Theory-driven design of AIED systems for enhanced interaction and problem-solving
Susanne P. Lajoie, Shan Li
Abstract
Successful AI learning environments are driven by effective designs for learning that incorporate what we know about how learners learn best. Theory-driven designs about the cognitive, metacognitive, affective, motivational, and behavioral components of learning need to be considered jointly in designing effective AI interfaces for learning and instruction. We provide examples of AI interfaces that embody specific theories. We discuss the benefits of AI-supported interfaces along with AI developments that enable new functionalities and modalities of AIED systems. We conclude with suggestions for future interface considerations.
Topics & Concepts
ModalitiesHuman–computer interactionComputer scienceInterface (matter)CognitionLearning theoryMetacognitionInterface designCognitive scienceArtificial intelligencePsychologyCognitive psychologyNeuroscienceMaximum bubble pressure methodBubbleSocial scienceSociologyParallel computingIntelligent Tutoring Systems and Adaptive LearningExplainable Artificial Intelligence (XAI)AI-based Problem Solving and Planning