Adaptive Learning Systems [ALS] have been proposed as a new approach for analyzing, designing and implementing complex software systems [Jennings et al., 1998]. Although varied in description, these systems are characterized by behaviors that are adaptable (by selfautomation of actions) and flexible (by learning the user's preferences, styles, and cognitive levels thereby offering proactive forms of interaction/support). ALS are usercentered, and have the potential to revolutionize the way users interact with computers, overcoming many of the limitations of current systems. However, the development and acceptance of agent based projects have been plagued with inconsistency and lack of grounded theoretical framework. A review of the literature also indicates that researchers have been complaining about the lack of these systems' use as practical tools for real world problems [Bradshaw, 1997; Hook 1996; Jennings et al., 1998; Maes, 1994; Nwana, 1996]. This dissertation agrees and identifies a domain that can significantly benefit from this technology.