Key Contributions
Remember the human
Adopt a human-centred approach when using or planning to use Gen AI. Recognise that Gen AI is one of many tools at our disposal—this encourages a sense of agency over Gen AI for both staff and students, and foregrounds the importance of taking a human-centred approach when working with Gen AI. A human-centred approach places focus on using Gen AI to enhance human abilities and well-being. This is in contrast to using Gen AI to diminish the creativity of humans or to replace human roles.
Consider the task at hand and whether Gen AI is the most suitable tool. Are the capabilities of the selected Gen AI software commensurate with what it is being used for? Are you in a position to evaluate the correctness or appropriateness of any outputs? Identify points in the workflow where human input would be essential or beneficial, and work this into your plan.
Are there wider ethical implications to bear in mind before using Gen AI (e.g. environmental impacts, data privacy issues, the ethics of big data, etc.)? This should be factored into your plans for using Gen AI.
Key evidence
Be open to the opportunities that Gen AI offers (do not reject Gen AI uncritically)
Recognise the potential for Gen AI to enhance teaching and learning practices, as well as working practices generally. A blanket rejection of Gen AI risks limiting access to new learning and developmental opportunities for both students and practitioners alike.
Key evidence
Contributions which encourage practitioners not to avoid Gen AI:
Using Gen AI to generate examples and artefacts to use in learning and teaching:
Using Gen AI to facilitate reflection on processes:
Using Gen AI for writing support and development:
Using Gen AI to field queries:
An example of a flipped classroom activity using Gen AI:
A team-wide approach to exploring the potential for Gen AI to enhance working practices:
Take advantage of opportunities to support students to develop their Gen AI capabilities and literacy. Considering the trend towards mainstream adoption of Gen AI in a number of industries, avoiding Gen AI risks limiting the competitiveness of graduates.
Key evidence
A central theme of the vast majority of contributions relates to supporting students to use Gen AI.
Case study 1 describes a holistic approach to integrating Gen AI into learning and teaching.
Case studies 5 and 6 discuss how Gen AI is being used in hospitality and in counselling respectively.
Case study 9 explores the role of AI in translation, shifting practitioners from translators to post-editors.
A pertinent piece of advice from case study 8: “you will not lose your job to AI… but you may to someone who knows how to use it”.
In addition, case studies 3, 4, 7 and 10 all acknowledge GenAI as an inevitable part of future jobs/society.
Recognise that Gen AI can enable inclusivity in and of itself.
Key evidence
Contributions which mention how Gen AI enhanced inclusivity and accessibility for students:
Frame any work you do involving Gen AI in an agentic way. To reiterate an earlier point, it may be useful to view Gen AI as one of many tools at your disposal. Inform yourself about Gen AI; explore its potential and limitations as you would with any other piece of learning technology available to you.
Be cognisant of the capabilities and limitations of Gen AI (do not accept Gen AI uncritically)
Gain hands-on experience with Gen AI. Firsthand familiarity as a user will contribute to your overall understanding of the merits and limitations of Gen AI.
Key evidence
All case studies explore the hands-on experience of AI use in a variety of contexts, demonstrating the value of direct experience.
Adopt a critical approach: evaluate and reflect on the strengths and weaknesses of Gen AI and its outputs, and what implications these may have. Build in opportunities for students to do the same.
Key evidence
All the case studies describe supporting students to develop their understanding of the strengths and limitations of Gen AI.
Contributions which mention the importance of human intervention and evaluation when working with Gen AI:
Case study 2 describes a good overall attitude to have towards Gen AI: “... there is merit in thinking of Gen AI as a helpful, albeit foolish tool, one to be treated with both care and suspicion.”
Use Gen AI in a carefully considered and pedagogically-rooted way. In this sense, Gen AI is no different to any other piece of technology available to practitioners in HE.
Key evidence
Work in partnership with students
Position yourself as a facilitator, rather than necessarily an expert user, of Gen AI.
Engage in student-centred decision making when planning Gen AI in teaching: partner with students to design and create Gen AI activities and resources, or when planning the integration of Gen AI into curricula more generally. This has the added benefit of placing students in a position of agency over Gen AI in their learning.
Key evidence
Contributions which mention student-staff partnerships and collaborations when working with Gen AI:
Be clear about your own expectations around Gen AI use – and communicate them to students
Key evidence
Contributions which describe approaches taken to support students to use Gen AI including in the context of academic integrity:
Case study 9 discusses academic integrity in language learning and assessment at an age of Gen AI and web-based machine translations.
Pearl 6 gives pragmatic advice with a few key considerations to make.
Use available resources to help with identifying and articulating different aspects of Gen AI use in HE. This will equip you to provide clear guidance to your students and to facilitate conversations around Gen AI.
Key evidence
Relevant resources mentioned in this compendium include:
Pearl 20 - I.D.E.A.s framework for structuring Gen AI activities
Pearl 25 AI marking criteria
Case study 7 referenced the AI Assessment Scale by Perkins et al. (2023)
Jisc (2023) A Generative AI Primer - Artificial intelligence
QAA (2023) QAA briefs members on artificial intelligence threat to academic integrity
Russell Group principles on the use of generative AI tools in education
DfE (2023) Generative artificial intelligence (AI) in education
Consciously prioritise ethics, inclusivity and sustainability
Make informed choices when choosing which tool or software to use (e.g. be aware of paywalls and ensure equitable access for all students; take into consideration issues around data privacy and the ethics of big data).
Key evidence
Regularly monitor and evaluate the impact of Gen AI on your students. Build in formal points of evaluation when designing activities. Ideally, include opportunities for open-ended comments in student evaluation activities to identify unexpected impacts.
Key evidence
All of the case studies discuss stakeholder commentary, most of which include examples from student evaluations or feedback of using Gen AI.
Contributions mentioning the importance of human intervention with Gen AI:
Recommendations for university and sector leaders
Develop clear and explicit guidance for acceptable use of Gen AI by staff and students at your institution. Regularly review and update guidance to take into account developments in Gen AI and AI more broadly.
Key evidence
Case study 1 demonstrates how they achieved sector recognition
Case study 3 has achieved exemplary dissemination in their university and gained institutional funding for ‘educational excellence’
Make informed decisions when recommending or purchasing particular Gen AI tools or software. Include rigorous measures of ethics, data privacy, inclusivity and sustainability when evaluating options.
Key evidence
Provide time and funding for staff to engage in Gen AI-related activities and projects. These can include professional development opportunities to develop Gen AI capabilities (e.g. for teaching, research, or other work tasks) and opportunities to partner with students to innovate with Gen AI in teaching, learning and assessment.