IN THE CLASSROOM: “Adaptive Learning Systems”

Course Title: “Adaptive Learning Systems”
Taught By: Associate Professor and Faculty Director of the Learning Analytics and Artificial Intelligence Program Seiji Isotani

Description: 
This course examines the pedagogy and technology of adaptive learning systems, focusing on individualized and personalized technologies that support knowledge construction and skill development. Through classic and contemporary research in intelligent tutoring systems, as well as the latest advances in large language models, students explore how AI can personalize learning while remaining grounded in the science of how people learn. Students study both breakthrough successes and ambitious failures to understand what makes adaptive systems effective, ethical, and sustainable. Throughout the semester, students engage with foundational and cutting-edge research while designing their own intelligent tutors. They develop practical capabilities to translate learning science principles into well-designed interfaces, craft adaptive feedback and hint strategies using behavior graphs, and evaluate the impact of their designs.

Says Isotani:
“I teach this course because adaptive learning environments, particularly Intelligent Tutoring Systems (ITS), represent one of the foundational ideas behind the use of AI in education. While AI hype may center the conversation on the latest tools or models, this course demonstrates that the field stands on the shoulders of a strong and enduring body of research grounded in how people learn. I hope students leave the course with a deep understanding of the field of ITS and the broader landscape of adaptive learning technologies. In particular, I hope they move beyond the current excitement around AI and large language models and think critically and thoughtfully about how to genuinely support student learning. It is not magic. It is not about leaving students alone with a chatbot to answer all of their questions. Learning requires productive struggle, intentionality, and carefully designed strategies to support students as they interact with AI technologies.”

Student reaction:
"In 'Adaptive Learning Systems,' we explore intelligent tutoring systems, which are AI-driven programs that provide personalized, one-on-one instruction and immediate feedback to learners without human intervention.” said Sue Kwon, a doctoral student in the Learning Sciences and Technologies program. “My biggest takeaway is that integrating technology into learning isn’t just about the tool’s affordances. It requires a clear educational vision that guides the underlying domain model (what’s being taught), student model (who the learner is), and pedagogical model (how learning should happen).”