Conclusions

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If designed properly and efficiently, intelligent tutoring systems can be of great benefit to distributed learning systems. Effective intelligent tutoring systems will make sure learners are given proper one-to-one instruction and attention to reach mastery as described by Bloom (1984) in his two-sigma article. The development of intelligent tutoring systems must include a team consisting of members from different disciplines to make sure that all of the expertise required for the one-to-one instruction is present.

Apart from research on how to develop intelligent tutoring systems, research must be completed in other areas to continually improve distributed-learning systems. One important area of research is to determine how an intelligent tutoring system can be used for individualized learning as well as collaborative learning. This area of research is important, because some learning styles prefer collaborative learning and some content areas require collaborative learning to achieve the learning outcomes for courses. Wasson’s (2000) research in this area is timely. She is looking at desired relationships between people, tools, and tasks that can stimulate collaborative behavior, such as genuine interdependence and the use of intelligent agents in collaborative learning. Another important area of research is how to design intelligent tutoring systems using constructivist’s learning principles, where students are active, and they construct their own meanings and knowledge from the information presented during the distributed learning session.

The challenge for researchers and scientists is how to develop intelligent tutoring systems for distributed learning to duplicate the human tutor expertise in a one-to-one learning environment. Ally (2000) conducted a study to determine what tutors do when they tutor students in a one-to-one environment using distance education. The information from this study could be expanded and utilized to build intelligent tutoring systems so that they can become closer to and start to behave like human tutors. Also, more research and development are needed to investigate how intelligent tutoring systems can show affect, emotions, and have a sense of humor, similar to human tutors, during the tutoring process.

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