Join us on Bluesky for #LTHEchat on Wednesday 11th March at 8pm GMT with guest Rachel Forsyth to discuss GenAI in higher education.
In many conversations about generative AI in higher education, the products themselves take centre stage. It is very hard to think about services based on Large Language Models (LLM) without getting into discussions about big tech, data protection, ethical software development and environmental impact. Of course, those discussions are essential, especially when public money is being spent on these products and services, but for many of us they have to run in parallel with the reality that there are lot of these things around already, and we have to find ways to talk about them which help us to explain our positions more clearly. We can’t look away, even if we really, really want to. So in this #LTHEchat, let’s try, just for an hour, to focus on what we value in higher education and consider the impact of LLM-based services on four core principles: student-centeredness, trust, relevance, and agency.
Sam Illingworth and I wrote GenAI in Higher Education: Redefining Teaching and Learning (open access from Bloomsbury) to support education-led discussions about these products, and we try to come back to these four principles in every section, whether we are talking about ethics, student engagement, assessment or teacher use of GenAI.
Student-Centeredness focuses on how students can still engage with valued forms of knowledge, quality, standards, and expertise in higher education. If GenAI can summarise a text or propose ideas, does that mean students no longer need to develop those skills? How do we ensure that learning remains rooted in meaningful disciplinary thinking, rather than in polished language extrusion (Bender, 2025)? How do we assess what students have actually learned (Fawns et al, 2026), when GenAI products may mask their authentic work? Can some LLM-based products reduce some barriers to traditional learning? Seeing GenAI through a student-centred lens forces us to explain to students what we think (and feel) about its impacts on them and their learning, and why we make certain decisions about assessment.
Trust becomes especially important in a moment when staff and students alike may feel unsettled. Trust is built not through surveillance or suspicion, but through environments where students feel comfortable taking risks, admitting uncertainty, and trying things out. If GenAI destabilises some of our familiar assessment practices – which is by no means guaranteed, since many universities are returning to the familiar examination halls – perhaps it is also an invitation to rethink how we communicate expectations, and how we reassure students that learning is not simply about getting things “right” and offering up a perfect essay.
Relevance encourages us to connect learning to students’ lives, interests and social contexts. GenAI is already present in many workplaces, creative practices and everyday tools. Pretending it doesn’t exist risks making higher education feel disconnected or outdated. But that in itself isn’t a reason for adoption. A relevance-first approach asks: How can we help students situate GenAI within their discipline? How can we support them to evaluate its limitations, biases and consequences? How do we keep the curriculum anchored in the real worlds students inhabit whilst explaining why they need to learn to do some things themselves, even if that learning is uncomfortable or feels like drudgery at times?
Finally, Agency reminds us that neither staff nor students should be passive recipients of technological change. Both groups need autonomy and voice in shaping how GenAI is explored, questioned or resisted. For students, this might mean being transparent about how and when they use GenAI products, understanding that digital products and services may not always be risk-free, developing their own judgement about when it supports their learning and when it undermines it. For staff, agency includes the freedom to make pedagogical decisions that align with their values, not simply with technological trends, but also to consider whether some GenAI products and services might actually improve something in their practices.
Seeing GenAI through these four pillars gives us all some agency over our actions in relation to GenAI. Rather than asking whether students are “using AI correctly”, we might ask whether our practices enable them to think critically, act ethically and engage deeply with their disciplines. Is there any way that GenAI might help us to focus on creating spaces where human judgement, curiosity and connection still come first.
Our recent research on trust-building between teachers and students (Felten et al, 2024; Glessmer et al, 2025; Forsyth et al, 2025) shows that the responses of teachers, librarians, learning developers and others who have direct contact with students matter very much to them: they need and want to hear our critical and informed responses to the real challenges and opportunities students see in GenAI. We can’t look away.
References
Bender, E. M. (2025). We do not have to accept AI (much less GenAI) as inevitable in education. AI and the future of education, 41.
Fawns, T., Boud, D., & Dawson, P. (2026). Identifying what our students have learned: a framework for practical assessment validation. Assessment & Evaluation in Higher Education, 1–17. https://doi.org/10.1080/02602938.2026.2620053
Felten, P., Forsyth, R., & Sutherland, K. (2023). Building Trust in the Classroom: A Conceptual Model for Teachers, Scholars, and Academic Developers in Higher Education. Teaching & Learning Inquiry: The ISSOTL Journal, 11. https://journalhosting.ucalgary.ca/index.php/TLI/article/view/77047
Forsyth, R., & Glessmer, M. (2025). How teachers build trust with students in the presence of GenAI Proceedings LTH: s 13: e Pedagogiska Inspirationskonferens, 4 december 2025, Lund. https://www.lth.se/fileadmin/cee/genombrottet/konferens2025/C1_Forsyth_Glessmer.pdf
Glessmer, M. S., Persson, P., & Forsyth, R. (2025). Engineering students trust teachers who ask, listen, and respond. International Journal for Academic Development, 30(1), 106–119. https://doi.org/10.1080/1360144X.2024.2438224
Illingworth, S., & Forsyth, R. (2026). GenAI in Higher Education: Redefining Teaching and Learning. Bloomsbury. https://www.bloomsbury.com/uk/genai-in-higher-education-9781350535787/
Speaker Bio
Rachel Forsyth is a senior educational developer at Lund University. She works in a unit which focuses on digital tools in education, and is mainly involved with pedagogical development, assessment design, and academic trust-building. More relevantly, this is her third time facilitating #LTHEChat but she is no quicker at typing than she was in 2014, unfortunately.
#LTHEchat 