What happens when you leverage state-of-the-art artificial intelligence to create learning environments that are both effective and engaging?

BACKGROUND
Recent years have seen a growing recognition of the importance and challenges of creating learning environments that promote motivating, inquiry-based science learning. Pedagogical agents are embodied software agents that have emerged as a promising vehicle for promoting effective learning.

GOAL
The project has two complementary technology and learning thrusts. First, it will develop a full suite of Bayesian pedagogical agent technologies for inquiry-based science learning environments. To promote effective and engaging learning processes and outcomes, it will create Bayesian pedagogical agents that leverage probabilistic computational models that systematically reason about the multitude of factors that bear on decision making to infer learners' beliefs, goals, and plans, including strategy use, from their problem-solving actions. Second, the project will provide a comprehensive account of the cognitive processes and results of interacting with Bayesian pedagogical agents in inquiry-based science learning by conducting extensive empirical studies. To understand the cognitive mechanisms by which self-regulated inquiry-based science learning occurs with middle school students interacting with Bayesian pedagogical agents, we will take a multi-method approach to investigating the use and effectiveness of Bayesian pedagogical agents.

PROGRESS
By introducing pedagogical agents into the visually engaging environments that typify high-end game platforms and embedding them in dynamically generated science narratives, it will address the complementary goals of achievement and engagement. In both controlled laboratory and classroom-based field settings, these studies will investigate the central issues of self-regulation with respect to both achievement (science content knowledge, transfer, and effective strategy use, including strategy selection and strategy shifting) and engagement (self-efficacy, situational interest, and mastery orientation with an emphasis on persistence) to determine precisely which technologies and conditions contribute most effectively to learning processes and outcomes.

FUTURE
By taking advantage of the high degree of interactivity offered by high-performance learning environments and delivering technologies that craft customized interactive scientific narratives, the project holds significant potential for creating compelling learning experiences that lead to higher achievement. Moreover, because the project adopts an inquiry-based approach to self-regulated science learning and employs a broad array of quantitative and qualitative learning measures, it will yield a cognitive account of self-regulation that can inform the design of next-generation learning environments.

“Students will only acheive their creative potential if the environment supports them”