In spring 2024, when over 3,000 math teachers were asked “How frequently have you used AI in your mathematics teaching this school year?”, about 82% responded, “Never.” (see Dan Meyer’s article). Does this mean that math teachers are not interested in learning about how AI can help them in the classroom? Does it mean that the professional learning about AI for math teachers is not very good? Does it mean that other areas of research are more advanced technologically than math education? I would have to say the answer to these questions is that “it’s complicated.” The ways in which other fields may be embracing AI may seem more exciting and invested, but math teachers are definitely talking about AI. However, the considerations that math teachers need to contemplate are not necessarily the same types of considerations that other professionals need to be thinking about. Most educators think very seriously about the effects that any change will have on their students’ learning. What are the ramifications of making this huge change in students’ learning process? Teachers can be very set in their ways, yes. What has worked for them for years can be very difficult to adapt to new developments in educational practice. However, I believe they do this out of a desire to do right by their students. Changing something too quickly can have huge affects down the line for learners. In my mind, math teachers and administrators are just waiting until some of their big questions are answered. What are these big questions? Well, here are some that come to mind.
One of the issues of using AI that comes to mind most for people is the question of “cheating.” Because learning mathematics has been limited in most people’s minds to the processes and procedures of algebraic manipulation, a lot of teachers find it difficult not to quickly scrutinize AI use as the AI “doing the math” for students. Alternatively, we could look at math learning as different from these procedures and think more about how AI could be even more helpful. In order to move in this direction, some things must change to allow math teachers to be creative with AI.
Curriculum Review
Although Common Core Math Standards were published only 14 years ago, it is clear that there needs to be a revisiting of those standards with AI in mind. A review of the current K-12 math curriculum in any state must occur, especially our algebra-heavy high school curriculum. We need to make it so that what students are being asked to learn is not how to factor, but why you might factor a polynomial. Instead of focusing on how to graph trigonometric functions, questions should focus on what affects the graph and why. AI can give students examples from which they can generalize about ideas instead of practicing dozens of examples that they should be mastering. There is so much that AI can do for students (and eventually employees) that can now be removed from the curriculum to focus on more critical thinking and higher-order learning skills.
Classroom Assessment
AI should not be used as a teaching tool until teachers can find ways to authentically assess these new skills – perhaps even with AI. We can no longer assess by giving a traditional test, where the goal is for students to show mastery by doing 5-6 problems that they have already shown that they can do in class. Is mastery really what we are looking for in the age of AI? This is not to say that students should not have number sense and be able to do some calculations in their head. AI will soon replace the basic calculator and be on everyone’s phone. There is room for both a need for basic understanding and skills on how best to use AI.
Culture of Standardized Testing
What has been on my mind recently, is the fact that math teachers are expected to find ways to teach with AI in the classroom, while many external standardized assessments have not changed. It is not possible for math teachers to consider what is being done in the classroom and ignore the expectations of a test on which much of their future rides. I am the last educator to tote the benefits of focusing on standardized tests or “teaching to the test,” however we cannot overlook the dependence of college admissions teams on these scores. Until the college admissions process faces the effects of AI, the trickle-down effects to math teachers are huge.