 |
| I would venture to guess that many of us find ourselves uttering the word “breakdown”—in a variety of negative senses—regularly these days.
But when we’re talking about teaching, breaking things down is a fundamental and positive practice. Breaking down learning goals, reading analyses, word problems, research processes—all of these forms of academic work require working through a set of steps that we have learned over time but that may not be obvious to our students. Many of us tend to think about learning goals in the big picture—for example, the ability to “think like an X [choose your discipline]” or to “analyze and interpret data” or “communicate effectively.” These broadly stated goals lie at the core of a liberal arts education. They are one reason that a colleague at the January 12 AI and Learning Goals workshop was able to observe that faculty from across departments and divisions have very similar goals and concerns, expressed in the same ways. At the same time, the precise steps towards each goal—including the sources examined, the standard questions asked, and the specific tools applied for analysis and interpretation—vary across our disciplines. A recurring theme emerging in our January workshops was the increasing importance of breaking down the learning process for students navigating a world of AI, much of which is thrown at them through social media and marketing with lots of hype about optimization, efficiency, and personalization, but with little transparency about how the technology works or what questions they might want to be asking. For some students, our classrooms may be the only spaces where they are actively invited to step back and think holistically about how they want and/or need to engage with AI. A key ingredient in that learning environment is our ability to help our students break processes down and pull ideas apart in order to build their own knowledge, skills, and understanding. |