Agent-Based Methods in Support of Adaptive Instructional Decisions

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.