This year’s virtual symposium (LRNT521 Digital Learning Environments, Networks, Communities) offered a valuable opportunity to examine how various institutions and organizations leverage technology to deliver digital learning experiences and navigate the challenges that arise along the way. The experience proved especially insightful when contrasting some of the concepts encountered with the learning methods employed at the school of medicine of the University of Fribourg (UNIFR) where I work as a technical officer.
In her presentation “Rethinking Participation: Neurodiversity and Inclusion in Digital Learning Communities”, Mary Burgess (2026) introduces a few examples of neurodivergent profiles—such as autistic spectrum disorder or ADHD—regrouped under the umbrella term “Neurodiversity” and describes how curricula are audited and redesigned to accommodate these differences and improve the learners’ experience. Notably, at UNIFR, neither the Bachelor’s nor the Master’s program in medicine currently integrates neurodiversity considerations into curriculum design. The sole exception remains assessment accommodations—under the form of extended time during examinations—for students with dyslexia.
The presentation “Safeguarding Academic Integrity” by Stephen Peasley (2026)—part of his applied research project—is particularly relevant in today’s academic landscape, as the research questions he raises are universally applicable across most educational systems: How AI should be used in an education context and how can institutions regulate its use? At UNIFR, during the Master of Medicine program, enrolled students are provided access to an interactive AI-powered tutor specifically designed for customized and on-demand learning. While students are encouraged to use it without restrictions, overreliance on it could create a risk of developing passive learning habits and as well as reduce their critical thinking (Kim et al., 2025). Yet, the availability of an AI-based tool as a resource for knowledge is greatly limited—if not entirely excluded—in a clinical context where an immediate physician/patient interaction is required.
Transparency statement : AI assistance was used for grammar checking, phrasing, and final language polish.
References
Burgess, M. (2026, April 7). Rethinking Participation: Neurodiversity and Inclusion in Digital Learning Communities [Webinar]. Royal Roads University. https://bit.ly/VS2026Burgess
Kim, J., Yu, S., Detrick, R., Lin, X., & Li, N. (2025). Designing AI-powered learning: Adult learners’ expectations for curriculum and human-AI interaction. Educational Technology Research and Development, 73, 3397–3421. https://doi.org/10.1007/s11423-025-10549-z
Peasley, S. (2026, April 7). Safeguarding academic integrity [Webinar]. Royal Roads University. https://bit.ly/PeaselyMALATVS2026
Hi Florian,
I enjoyed your post, especially the part about neurodiversity and how it’s handled (or not handled) at UNIFR. The point about dyslexia accomodations being mostly limited to extra exam time really stood out to me. I actually have a close friend with dyslexia who never went after post-secondary because of how difficult high school was for him, so your post hit a bit personally. It really made me think about how many people might be missing out; not because they can’t do the work, but because the system just isn’t set up for them.
I also thought your point about AI was interesting, especially the idea of overreliance. It is such a fine line between using it as support and depending on it too much. Thanks for sharing! It gave me a lot to think about.
Hi Christina,
Your friend’s experience clearly shows the potential ramifications of non-inclusive design and highlight how important it is to take neurodiversity into consideration.
Thank you for taking the time to read my post and sharing your impressions 🙏