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Multiple Agent Designs in Conversational Intelligent Tutoring Systems

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  • Additional Information
    • Peer Reviewed:
      Y
    • Source:
      21
    • Sponsoring Agency:
      National Science Foundation (NSF)
      Institute of Education Sciences (ED)
      US Army Research Laboratory (ARL)
      Office of Naval Research (ONR) (DOD)
    • Contract Number:
      SBR9720314
      REC0106965
      REC0126265
      ITR0325428
      REESE0633918
      ALT0834847
      DRK120918409
      1108845
      R305H050169
      R305B070349
      R305A080589
      R305A080594
      R305G020018
      R305C120001
      W911INF1220030
      N000140010600
      N0001412C0643
    • Subject Terms:
      Intelligent Tutoring Systems; Man Machine Systems; Natural Language Processing; Educational Technology; Affordances; Educational Benefits; Instructional Design; Artificial Intelligence
    • Accession Number:
      10.1007/s10758-019-09431-8
    • Abstract:
      This article describes designs that use multiple conversational agents within the framework of intelligent tutoring systems. Agents in this case are computerized talking heads or embodied animated avatars that help students learn by performing actions and holding conversations with them in natural language. The earliest conversational intelligent tutoring systems were limited to a single agent that interacted with a student in the role of a teacher or expert. Technological advances have since made possible systems in which multiple agents interact with the learner and each other to model ideal behavior, strategies, reflections, and social interactions. Though still an emerging technology, multi-agent intelligent tutoring systems afford pedagogical benefits that go beyond the capabilities of the single-agent system and have facilitated learning gains on a variety of subject matters and skills, including science, technology, engineering, mathematics, research methods, metacognition, and language comprehension. The present work describes some common multi-agent designs that may be used to achieve a variety of pedagogical goals. We provide examples of how these designs have been implemented in educational or experimental settings and anticipate future use within the field of artificial intelligence. [This article was published in "Technology, Knowledge and Learning" (EJ1262514).]
    • Abstract:
      As Provided
    • IES Funded:
      Yes
    • Publication Date:
      2022
    • Accession Number:
      ED616902