By Dr Mahbub Sarkar
Posted Tuesday, 16 June, 2026

We recently introduced “Teaching Conversations” as part of our continuous improvement of the Teaching Excellence Program (TEP). TEP is a key professional development requirement for academic staff new to Monash University. Teaching conversations are synchronous sessions, open not only to TEP participants but to the wider Monash community. They follow a similar format to our Challenge Conversations but instead of grappling with potentially unresolvable, complex challenges, they focus on more tractable questions of learning and teaching. Having said that, our first conversation covered some challenging terrain.

This first Teaching Conversation, hosted by Associate Professor Tim Fawns, focused on AI, learning, teaching and assessment. The panel featured Professor Margaret Bearman (Centre for Research in Assessment and Digital Learning – CRADLE, Deakin University), Professor Michael Henderson (Higher Education and Digital Futures, Faculty of Education,  Monash University), and Dr Paul Burgess (Senior Lecturer, Monash Faculty of Law).

This conversation was held on 15th May, 2026. The recording and my key takeaways from the conversation are below. 

AI for scaffolding learning

Early institutional responses to AI often focused on academic integrity, especially detection and the prevention of cheating. The panel encouraged us to look beyond this and consider how students are actually using these tools in their learning. 

Margaret suggested that AI could be used interactively, as a kind of partner to develop  thinking. For example, for some students, it may be helpful to reframe writing as a dynamic process, in which they engage in an ongoing conversation with AI. For her, AI is better understood as an active influence on how students learn, not as “a tool”.  

Michael extended this idea, noting that AI disrupts established boundaries of expertise. When human cognition and AI outputs become intertwined, focusing only on final outputs is no longer sufficient. Instead, we need to pay attention to how these tools support the learning process itself.

Process versus product

If generative tools can produce standard essays efficiently, relying on a final text as a proxy for student understanding becomes problematic. The panel reframed this as an opportunity to rethink assessment. 

Paul shared an example from the Faculty of Law in which students are required to use AI in their assessment. The focus shifts to their analytical process—how they formulate prompts, evaluate outputs, and refine those outputs into a valid legal argument. The aim of this approach is to realign assessment with higher-order critical reflection and evaluative judgement.

The new digital divide

Equity was a key theme, drawing on findings from the AI in Higher Education: Students and AI Project, of which Margaret, Michael and Tim are members. The project is a collaboration between Monash University, University of Queensland, Deakin University, and University of Technology Sydney. The panel highlighted that students use AI tools in very different ways (Bearman at al. 2025). For some, including students navigating English as an additional language, neurodiverse students, and those with a range of educational disadvantages, AI can support access, help with comprehension, confidence building, and workflow management.

At the same time, structural inequalities persist. Students who lack the literacy to critically evaluate outputs, or access to premium, higher-performing models, may be disadvantaged. The panel emphasised that equitable baseline access and teaching for AI literacy is increasingly part of our institutional duty of care.

Technology adoption as an internalised process

The panel emphasised that for many educators, using GenAI involves uncertainty, experimentation, and reflection. In response to audience questions about guiding students without being AI specialists, the panel reinforced the importance of having the time, autonomy, and psychological safety to explore these tools and connect them to one’s own pedagogical values and teaching philosophies. This process can be messy and non-linear, and often involves emotional labour. That’s why supportive communities of practice are so important.

The panel closed with a helpful reminder: to support students effectively, educators do not need to be technical experts. They need to support students as they work through the messy, human process of evolving their own practices alongside the technology.

Join our next Teaching Conversation

“Teaching through Tension”: practical tools for facilitating difficult conversations in the classroom. Friday 17 July, 1:00-2:00pm

References

Bearman, M., Fawns, T., Corbin, T., Henderson, M., Liang, Y., Oberg, G., … Matthews, K. E. (2026). Time, emotions and moral judgements: how university students position GenAI within their study. Higher Education Research & Development, 45(4), 884–898. https://doi.org/10.1080/07294360.2025.2580616

[The initial draft of this blog post was edited for brevity and clarity using Microsoft Copilot.]