It was great to reconnect with the Learning Analytics community at LAK25 in Dublin (before coming straight back to a busy teaching semester). LAK15 was the first conference I ever attended, and I have fond memories of this first scholarly experience. I went down memory lane after 10 years, reflecting on how it is probably the reason why I chose to do a PhD and stay in academia. My publications at LAK25 are discussed below, but also check out the full UTS line-up from the Connected Intelligence Centre (CIC) website!
In the writing analytics workshop I co-organised, our discussions spanned new methods that can inform and use generative AI for writing (tools and analytics), but we also asked some fundamental questions on the nature and evolving role of writing itself.
Rianne Conijn, Antonette Shibani, Laura Allen, Simon Buckingham Shum & Cerstin Mahlow (2025) Writing Analytics in the age of Large Language Models: Expanding horizons for assessing and designing writing with AI. In companion proceedings of the 15th International Conference on Learning Analytics & Knowledge (LAK25) pg 436-439.
Abstracts of the presented papers are on the workshop website, and you can access the organiser slides and the shared notes as well. As someone who values writing quite a bit (I’ve always found academic writing really helpful for my thinking, which enhances my own ideas as they evolve in my head), the diminishing role of writing especially for student assessments worries me (and many other folks I know, we had a great question on it at the workshop). But, having heard Prof. Rupert Wegerif talk about dialogic theory and education servicing technologies (Yes, that’s right!), I’ve come to peace with other ways of thinking. While writing is still important, it may become less essential; and maybe that’s okay. Because people think differently, writing is not the only medium for it (for example, Indigenous Knowledges have been around for centuries in various other forms). For those who prefer writing, it can continue to be of significant value, and I agree, the college essay is not dead (manifesto), we just need better, more relevant ones. I believe that we should still teach students how to write well, especially in finding their voice and being authentic (with text starting to sound very similar everywhere, I’ve personally started craving for more originality in tone of writing; Sounding professional is great, but looks like I could only take so many AI-generated emails)!
I was invited to take part in the full day Grand Challenges workshop, where workshop participants had a lot of fun identifying and voting for the grand challenges that the field of Learning Analytics should tackle in the next decade. Thanks, Sasha for the shoutout on LinkedIn.
The workshop had interesting follow-up events: an interactive poster, and an interactive panel session (keynote 3), and Prof. Simon Buckingham Shum was able to convince most (voted #1 challenge in the workshop), or me atleast, that LA should prioritize the soceity’s most pressing challenges. I’ve asked Simon how exactly we can make this happen (I can draw basic connections between learning analytics empowering learners to tackle skill challenges but I cannot extend it to how it can help save the world in this polycrisis!). He promised that he would write about it in detail, so I’m looking forward to reading it. Sidenote: I was reading Tomitsch (Head of School, TD School, UTS) and Baty’s Designing Tomorrow book and all about the climate challenges during my flight to Dublin, so it made sense – highly recommended read; it completely changed how I view Human-centred design!
Another challenge around defining core principles for LA in the rapidly changing GenAI context was something I contributed to quite a bit (stemming from the many conversations at LAK around futures and past failures of LA). There’s a lot happening around GenAI pretty quickly; its affordances are rapidly improving – but this is all the more the reason to be mindful and cautious in what we research and implement in education. We’ve contributed to some starter discussions in an upcoming editorial for the JLA Special Section on Generative AI and Learning Analytics – look out for this in the coming weeks!
At another workshop From Data to Discovery: LLMs for Qualitative Analysis in Education, Lisa did a fabulous job of presenting our team’s work (unfortunately, it conflicted with my writing analytics workshop, so I couldn’t make it). The title is self-explanatory, and the full paper has all the details:
Aneesha Bakharia, Antonette Shibani, Lisa-Angelique Lim, Trish McCluskey and Simon Buckingham Shum (2025) From Transcripts to Themes: A Trustworthy Workflow for Qualitative Analysis Using Large Language Models. In CEUR proceedings of the workshop ‘From Data to Discovery: LLMs for Qualitative Analysis in Education’. Companion proceedings of the 15th International Learning Analytics & Knowledge Conference (LAK ’25).
There’s another detailed blog post on this work, so I’ll add its link here:
We also presented a poster that expands analytics on writing for potential feedback on AI-human collaboration (student work, extended from CoauthorViz):
Antonette Shibani, Vishal Raj and Simon Buckingham Shum (2025). Towards Analytics for Self-regulated Human-AI Collaboration in Writing. In Companion proceedings of the 15th International Learning Analytics & Knowledge Conference (LAK25) pg 120-122.
There are ongoing discussions at LAK and beyond on the use of the term “collaboration” to denote human-AI partnerships (and “partnerships” as a term, too but we mean partners in cognition like this 1991 paper). Various disciplines have always interpreted terminologies differently (especially computer science, which makes loose analogies with human psychology – e.g., hallucinations), and it is an open question if we can even agree on how we must perceive terms. Maybe we need better definitions for what we mean by regulation, hybrid, augmenting, or just human-AI interaction? More on this is likely to come later!
I also chaired two sessions at LAK (one where the best papers from sister conferences were invited to present, and another on learning design). The Q&A parts were super interesting there and throughout LAK. We could really see the diversity of the audience’s backgrounds here – the constant switch between statistics, practitioners, LA, and LLMs is mind-boggling!
And more photo dumps because… why not 🙂
Team UTS
