Adaptation Vectors for Instructional Agents

Abstract

This paper examines the application of intelligent agents to guide and adapt instruction in a class of learning technologies known as 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, and preferences of each individual learner or team in the context of domain learning objectives. Intelligent agents are autonomous entities which observe the conditions in their environments through percepts (e.g., sensors) and then act upon the environment using actuators. Their activity is intelligently directed to strategies (plans for action) and tactics (actions executed by the AIS) which enhance the progress of the learner toward the achievement of assigned goals and objectives. To optimize agent and learner performance we examine a notional set of principles that is needed to guide adaptive instructional designers and system developers toward adaptation vectors that support both instructionally meaningful (effective) and doctrinally correct (relevant and tactically plausible) actions by the AIS. In conclusion, we explore the literature to understand the design of adaptation vectors and provide recommendations for future AIS research and standards development.