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Mon-Lin Monica Ko is an Assistant Research Professor at the Institute of Cognitive Science at CU Boulder and a team lead for Strand 3 at iSAT. Her work focuses on promoting and studying classroom interactions that support students' engagement in disciplinary practices. At iSAT, she investigates how inclusive co-design processes can empower teachers and students with diverse identities to better understand how AI learning technologies can be used for good in their schools and com­munities. In addition to her role on iSAT, Ko is also currently a Research Assistant Professor at the Learning Sciences Research Institute (LSRI) at the University of Illinois Chicago. 

Over the last couple of years, the topic of AI has catapulted into the public sphere, fueled in part by the release of .  Numerous headlines that we encounter focus on the doom and gloom of AI. Others boast its amazing capacity to mimic human intelligence. In the U.S., we’ve seen many school districts BAN the use of AI. The explosion of AI-powered tools also surfaces  for learning scientists, curriculum developers, and teacher educators such as: “what do we think students need to learn in order to use AI safely, ethically, and responsibly ...and what does it look like for teachers to model these practices?” Clearly, we are at an interesting juncture in conceptualizing exactly what it is that students need to learn about AI.

Our iSAT team has been aiming to prepare students to be critical consumers and builders of ethical AI systems. Rather than simply supporting students in learning about AI systems, we want them to interrogate how these systems work, what purposes they serve (and for whom), and what societal impacts these technologies can and should have.  This vision for student learning requires us to examine and reflect on what it is that students need to know and be able to do when it comes to AI. It also requires us to think about how it is that we position students as the ones who envision, build and critique the AI systems of the future. 

Moving toward this vision for student learning has led us to encounter what we’re calling the triple innovation challenge. First, AI is an emerging field and there are no standards yet for what should be taught – and when – in K-12 classrooms. Teachers are also new to AI and are learning about it as they are teaching it in their classrooms. Second, our work in schools is part of a movement that seeks to promote student-centered learning through curriculum-based professional learning. All of our units are created using a so-called . This, too, is novel to teachers. Our third challenge involves integrating our AI partners into existing socio-technical infrastructures of our partner schools and districts, each of whom have different capacities for integrating these emerging technologies into classrooms. These three innovation challenges present both challenges and opportunities to our work. 

Over the past four years, we’ve been partnering with community organizations, teachers, and school districts to figure out how we can address this triple innovation challenge. These partnerships have led to modifications and revisions to our curriculum materials. Our conversations with teachers and students have resulted in a deeper understanding of how we can better integrate our AI partners into curricular routines. Our work inside classrooms has sharpened the need to better understand what teacher-student-AI partner interaction can and should look like, and how these interactions can best support collaborative learning inside classrooms. 

What we’ve learned from our repeated engagements with various stakeholders is that we need to cultivate a dynamic learning environment to sufficiently prepare students to understand, critique and build ethical AI systems. We need rich curriculum materials that tackle questions and phenomena that students are interested in – something that really invites and builds on their everyday experiences. We also need to create AI partners that are not just a black box – but something that can be interrogated and questioned and opened up for inquiry. We also need to support teachers in enacting these AI-partner embedded curriculum materials – and provide ample learning opportunities for teachers to do their own learning about AI. Lastly, we need to leverage and build on student experiences in order to support their own learning. 

This is really important – the AI partner we’ve developed can’t stand on its own to reach our goals for students. We need to think about how all these pieces fit together to enable meaningful student learning. As we look to Year 5 of the iSAT project, we are continuing to rely on our work inside classrooms, alongside teachers and students, to better understand how we can create this dynamic learning environment.