GenAI tools offer students an additional 24/7 partner in their learning, while learning analytics offer solutions to personalised feedback to foster self-regulated learning. How can both work together in a single tool, within the context of a learning design?
Generative AI (genAI) tools – especially large language models (LLMs) – are becoming increasingly ubiquitous in education, providing yet another partner for students in their learning. Their 24/7 availability and conversational functions offer complementary support to teacher and peer interactions and feedback.
However, commercially available genAI tools are rarely built with student learning in mind. Especially when it comes to feedback, these open tools do not have knowledge or information about students’ progress and therefore are limited in their ability to communicate feedback to foster self-regulated learning in personalised and timely ways.
This is where learning analytics enters the picture. Learning analytics draw on information about students’ progress – and research shows that feedback that is grounded in this kind of contextual understanding is both motivating and effective for helping students self-regulate their learning. So, how does it look like to combine the conversational, always-available nature of AI, with the personalised insights from learning analytics, and what are the benefits of embedding this in a learning design?
To illustrate the possibility of this, CIC’s Lecturer Dr Lisa-Angelique Lim invited Dr Siamak Mirzaei, an educator from University of South Australia Online (UO) and researcher at the Centre for Change and Complexity in Learning (C3L), to share about Chatmate, a generative AI and learning analytics platform developed to enhance feedback and student engagement in higher education. Drawing on real-world use cases at UO and UniSA, Dr Mirzaei highlighted how Chatmate integrates with learning design to support personalised, data-informed feedback loops. In addition, his talk discussed the pedagogical rationale, technical considerations, and challenges of embedding GenAI tools within online learning environments, as well as future directions for responsible and scalable use of AI in learning design.
This invited talk was part of the subject Crunch: Learning Analytics for Performance and Improvement (Oct 2025 iteration), part of UTS’ Graduate Certificate of Learning Design and also available as a micro-credential, taught by Dr Lisa-Angelique Lim. The subject’s final Expression session invites external experts to share their work at the intersection of learning analytics and learning design. Enjoy the replay below!