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Agents are artificial intelligent programs that are capable of performing autonomous actions by controlling their actions. An agent may be able to discover and interact with other agents (including humans). Some agents may perform reactive actions through sensors. Such agents will react to changes sensed in the environment. Other agents do not simply react but are proactive, exhibiting goal-oriented behavior. Agents can be classified into four types: autonomous, communicative (socially able), reactive, and proactive. An agent may be a single agent system consisting of one of the four types of agents, or a multiagent system consisting of more than one of the four types. Agents can also be classified as reasoning agents, task-specific agents, communicative agents, information agents, collaborative agents, interface agents, and mobile agents.

Agents can be described in terms of the environment, abilities of sensors, built-in actions, built-in preferences, and decision-making mechanisms. Stan Franklin, the founder of the Institute for Intelligent Systems at the University of Memphis, defined this as a specification for describing an autonomous agent. The four types of agents can be considered as basic properties. Michael Wooldridge and Nicholas Jennings argued that other properties of agents include temporally continuous, learning, mobile, flexible, and character specific.

For educational purposes, agents have been used to assist instructors and learners in the teaching and learning process. This entry first describes the four main types of agents used in education and then discusses the application of mobile or handheld agents, a more recent advancement in agent technology, to teaching and learning.

Agents for Teaching and Learning

The early adoption of agents in teaching and learning are mainly in the form of single-agent systems. Multiagent systems came later. Recently distributed multiagent systems to create ambient intelligence have appeared. Ambient intelligence involves a ubiquitous environment wherein many networked devices are integrated to the environment containing agents that can recognize individuals and be personalized to meet individual needs.

Four main categories of agents are widely known for education: (1) knowledge-based systems (KBSs), (2) conversational agents, (3) intelligent tutoring systems (ITSs), and (4) animated pedagogical agents (APAs).

Knowledge-Based Systems

Knowledge-based systems assist decision making by using a query-based or knowledge-oriented approach. Knowledge-based systems integrate expert system technology (artificial intelligence techniques) into the decision-making process.

Conversational Agents

Conversational agents, or chatbots, use natural language as the interface with users. These agents have natural language-processing ability to interpret dialogs in natural language. Such agents are normally applied for text-based information search and task-oriented dialogs. For teaching and learning, pedagogical chatbots may be used to assist learners with problem-solving advice.

Intelligent Tutoring Systems

Intelligent tutoring systems, or intelligent tutors, provide personalized training by modeling learners’ knowledge, domain knowledge, tutoring knowledge, and communication knowledge. Most ITSs integrate cognitive science, artificial intelligence, and the World Wide Web. Generally, ITSs can create appropriate problems, hints, and help for learners. In these systems, learners’ current state of knowledge and instructors’ expertise are assessed and modeled. ITSs are able to manage, monitor, evaluate, and improve teaching over time. A good ITS allows one-on-one learner-to-instructor tutoring with fewer human instructors.

A variety of knowledge is normally represented in a learner model including (a) topics for the knowledge to be taught, (b) a database of faulty understanding, (c) the learner’s learning characteristics, (d) learners’ experience, (e) learners’ emotional characteristics, and (f) initial model of the learners including general ability and characteristics.

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