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Essays in Education

Abstract

This paper explores the integration of artificial intelligence chatbots as tools for implementing retrieval practice – an evidence-based learning technique that significantly enhances long-term memory retention – in business education contexts. We present four distinct implementation strategies that vary in sophistication and resource requirements: the standard AI model approach, which allows students to upload materials and use custom prompts for flexible, self-directed practice; the course-specific chatbot, which provides immediate access to preloaded curriculum content; the dedicated retrieval practice chatbot, which incorporates systematic algorithms for spaced repetition and interleaved practice; and the course-specific chatbot with integrated retrieval practice module, which combines preloaded content with automated, adaptive retrieval exercises. Drawing on empirical research demonstrating the superiority of retrieval-based learning over passive review methods, we examine how chatbots can enhance traditional retrieval practice formats – including true/false questions, multiple-choice questions, short answer exercises, flashcards, scenario-based assessments, retrieval games, and personalized tutoring – by providing immediate, tailored feedback that corrects misconceptions and prevents error encoding. Each implementation strategy offers distinct advantages and limitations regarding flexibility, development costs, technical requirements, and pedagogical effectiveness, enabling educators to select approaches aligned with their specific instructional goals, student populations, and available resources.

Primary Author Bio Sketch

Marcy Faircloth is Associate Professor of Business Law at Winona State University.

Secondary Author Bio Sketch

Patrick Paulson is Professor of Management Information Systems at Winona State University.

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