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Agent-Based Methods in Support of Adaptive Instructional Decisions

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Adaptive Instructional Systems (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12214))

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Abstract

This paper examines the functionality of artificially-intelligent agents as a methodology for supporting automated decisions in adaptive instructional systems (AISs). AISs are artificially-intelligent, computer-based systems that guide learning experiences by tailoring instruction and recommendations based on the goals, needs, preferences, and interests of each individual learner or team in the context of domain learning objectives. AISs are a class of instructional technologies that include intelligent tutoring systems (ITSs), intelligent mentors or recommender systems, and intelligent instructional media. This paper explores various agent-based methods to gauge their impact on four automated decisions within the Learning Effect Model (LEM): 1) determining current and predicting future learner states, 2) making recommendations for new experiences (e.g., courses or problem selection), 3) selecting high level instructional strategies to influence long-term learning, and 4) selecting low level instructional tactics to influence near-term learning.

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Acknowledgments

The author wishes to gratefully acknowledge Benjamin Bell for his contributions to our 2019 paper that formed the basis for this expanded paper. The author also thanks Dr. R. Bowen Loftin, President-Emeritus at Texas A&M University, Dr. J. Dexter Fletcher at the Institute for Defense Analyses, and Dr. Michael van Lent at Soar Technologies, Inc. for their positive influence in shaping the narrative in this paper.

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Authors and Affiliations

  1. Soar Technology, Inc., Orlando, FL, 32817, USA

    Robert Sottilare

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  1. Robert Sottilare
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Correspondence to Robert Sottilare .

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Editors and Affiliations

  1. Soar Technology, Inc., Orlando, FL, USA

    Robert A. Sottilare

  2. Fraunhofer FKIE, Wachtberg, Germany

    Jessica Schwarz

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Sottilare, R. (2020). Agent-Based Methods in Support of Adaptive Instructional Decisions. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. HCII 2020. Lecture Notes in Computer Science(), vol 12214. Springer, Cham. https://doi.org/10.1007/978-3-030-50788-6_12

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  • DOI: https://doi.org/10.1007/978-3-030-50788-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50787-9

  • Online ISBN: 978-3-030-50788-6

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