Successes and Failures in Building Learning Environments to Promote Deep Learning: The Value of Conversational Agents

  • Arthur C. Graesser
  • Anne M. Lippert
  • Andrew J. Hampton
Chapter

Abstract

This chapter describes some attempts to promote deep learning (as opposed to shallow learning) through conversational pedagogical agents. Learning environments with agents have been developed to serve as substitutes for humans who range in expertise from novices to experts. For example, AutoTutor helps students learn by holding a dialogue in natural language with the student, whereas trialogues have two agents interacting with the student in a three-way interaction. Agents can guide the interaction with the learner, instruct the learner what to do, and interact with other agents to model ideal behavior, strategies, reflections, and social interactions. Some agents generate speech, gestures, body movements, and facial expressions in ways similar to people. These agent-based systems have sometimes facilitated deep learning more than conventional learning environments. Agents have shown learning gains on a variety of subject matters and skills, including science, technology, engineering, mathematics, research methods, metacognition, and language comprehension. Learning environments are currently being developed to improve lifelong learning and collaborative problem solving.

Keywords

AutoTutor Collaboration Conversational agents Deep learning Games Intelligent tutoring systems Metacognition Self-regulated learning 

Notes

Acknowledgments

The research was supported by the National Science Foundation (0325428, 633918, 0834847, 0918409, 1108845, 1443068), the Institute of Education Sciences (R305B07460, R305B070349, R305A080594, R305C120001), and the Office of Naval Research (N00014-00-1-0600, N00014-12-C-0643; N00014-16-C-3027). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of these funding sources.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Arthur C. Graesser
    • 1
  • Anne M. Lippert
    • 1
  • Andrew J. Hampton
    • 1
  1. 1.Department of Psychology and Institute for Intelligent SystemsUniversity of MemphisMemphisUSA

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