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:
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.
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
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.
— Shibani Antonette (@ShibaniAntonett) May 14, 2024
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
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:
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) 🙂
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:
An interactive AI writing environment that offers several input modes to write in Tamil
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:
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:
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:
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?
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.
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).
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.
"Automated feedback on writing is still pretty new, so it’s not surprising that some may be sceptical. After all, how can a machine possibly understand a complex piece of writing at university level?" Read my blog post @SoLAResearch to see what AcaWriter can do #WritingAnalyticshttps://t.co/coNXYPa3iP
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.
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!
Such a good news to wake up to – Best paper award at the 21st International Conference on Artificial Intelligence in Education! Great to be part of this team 🙂 #AIED2020https://t.co/9myg3GUWsl
— Shibani Antonette (@ShibaniAntonett) July 10, 2020
Three of my journal articles got published recently, two on learning analytics/ writing analytics implementations [Learning Analytics Special Issue in The Internet and Higher Education journal], and one on a text analysis method [Educational Technology Research and Development journal]. that I worked on earlier (many years ago in fact, which just got published!).
Article 1: Educator Perspectives on Learning Analytics in Classroom Practice
The first one is predominantly qualitative in nature, based on instructor interviews of their experiences in using Learning Analytics tools such as the automated Writing feedback tool AcaWriter. It provides a practical account of implementing learning analytics in authentic classroom practice from the voices of educators. Details below:
Abstract: Failing to understand the perspectives of educators, and the constraints under which they work, is a hallmark of many educational technology innovations’ failure to achieve usage in authentic contexts, and sustained adoption. Learning Analytics (LA) is no exception, and there are increasingly recognised policy and implementation challenges in higher education for educators to integrate LA into their teaching. This paper contributes a detailed analysis of interviews with educators who introduced an automated writing feedback tool in their classrooms (triangulated with student and tutor survey data), over the course of a three-year collaboration with researchers, spanning six semesters’ teaching. It explains educators’ motivations, implementation strategies, outcomes, and challenges when using LA in authentic practice. The paper foregrounds the views of educators to support cross-fertilization between LA research and practice, and discusses the importance of cultivating educators’ and students’ agency when introducing novel, student-facing LA tools.
Article 2: Implementing Learning Analytics for Learning Impact: Taking Tools to Task
The second one led by Simon Knight provides a broader framing for how we define impact in learning analytics. It defines a model addressing the key challenges in LA implementations based on our writing analytics example. Details below:
Abstract: Learning analytics has the potential to impact student learning, at scale. Embedded in that claim are a set of assumptions and tensions around the nature of scale, impact on student learning, and the scope of infrastructure encompassed by ‘learning analytics’ as a socio-technical field. Drawing on our design experience of developing learning analytics and inducting others into its use, we present a model that we have used to address five key challenges we have encountered. In developing this model, we recommend: A focus on impact on learning through augmentation of existing practice; the centrality of tasks in implementing learning analytics for impact on learning; the commensurate centrality of learning in evaluating learning analytics; inclusion of co-design approaches in implementing learning analytics across sites; and an attention to both social and technical infrastructure.
Article 3: Identifying patterns in students’ scientific argumentation: content analysis through text mining using LDA
The third one led by Wanli Xing discusses the use of Latent Dirichlet Allocation, a text mining method to study argumentation patterns in student writing (in an unsupervised way). Details below:
Abstract: Constructing scientific arguments is an important practice for students because it helps them to make sense of data using scientific knowledge and within the conceptual and experimental boundaries of an investigation. In this study, we used a text mining method called Latent Dirichlet Allocation (LDA) to identify underlying patterns in students written scientific arguments about a complex scientific phenomenon called Albedo Effect. We further examined how identified patterns compare to existing frameworks related to explaining evidence to support claims and attributing sources of uncertainty. LDA was applied to electronically stored arguments written by 2472 students and concerning how decreases in sea ice affect global temperatures. The results indicated that each content topic identified in the explanations by the LDA— “data only,” “reasoning only,” “data and reasoning combined,” “wrong reasoning types,” and “restatement of the claim”—could be interpreted using the claim–evidence–reasoning framework. Similarly, each topic identified in the students’ uncertainty attributions— “self-evaluations,” “personal sources related to knowledge and experience,” and “scientific sources related to reasoning and data”—could be interpreted using the taxonomy of uncertainty attribution. These results indicate that LDA can serve as a tool for content analysis that can discover semantic patterns in students’ scientific argumentation in particular science domains and facilitate teachers’ providing help to students.
Keywords: text mining, latent dirichlet allocation, educational data mining, scientific argumentation
