What Can Computer Science Educators Learn From the Failures of Top-Down Pedagogy?

While educational researchers in various disciplines are grappling with how to develop policies and pedagogical approaches that address the use of generative artificial intelligence, the challenge is particularly complex in computer science education where the new technology is changing the core of the field. In this paper, we take a look at the pedagogy of other subjects with a longer history than computer science and a more extensive body of educational research to collect insights on how this challenge can be met. We begin by drawing from recent neurological research to find domains that share cognitive commonalities with computer programming and then build upon comparisons that others have made to literacy and mathematics education. We then consider how the "reading wars" and "math wars" have shaped these fields, which we see as conflicts between less effective top-down pedagogy and more effective bottom-up pedagogy, and reflect on what would be comparable approaches in teaching computing. We find that approaches that make heavy use of large language models without teaching fundamentals can be compared to the top-down pedagogy of reading and mathematics and are likely to be ineffective on their own. Therefore, we advise against the exclusive use of such approaches with novices. However, we also acknowledge that the social science surrounding computer science education is complex and that effectiveness only tells a part of the story, with other factors such as engagement, motivation and social dynamics also being important.