Author: Shibani

LAK25 at Dublin – Publications and Reflections

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:

Theme Explorer: LLM-augmented Inductive Coding

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

The Long Room

Book of Kells Experience

Generative AI – Rethinking Learning Futures Forum

In July, I was invited to speak at an Australia India InnovEd Forum on Generative AI – Rethinking Learning Futures hosted by the Australia India Institute.

Speakers for the Australia India InnovEd Forum on Generative AI

The forum ran as an online webinar where speakers from India and Australia shared their thoughts on the evolving role of generative AI tools such as ChatGPT in higher education. The full recording of the session is below, and a transcript is also available. As part of the InnovEd series, a detailed report was also published, summarising insights from all the events held during the year.

I am delighted to see emerging initiatives that promote and foster collaboration between India and Australia, and I’m glad to be a small part of it. I was also previously selected as one of the AIRS Fellows in the Australia India Research Students Fellowship program that supported short-term research exchange of scholars between the two countries, and have published with Indian colleagues. I hope to continue my research collaboration with colleagues from various institutions in India on related topics.

CHI’24 research publications

I attended the prestigious Human-Computer Interaction (HCI) conference CHI’24 at Hawaii, Honolulu in May 2024. While I’m quite familiar with the field of HCI, it was my first time attending the conference because it is much wider than my main area of research (Learning Analytics, AI in education, and Writing Analytics). The sheer scale of the conference (~2K to 3K attendees) and the broad range of topics it covers (check out this full program) is almost impossible to fully grasp!

TLDR; Go to the end for the list of paper from CHI’24.

My personal highlight was the Intelligent Writing Assistants Workshop, which was running for the third time at CHI, organized by a bunch of fun people who are all super keen about researching the use of AI to assist writing. Picture from our workshop below (thanks, Theimo, for the LinkedIn post)

Pictured: Participants of the Intelligent Writing Assistants CHI’24 workshop at the end of the session

The workshop had many mini presentations on the overall theme of Dark Sides: Envisioning, Understanding, and Preventing Harmful Effects of Writing Assistants. I presented my work with Prof. Simon Buckingham Shum on AI-Assisted Writing in Education: Ecosystem Risks and Mitigations, where we examined key factors (in the broader socio-technical ecosystem which are often hidden) that need consideration for implementing AI writing assistants at scale in educational contexts.

This was actually a deep dive into the Ecosystem aspect of a larger piece of work we presented at CHI on A Design Space for Intelligent and Interactive Writing Assistants. The full design space from our full paper mapped the space of intelligent writing assistants reviewing 115 papers from HCI and NLP, with a team of 36 authors, led by Mina Lee.

Figure: Design space for intelligent and interactive writing assistants consisting of five key aspects—task, user, technology, interaction, and ecosystem from our full paper.

An interactive tool is also presented to explore the literature in detail.

I also had a late-breaking work poster presentation on Critical Interaction with AI on Written Assessment (I have a seperate post about it!) where we explored how students engaged with generative AI tools like ChatGPT for their writing tasks, and if they were able to navigate this interaction critically.

A cherished memory to hold on to was also the time I spent with my friend Vanessa, who is currently a Research Fellow at Monash university during this trip in Hawaii. Vanessa and I started our PhD together at th Connected Intelligence Centre at UTS ~8 years ago, and it was really nice to catch up after a long time (along with few others). I had also just visited Monash university’s CoLAM a week before for a talk and meeting fellow Learning Analytics researchers, hosted by her and Roberto. The group do interesting work in Learning Analytics that is worth checking out.

6 years apart… On the left: Vanessa and I in 2018 while attending AIED/ ICLS 2018 in London; On the right: Us while attending CHI in 2024 in Hawaii.

TLDR -> Research publications:

Here are all the papers from the work we presented at CHI’24:

Mina Lee, Katy Ilonka Gero, John Joon Young Chung, Simon Buckingham Shum, Vipul Raheja, Hua Shen, Subhashini Venugopalan, Thiemo Wambsganss, David Zhou, Emad A. Alghamdi, Tal August, Avinash Bhat, Madiha Zahrah Choksi, Senjuti Dutta, Jin L.C. Guo, Md Naimul Hoque, Yewon Kim, Simon Knight, Seyed Parsa Neshaei, Agnia Sergeyuk, Antonette Shibani, Disha Shrivastava, Lila Shroff, Jessi Stark, Sarah Sterman, Sitong Wang, Antoine Bosselut, Daniel Buschek, Joseph Chee Chang, Sherol Chen, Max Kreminski, Joonsuk Park, and Roy Pea, Eugenia H. Rho, Shannon Zejiang Shen, Pao Siangliulue. 2024. A Design Space for Intelligent and Interactive Writing Assistants. In Proceedings
of the CHI Conference on Human Factors in Computing Systems (CHI ’24),
May 11–16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 33 pages.
https://doi.org/10.1145/3613904.3642697

Antonette Shibani, Simon Knight, Kirsty Kitto, Ajanie Karunanayake, Simon Buckingham Shum (2024). Untangling Critical Interaction with AI in Students’ Written Assessment. Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI ’24), May 11-16, 2024, Honolulu, HI, USA. doi.org/10.1145/3613905.3651083

Antonette Shibani & Simon Buckingham Shum (2024). AI-Assisted Writing in Education: Ecosystem Risks and Mitigations. In The Third Workshop on Intelligent and Interactive Writing Assistants @ CHI ’24, Honolulu, HI, USA. https://arxiv.org/abs/2404.10281

New publication: Untangling Critical Interaction with AI

I’ve had a longstanding interest in exploring how students engage critically with automated feedback and develop their AI literacy. In our LAK22 paper, we argued why it is so important that we develop these skills in learners. There is a heightened necessity in today’s educational landscape for learners in the age of generative AI (Gen AI) to engage with AI critically.

Our upcoming CHI publication investigates the fundamental question: Why do students engage with Gen AI for their writing tasks, and how can they navigate this interaction critically? In our paper, we define in concrete terms and stages how criticality can manifest when students write with ChatGPT support. We draw from theory and examples in empirical data (which are still unbelievably scarce in the literature) to understand and expand the notion of critical interaction with AI.

A pre-print version is available for download on Arxiv [PDF]. Full citation below:

Antonette Shibani, Simon Knight, Kirsty Kitto, Ajanie Karunanayake, Simon Buckingham Shum (2024). Untangling Critical Interaction with AI in Students’ Written Assessment. Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI ’24), May 11-16, 2024, Honolulu, HI, USA. Pre-print: https://arxiv.org/abs/2404.06955 

A short video presentation gives the gist of the paper [Follow along with the transcript]

Recognition for Teaching

[Originally posted on LinkedIn]

On Wednesday, I received the ‘2023 Learning and Teaching Citation’ from the Vice-Chancellor of the University of Technology Sydney (UTS) for nurturing well-rounded data science professionals. UTS citations and awards recognise significant and sustained contributions to student learning, student engagement, and the student experience by individuals or teams.



Earlier this week, I also received the ‘Impactful Educator Award‘ in the Partnerships Builder category that recognises educators who build collaboration with external stakeholders into their learning programmes and use elements of innovation to create a demonstrable impact on their students.

I feel deeply honoured and humbled to be recognised for my teaching initiatives and to share the stage with many esteemed educators who are making a positive impact on students. It holds a special place in my heart as a nod to a legacy that I once thought I would not follow. Growing up in a family of educators in India – including both my grandmothers, my mum, and several other relatives – I was taught from a young age how education can uplift individuals and societies. These values were deeply instilled in me, shaping my understanding and appreciation of education, which influenced my research direction. However, interestingly, teaching was never on the cards for me as I trained to be a computer engineer.

The journey from a computer engineer to an educator has been unexpected yet profoundly rewarding for me and comes as a testament to the unpredictable paths our passions can take us. My sincere thanks to colleagues at UTS TD School, mentors, family, and, most importantly, my students, whose enthusiasm and eagerness to learn inspire me. I value every little interaction I’ve had with my students over the last few years, and I hope to have touched some of their lives in a way. I learn from them as much as they learn from me!

The recognition motivates me to continue striving for excellence, innovation, and educational impact. Let’s keep pushing the boundaries of what is possible in education, for the betterment of our students and the future they will create. Thank you once again for this incredible honor. It is a milestone I will cherish deeply, reminding me of the journey so far and the exciting road ahead!

P.S: It was also a lovely memory to share with my family who joined me in the celebration at UTS, and an appreciation for my culture through my attire (a saree, traditionally worn by women in India) 🙂

Tamil Co-Writer: Inclusive AI for writing support

Next week, I’m presenting my work in the First workshop on
Generative AI for Learning Analytics (GenAI-LA) at the 14th International Conference on Learning Analytics and Knowledge LAK 2024:

Antonette Shibani, Faerie Mattins, Srivarshan Selvaraj, Ratnavel Rajalakshmi & Gnana Bharathy (2024) Tamil Co-Writer: Towards inclusive use of generative AI for writing support. In Joint Proceedings of LAK 2024 Workshops, co-located with 14th International Conference on Learning Analytics and Knowledge (LAK 2024), Kyoto, Japan, March 18-22, 2024.

With colleagues in India, we developed Tamil Co-Writer, a GenAI-supported writing tool that offers AI suggestions for writing in the regional Indian language Tamil (which is my first language). The majority of AI-based writing assistants are created for English language users and do not address the needs of linguistically diverse groups of learners. Catering to languages typically under-represented in NLP is important in the generative AI era for the inclusive use of AI for learner support. Combined with analytics on AI usage, the tool can offer writers improved productivity and a chance to reflect on their optimal/sub-optimal collaborations with AI.

The tool combined the following elements:

  1. An interactive AI writing environment that offers several input modes to write in Tamil
  2. Analytics of writer’s AI interaction in the session for reflection (See post on CoAuthorViz for details, and related paper here)

A short video summarising the key insights from the paper is below:

Understanding human-AI collaboration in writing (CoAuthorViz)

Generative AI (GenAI) has captured global attention since ChatGPT was publicly released in November 2022. The remarkable capabilities of AI have sparked a myriad of discussions around its vast potential, ethical considerations, and transformative impact across diverse sectors, including education. In particular, how humans can learn to work with AI to augment their intelligence rather than undermine it greatly interests many communities.

My own interest in writing research led me to explore human-AI partnerships for writing. We are not very far from using generative AI technologies in everyday writing when co-pilots become the norm rather than an exception. It is possible that a ubiquitous tool like Microsoft Word that many use as their preferred platform for digital writing comes with AI support as an essential feature (and early research shows how people are imagining these) for improved productivity. But at what cost?

In our recent full paper, we explored an analytic approach to study writers’ support seeking behaviour and dependence on AI in a co-writing environment:

Antonette Shibani, Ratnavel Rajalakshmi, Srivarshan Selvaraj, Faerie Mattins, Simon Knight (2023). Visual representation of co-authorship with GPT-3: Studying human-machine interaction for effective writing. In M. Feng, T. K¨aser, and P. Talukdar, editors, Proceedings of the 16th International Conference on Educational Data Mining, pages 183–193, Bengaluru, India, July 2023. International Educational Data Mining Society [PDF].

Using keystroke data from the interactive writing environment CoAuthor powered by GPT-3, we developed CoAuthorViz (See example figure below) to characterize writer interaction with AI feedback. ‘CoAuthorViz’ captured key constructs such as the writer incorporating a GPT-3 suggested text as is (GPT-3 suggestion selection), the writer not incorporating a GPT-3 suggestion
(Empty GPT-3 call), the writer modifying the suggested text (GPT-3 suggestion modification), and the writer’s own writing (user text addition). We demonstrated how such visualizations (and associated metrics) help characterise varied levels of AI interaction in writing from low to high dependency on AI.

Figure: CoAuthorViz legend and three samples of AI-assisted writing (squares denote writer written text, and triangles denote AI suggested text)

Full details of the work can be found in the resources below:

Several complex questions are yet to be answered:

  • Is autonomy (self-writing, without AI support) preferable to better quality writing (with AI support)?
  • As AI becomes embedded into our everyday writing, do we lose our own writing skills? And if so, is that of concern, or will writing become one of those outdated skills in the future that AI can do much better than humans?
  • Do we lose our ‘uniquely human’ attributes if we continue to write with AI?
  • What is an acceptable use of AI in writing that still lets you think? (We know by writing we think more clearly; would an AI tool providing the first draft restrict our thinking?)
  • What knowledge and skills do writers need to use AI tools appropriately?

Edit: If you want to delve into the topic further, here’s an intriguing article that imagines how writing might look in the future: https://simon.buckinghamshum.net/2023/03/the-writing-synth-hypothesis/

Questioning Learning Analytics – Cultivating critical engagement (LAK’22)

Gist of LAK 22 paper

Our full research paper has been nominated for Best Paper at the prestigious Learning Analytics and Knowledge (LAK) Conference:

Antonette Shibani, Simon Knight and Simon Buckingham Shum (2022, Forthcoming). Questioning learning analytics? Cultivating critical engagement as student automated feedback literacy. [BEST RESEARCH PAPER NOMINEE] The 12th International Learning Analytics & Knowledge Conference (LAK ’22).

Here’s the gist of what the paper talks about:

  • Learning Analytics (LA) still requires substantive evidence for outcomes of impact in educational practice. A human-centered approach can bring about better uptake of LA.
  • We need critical engagement and interaction with LA to help tackle issues ranging from black-boxing, imperfect analytics, and the lack of explainability of algorithms and artificial intelligence systems, to the required relevant skills and capabilities of LA users when dealing with such advanced technologies.
  • Students must be able to, and should be encouraged to, question analytics in student-facing LA systems as Critical engagement is a metacognitive capacity that both demonstrates and builds student understanding.
  • This puts the power back to users and empowers them with agency when using LA.
  • Critical engagement with LA should be facilitated with careful design for learning; we provide an example case with automated writing feedback – see the paper for details on what the design involved.
  • We show empirical data and findings from student annotations of automated feedback from AcaWriter, where we want them to develop their automated feedback literacy.

The full paper is available for download at this link: [Author accepted manuscript pdf].

This paper was the hardest for me to write personally since I was running on 2-3 hours of sleep right after joining work part-time following my maternity leave. Super stoked to hear about the best paper nomination, as my work as a new mum paid off. Good to be back at work while also taking care of the little bubba 🙂 Thanks to my co-authors for accommodating my writing request really close to the deadline!

Also, workshops coming up in LAK22:

  • Antonette Shibani, Andrew Gibson, Simon Knight, Philip H Winne, Diane Litman (2022, Forthcoming). Writing Analytics for higher-order thinking skills. Accepted workshop at The 12th International Learning Analytics & Knowledge Conference (LAK ’22).
  • Yi-Shan Tsai, Melanie Peffer, Antonette Shibani, Isabel Hilliger, Bodong Chen, Yizhou Fan, Rogers Kaliisa, Nia Dowell and Simon Knight (2022, Forthcoming). Writing for Publication: Engaging Your Audience. Accepted workshop at The 12th International Learning Analytics & Knowledge Conference (LAK ’22).

Automated Writing Feedback in AcaWriter

You might be familiar with my research in the field of Writing Analytics, particularly Automated Writing Feedback during my PhD and beyond. The work is based off an automated feedback tool called AcaWriter (previously called Automated Writing Analytics/ AWA) which we developed at the Connected Intelligence Centre, University of Technology Sydney.

Recently we have come up with resources to spread the word and introduce the tool to anyone who wants to learn more. First is an introductory blog post I wrote for the Society for Learning Analytics Research (SoLAR) Nexus publication. You can access the full blog post here: https://www.solaresearch.org/2020/11/acawriter-designing-automated-feedback-on-writing-that-teachers-and-students-trust/

We also ran a 2 hour long workshop online as part of a LALN event to add more detail and resources for others to participate. Details are here: http://wa.utscic.edu.au/events/laln-2020-workshop/

Video recording from the event is available for replay:

Learn more: https://cic.uts.edu.au/tools/awa/

Automated Revision Graphs – AIED 2020

I’ve recently had my writing analytics work published at the 21st international conference on artificial intelligence in education (AIED 2020) where the theme was “Augmented Intelligence to Empower Education”. It is a short paper describing a text analysis and visualisation method to study revisions. It introduced ‘Automated Revision Graphs’ to study revisions in short texts at a sentence level by visualising text as graph, with open source code.

Shibani A. (2020) Constructing Automated Revision Graphs: A Novel Visualization Technique to Study Student Writing. In: Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12164. Springer, Cham. [pdf] https://doi.org/10.1007/978-3-030-52240-7_52

I did a short introductory video for the conference, which can be viewed below:

I also had another paper I co-authored on multi-modal learning analytics lead by Roberto Martinez, which received the best paper award in the conference. The main contribution of the paper is a set of conceptual mappings from x-y positional data (captured from sensors) to meaningful measurable constructs in physical classroom movements, grounded in the theory of Spatial Pedagogy. Great effort by the team!

Details of the second paper can be found here:

Martinez-Maldonado R., Echeverria V., Schulte J., Shibani A., Mangaroska K., Buckingham Shum S. (2020) Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching. In: Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163. Springer, Cham. [pdf] https://doi.org/10.1007/978-3-030-52237-7_29