- Faculty/module/cohort: Institution-wide perspective, with examples drawn from Fashion, Law and Education.
- AI Tool(s) Used: This case study focuses on institutional governance rather than the implementation of specific AI tools. Examples discussed during the interview included Grammarly and AI software commonly used within the fashion industry.
- AI Mode(s) Used: Not applicable in the conventional sense. The case study examines institutional guidance and governance that supports the responsible use of AI across teaching and learning rather than a specific multimodal learning activity.
What was the challenge?
Chris described the University’s primary challenge as developing guidance that could respond effectively to the rapid evolution of generative AI while remaining relevant to both staff and students.
Rather than introducing a fixed institutional policy, the University chose to develop guidance that could be updated more readily as technologies and educational practices evolved. Chris explained that guidance was viewed as more adaptable than formal policy and more likely to be consulted by staff and students when making decisions about AI use.
Throughout the interview, Chris emphasised that effective AI governance should support informed academic decision-making while remaining sufficiently flexible to accommodate disciplinary differences and future technological developments.
What did they do?
Chris described the development of a university-wide community of practice centred on AI in learning and teaching.
An AI Student Group was established with membership open to any interested colleague rather than operating as a representative committee. Chris explained that the intention was to encourage discussion among individuals holding a wide range of perspectives, including those who were enthusiastic about AI as well as those who remained sceptical of its educational value.
The group reports into the University’s Digital Transformation Committee and serves as a mechanism for identifying, sharing and disseminating examples of effective practice emerging across different disciplines.
Chris reported that the group has produced several institutional resources, including public statements, guidance documents, case studies and staff development activities. Rather than prescribing uniform approaches, these resources aim to support informed decision-making while recognising disciplinary variation.
He also described embedding references to institutional AI guidance directly within assessment documentation and marking criteria, reflecting the observation that students are more likely to engage with guidance presented within assessment materials than with standalone policy documents.
At the time of the interview, the guidance was undergoing further revision to broaden its scope beyond academic integrity by incorporating considerations relating to sustainability and the environmental implications of AI technologies.
Multimodal Use
Although this case study does not focus on a specific multimodal teaching activity, it considers the institutional conditions that enable multimodal AI practices to be implemented responsibly across different disciplines.
Chris identified the Fashion programme as an example where AI-supported creative practice has been explicitly incorporated into assessment. Students are encouraged to use industry-standard AI tools during design development and storyboarding, provided that their use of AI is acknowledged appropriately within assessed work.
More broadly, Chris argued that institutional guidance should provide a flexible framework that supports discipline-specific applications of AI rather than prescribing identical approaches across all subject areas.
What did this look like in practice?
Chris described several examples illustrating how institutional guidance has informed practice across the University.
Within Fashion, assessment criteria explicitly recognise the appropriate use of AI during design development and storyboarding, with students expected to acknowledge the contribution of AI within their submissions.
He also discussed Grammarly as an example frequently considered when supporting students with additional learning needs, particularly in relation to writing support.
Another observation concerned multilingual students who may draft work in their first language before using AI to support translation into English. Chris suggested that this practice may be relatively common but also noted that students may be reluctant to disclose such use despite institutional guidance permitting acknowledged AI support.
Chris further explained that colleagues in disciplines such as Law and Education were responsible for many of the detailed classroom examples of AI implementation and suggested that these subject specialists would be better placed to discuss those practices in depth.
What was the impact?
Chris described several perceived outcomes associated with the University’s approach to AI guidance.
He identified the Fashion programme as an example of a department that has integrated AI into assessment in a transparent and discipline-appropriate manner. In his view, this has provided a useful example for wider institutional discussions concerning AI adoption.
Chris also observed differences in how students appeared to discuss AI use. Based on his experience, international students often appeared more hesitant to disclose AI use than UK students, even where institutional guidance explicitly permitted acknowledged use. He suggested that this reluctance may be associated with concerns about unintentionally breaching academic expectations.
He further noted that AI may be used by some international students to support language translation rather than to replace academic thinking. However, he emphasised that these observations were based on professional experience rather than systematic institutional evidence.
Overall, Chris described a continuing process of cultural change in which increasing openness about appropriate AI use remains an important objective of institutional guidance.
Ethical or practical considerations
Chris identified several ethical and practical considerations relating to institutional AI governance.
Cultural and linguistic inclusion
Chris suggested that some international students may be less confident than domestic students in disclosing legitimate AI use, despite guidance encouraging transparency. He described this as a potential gap between institutional messaging and students’ perceptions of acceptable practice.
Academic integrity
He acknowledged that concerns surrounding originality, authorship and appropriate AI use remain legitimate considerations within higher education. Rather than dismissing these concerns, he argued that institutional guidance should help staff and students navigate them transparently.
Sustainability
Chris explained that the current revision of the University’s AI guidance seeks to expand beyond academic integrity by considering the environmental implications of AI technologies, including the infrastructure required to support large-scale AI systems.
Evidence and evaluation
Chris noted that the University currently has limited evidence regarding how staff and students engage with institutional AI guidance. He observed that measures such as webpage views provide only limited insight into how guidance influences educational practice.
Reflections and Advice
What worked well?
Chris considered concise, practical guidance to be more effective than lengthy policy documents. He also identified embedding guidance directly within assessment documentation as a useful strategy for increasing student engagement with institutional expectations.
What would you refine?
At the time of the interview, the guidance was being revised for the forthcoming academic year. Chris explained that one objective was to integrate AI guidance more fully into routine educational practice rather than presenting it as a separate or exceptional topic.
Advice for colleagues
Chris emphasised that meaningful educational change is most likely to emerge from disciplinary communities rather than through top-down implementation. He recommended supporting colleagues with practical, subject-specific examples that demonstrate how AI can contribute to particular learning activities and assessment contexts.
Further reflections
Chris suggested that one of the continuing challenges is changing perceptions of AI within higher education. In his view, some concerns surrounding AI are shaped by broader public narratives, and he argued that demonstrating examples of thoughtful, transparent and academically appropriate AI use may help build confidence among both staff and students.
Quick Start Guide for Colleagues
Estimated time required – Ongoing. Chris described institutional guidance as an iterative process that has developed over several years and continues to be reviewed as technologies and educational practices evolve.
Digital skill level required – Not directly applicable. The focus is on institutional leadership, educational governance and curriculum design rather than technical implementation.
Common pitfalls to avoid –
- Producing guidance that is overly detailed or difficult to apply in practice.
- Assuming a single institutional approach will meet the needs of every discipline.
- Interpreting disclosure rates as a direct measure of AI use.
- Developing guidance without providing discipline-specific examples that illustrate its practical application.
Best suited for – Institutional AI working groups, faculty leadership teams, programme leaders, and committees responsible for developing guidance on the responsible integration of AI into teaching, learning and assessment.
