Artificial Intelligence To Support Human Instruction
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA(2019)
摘要
The popular media’s recent interest in artificial intelligence (AI) has focused on autonomous systems that might ultimately replace people in fields as diverse as medicine, customer service, and transportation and logistics. Often neglected is a subfield of AI that focuses on empowering people by improving how we learn, remember, perceive, and make decisions. This human-centered focus relies on interdisciplinary research from cognitive neuroscience, psychology, and theoretical computer science.The synergy among these fields promises to improve the microorganization of human instruction: picking the next exercise for a student to attempt, choosing what sort of hints and feedback to provide, determining when material should be reviewed, and selecting among teaching activities. These are decisions which occur on a granularity that human instructors are typically unable to monitor and individualize, and for which students have poor metacognitive strategies (1). Such microinstruction complements the strengths of human teachers and can yield significant benefits. For example, in a semester-long experiment integrated into a middle-school foreign language course, setting aside roughly 30 min per week for AI-guided personalized review of previously introduced material led to a 16.5% improvement in overall course retention on a cumulative examination administered a month after the end of the semester, relative to a time-matched control condition that reflects current educational practice (2).In PNAS, Tabibian et al. (3) address the learning and retention of factual material such as foreign language vocabulary. They present an adaptive, data-driven method with theoretical guarantees for scheduling retrieval practice. Their work contributes to a growing body of results in algorithmic-education theory that proves properties of idealized mathematical models of educational scenarios (4⇓–6).Like every proposal for AI-based instruction, the approach of Tabibian et al. (3) is formalized by two models: student and teacher. The student model quantifies an individual’s current … [↵][1]1To whom correspondence should be addressed. Email: mozer{at}colorado.edu. [1]: #xref-corresp-1-1
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