Learning technologies research can be useful to many different groups of people. This is one of the reasons why there has been an emphasis on getting research findings into the hands of broader audiences – aka knowledge mobilization. “Broader” here refers to audiences other than researchers. Examples of audiences that might find value in learning technologies research include educators (teachers and higher ed faculty), administrators, policymakers, instructional/learning designers, edtech developers, edtech entrepreneurs, and parents.
One* of the challenges that researchers face in doing this work, is in representing and translating their research in ways in which broader audiences will find it meaningful, engaging, and useful. Some approaches that researchers have used include podcasts, YouTube videos (e.g., see our ResearchShorts series), opinion editorials, and so on. In doing this work over the years, I have learned that it’s incredibly helpful to see examples of how others translate their research for the broader public.
This is where NotebookLM, the Google AI tool which generates an audio summary of research papers, comes in. Plug in a paper, say D’Arcy Norman’s dissertation or our recently-published paper Is Artificial Intelligence in education an object or a subject?, and it generates a five-minute podcast hosted by two synthetic voices.
Some will say that the AI-generated podcast is the outcome, i.e. the knowledge dissemination vehicle: You now have a podcast for your research, and the usual caveats around accuracy, hallucinations, and biases apply.
But, there’s another, perhaps more personally meaningful way to view this: The AI-generated product is a means to an end, a way to help you think about how you might go about translating your research for broader audiences. It’s one thing to read an op ed and marvel at the ways an author frames and describes their research. It’s another to read or listen to how your own research is translated. Try it with one of your own papers, and listen closely to how the topic is introduced, explore the analogies, and pay attention to the accessible language. This is not to say that you should offload your writing or dissemination efforts to this tool. It’s to say that this is a way to see an example of how your research translated for broader audiences could be framed and described.
To be clear, I am certain that you could do better than the AI-generated podcast/summary. There will likely be inaccuracies and shortcomings in the AI-generated summary. Also, the audience isn’t specific, so if your target audience is policymakers, for example, your arguments may be different that if your audience were teachers.
Let me know what happens if you try this!
* There are many other challenges in doing this work, including systemic issues (e.g., what the institution values), whose voice is prioritized, etc etc.