|
Getting your Trinity Audio player ready...
|
No, this isn’t about making an analogy between AI in the classroom and calculators. It is about Wolfram|Alpha.
If you are not involved in a discipline that relies on mathematical computations, you may not be familiar with Wolfram|Alpha (W|A).
Released in 2009, W|A is an AI powered computational engine for math that has expanded beyond math in recent years to cover other subject areas. Indeed, until the entire furor over generative AI and academic integrity popped up 6 months ago, I had completely forgotten about W|A and the similar hornets nest of cheating controversy it stirred up back in 2009 when it was released that feels very similar to what we are hearing today about generative tools like ChatGPT.
This past week I was invited to speak for a few minutes at the BC math & stats articulation group meeting, an annual gathering of math and stats instructors and department heads representing most of the public higher education institutions in the province. The speaker who presented just before me from another provincial wide system organization that works across all institutions made a comment in their update that generative AI is the hot topic that everyone is talking about these days, especially around academic integrity. This was a virtual meeting and, as the speaker was talking about the conversations he was hearing within the post-sec system about AI and AI, a math instructor typed a pithy comment in the chat something along the lines of “This isn’t new for math. Wolfram|Alpha.”
Indeed, in the early days of its existence there was so much conversation around whether students using Wolfram|Alpha to do math was cheating that the co-founder of Wolfram|Alpha, Conrad Wolfram, tackled the topic in a TEDx talk (remember those?) in 2009 with the title “Is It Cheating to Use Wolfram|Alpha for Homework?”. In it Wolfram argues that teaching students how to compute is not the same as teaching them math, and that tools like W|A actually make it possible for student to engage with math in a much more conceptual and (he argues) more authentic, “real world” way. In his talk he also argues that the way math is assessed needs to change, echoing much of what we are hearing today about the need for changing assessment practices.
Given that this talk is almost 15 years old, it will likely feel very relevant for many involved in teaching and learning who are experiencing the impact of generative AI in the classroom at the same scale as math instructors faced 15 years ago. If you want to get some insight into how AI has impacted teaching & learning practices, I suspect the math department would be a good place to start looking.
