Lifelike Pedagogical Agents for Mixed-initiative Problem Solving in Constructivist Learning Environments

  • Authors:

  • James C. Lester;Brian A. Stone;Gary D. Stelling

  • Affiliations:

  • Department of Computer Science, Engineering Graduate Research Center, North Carolina State University, Raleigh, NC 27695-7534, USA/ e-mail: lester,gdstell@csc.ncsu.edu;Department of Computer Science, North Carolina State University, Raleigh, NC 27695-8206, USA/ e-mail: bastone@eos.ncsu.edu;Department of Computer Science, Engineering Graduate Research Center, North Carolina State University, Raleigh, NC 27695-7534, USA/ e-mail: lester,gdstell@csc.ncsu.edu

  • Venue:
  • User Modeling and User-Adapted Interaction
  • Year:
  • 1999

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Abstract

Mixed-initiative problem solving lies at the heart of knowledge-based learning environments. While learners are actively engaged in problem-solving activities, learning environments should monitor their progress and provide them with feedback in a manner that contributes to achieving the twin goals of learning effectiveness and learning efficiency. Mixed-initiative interactions are particularly critical for constructivist learning environments in which learners participate in active problem solving. We have recently begun to see the emergence of believable agents with lifelike qualities. Featured prominently in constructivist learning environments, lifelike pedagogical agents could couple key feedback functionalities with a strong visual presence by observing learners‘ progress and providing them with visually contextualized advice during mixed-initiative problem solving. For the past three years, we have been engaged in a large-scale research program on lifelike pedagogical agents and their role in constructivist learning environments. In the resulting computational framework, lifelike pedagogical agents are specified by (1) a behavior space containing animated and vocal behaviors,(2) a design-centered context model that maintains constructivist problem representations, multimodal advisory contexts, and evolving problem-solving tasks, and(3) a behavior sequencing engine that in realtime dynamically selects and assembles agents‘ actions to create pedagogically effective, lifelike behaviors. To empirically investigate this framework, it has been instantiated in a full-scale implementation of a lifelike pedagogical agent for Design-A-Plant, a learning environment developed for the domain of botanical anatomy and physiology for middle school students. Experience with focus group studies conducted with middle school students interacting with the implemented agent suggests that lifelike pedagogical agents hold much promise for mixed-initiative learning.