A review and synthesis of recent research in intelligent computer-assisted instruction

Abstract

Educational devices incorporating artificial intelligence (AI) would “understand” what , whom and how they were teaching and could therefore tailor content and method to the needs of an individual learner without being limited to a repertoire of prespecified responses (as are conventional computer assisted instruction systems). This article summarizes and synthesizes some of the most important research in the development of stand-alone intelligent computer-assisted instruction (ICAI) systems; a review of passive AI-based educational tools (e.g. microworlds, “idea processors”, empowering envionments) would require a separate discussion. ICAI tutors and coaches have four major components: a knowledge base, a student model, a pedagogical module and a user interface. Major current themes of research in the knowledge base include studies of expert cognition, the transfer of meaning, and the sequencing of content. Student-modelling issues focus on alternative ways to represent a pupiľs knowledge, errors and learning. Pedagogical strategies used by ICAI devices range over presenting increasingly complex concepts or problems, simulating phenomena, Socratic tutoring with correction of pupil misconceptions and modelling of expert problem solving via coaching; the central theme of research is finding overarching paradigms for explanation. Language comprehension and generation topics which have special relevence to intelligent tutors and coaches are also briefly reviewed. Overall, increasing availability, decreasing cost and growing commercial interest in AI-based educational devices are enhancing the development of ICAI systems. Limits on the sophistication of user interfaces, on the scope of subject domains and on current understanding of individual learning are all constraining the effectiveness of computer tutors and coaches. The explicitness required for constructing intelligent devices makes their evolution more difficult and time consuming, but enriches the theoretical perspective which emerges. In brief, the computational and economic enabling of ICAI is proceeding more rapidly than are its empirical and cognitive foundations, but significant overall progress is being made.