Adaptive and Intelligent Web-based Educational Systems

Adaptive and intelligent Web-based educational systems (AIWBES) provide an alternative to the traditional “just-put-it-on-the-Web” approach in the development of Web-based educational courseware (Brusilovsky & Miller, 2001). AIWBES attempt to be more adaptive by building a model of the goals, preferences and knowledge of each individual student and using this model throughout the interaction with the student in order to adapt to the needs of that student. They also attempt to be more intelligent by incorporating and performing some activities traditionally executed by a human teacher such as coaching students or diagnosing their misconceptions. The first pioneer intelligent and adaptive Web-based educational systems were developed in 19951996 (Brusilovsky, Schwarz, & Weber, 1996a; Brusilovsky, Schwarz, & Weber, 1996b; De Bra, 1996; Nakabayashi, et al., 1995; Okazaki, Watanabe, & Kondo, 1996). Since then many interesting systems have been developed and reported. An interest to provide distance education over the Web has been a strong driving force behind these research efforts. The research community was helped by the provision of a sequence of workshops that brought together researchers working on AIWBES, let them learn from each other, and then advocate the ideas of this research direction via on-line workshop proceedings (Brusilovsky, Henze, & Millán, 2002; Brusilovsky, Nakabayashi, & Ritter, 1997; Peylo, 2000; Stern, Woolf, & Murray, 1998). A number of interesting AIWBES that were reported at early stages of their development during these workshops have since achieved the level of maturity. This double special issue capitalizes on the results of these workshops and assembles a collection of papers that represents the state of the art in the development of AIWBES. The goal of this introductory article is to provide a more systematic view to the variety of modern AIWBES and to discuss the role and the place of the AIWBES research stream in the field of Artificial Intelligence in Education (AI-Ed). It provides a brief overview of known AIWBES technologies classified by the field of their origin. It also attempts to distill the new design paradigm behind modern AIWBES and to compare this paradigm with a traditional design paradigm that has been dominating the field of AI-Ed for the last 15 years.