S. Powell, and S. Hartley, ed., Using AI effectively in HE: sustainable and ethical practices for teaching, learning and assessment, 1st ed. [ebook] Routledge: UK, pp.106-116.
Smith, D. & Francis, N., 2024. Process not product in the written assessment. In: S. Beckingham, J. Lawrence, S. Powell, and S. Hartley, ed., Using AI effectively in HE: sustainable and ethical practices for teaching, learning and assessment, 1st ed. [ebook] Routledge: UK, pp.117-127.
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