Beyond Good AI: The Need for Sound Learning Theories in AIED

I nudge reevaluation of the idea that artificial intelligence for education (AIED) is merely about using artificial intelligence (AI) tools to automate understanding and responding to learning processes. Instead, I advocate for a human-centered approach to AIED that emphasizes the importance of personal connections, relationship-building, and scaffolding that goes beyond simplifying tasks to push students in their critical thinking. This approach calls for curating multimodal data from ecologically valid learning settings to train AIED systems, and maintaining flexibility in expectations around rational learner behavior when analyzing data from such systems. Given that the definition of good AIED is often discipline-specific and influenced by the underlying pedagogical models of student learning, the article calls on learning sciences researchers to integrate their complementary yet often competing theoretical lenses in rigorously studying AI-supported learning phenomena at scale.