Davide Bilardi, Teaching Fellow, Primary Care Health Sciences
Within the MSc in Global Healthcare Leadership programme, I design and deliver teaching for senior healthcare professionals that integrates global health, leadership theory, organisational studies, health policy, and research methods. Preparing these sessions requires synthesising interdisciplinary literature into coherent conceptual narratives and translating theory into applied executive learning contexts.
Previously, this process involved extensive manual drafting of teaching notes, iterative restructuring of lecture narratives, and repeated refinement before converting material into slides and discussion formats.
GenAI has supported the structural and reflective stages of this process. I use it to test the coherence of teaching notes derived from my own research and prior materials, examine logical flow, identify implicit assumptions, and stress-test conceptual clarity before finalising session design. All conceptual content remains grounded in my disciplinary expertise and scholarship; AI is used to interrogate and refine structure rather than generate original academic material. The result are more interactive sessions with the students and a sharper presentation of the key concepts.
In addition, I have used AI tools to transform curated readings and my own lecture drafts into alternative learning formats (e.g., structured audio summaries) to extend access for executive learners.
ChatGPT has acted as a structured thinking partner to:
- Test the architecture of teaching notes and session narratives developed from my own drafts.
- Generate alternative conceptual framings of arguments I have already constructed (e.g., systems lens vs managerial lens).
- Identify gaps, ambiguities, weak transitions, or oversimplifications.
- Produce scenario-based discussion prompts tailored to experienced global healthcare professionals.
- Deliberately critique my arguments and surface potential counter-positions.
My prompts typically include:
- Clear learning objectives and specification of audience (senior, international healthcare leaders).
- Instruction to adopt a critical and interdisciplinary stance with accuracy of the level of challenge I want to bring to the class.
- A request to identify blind spots, assumptions, or limitations in the argument presented.
NotebookLM has been used to:
- Convert selected readings and my lecture drafts into structured podcast-style summaries.
- Provide audio reinforcement of conceptually dense material, supporting revision and reflection outside formal contact hours.
I stress the fact that all outputs are reviewed, edited, and aligned with programme-level learning outcomes. AI-generated material is never used without academic scrutiny and adaptation.
Overall, the implementation of GenAI has redistributed effort from structural drafting toward deeper pedagogical design and critical reflection. Initial drafting of session structure is faster (approximately 30–40% reduction in early-stage drafting time), allowing greater focus on higher-order pedagogical refinement rather than mechanical restructuring. Using AI to critique and stress-test arguments reduces the risk of unexamined assumptions and improves coherence across interdisciplinary material. Audio summaries created from lecture drafts allow executive learners to revisit complex theoretical content while balancing demanding professional roles. These resources reinforce rather than replace engagement with core readings.
As a non-native English speaker working within an English-dominant academic environment, I use AI to refine clarity of expression and ensure that conceptual nuance is accurately conveyed. The intellectual ideas remain my own; the tool assists in sharpening articulation and identifying ambiguous phrasing. This supports precision without altering authorship.
GenAI should be approached as a structured thinking partner rather than a content generator. Its value lies in helping clarify arguments, surface blind spots, test assumptions, and refine structure. Users should be explicit about audience, epistemic stance, and learning objectives when prompting, and should deliberately ask the system to identify weaknesses or counter-positions. Outputs must always be critically reviewed and academically verified.
It is also important to recognise that English functions as the dominant language of global scholarship. Being born and educated in an English-speaking context confers a structural advantage within academic systems. Intellectual rigour, methodological strength, and innovative thinking, however, are not properties of any particular language. They are human capacities distributed globally.
For colleagues and students working in a second or third language, AI tools can play a meaningful role in promoting inclusivity. They can help refine clarity, strengthen academic tone, and ensure that complex ideas are expressed with precision without altering authorship or intellectual ownership. In this sense, AI can reduce linguistic barriers that may otherwise obscure strong ideas, allowing scholarly contribution to be evaluated on conceptual merit rather than fluency alone.
Used responsibly, GenAI has the potential to support more equitable participation in academic discourse. However, this requires a clear boundary: AI may assist in articulation and critical reflection, but it must never substitute for original thinking, disciplinary knowledge, or scholarly accountability.