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Adaptive graphical user interfaces (GUIs) have the potential to improve performance and user satisfaction by automatically tailoring the presentation of functionality to each individual user. In practice, however, many challenges exist, and evaluation results of adaptive GUIs have been mixed. To guide researchers and designers in developing effective adaptive GUIs, we outline a design space and discuss three important aspects to consider when conducting user evaluations of these types of interfaces: the control and reporting of adaptive algorithm characteristics, the impact of task choice and user characteristics on the overall effectiveness of a design, and evaluation measures that are appropriate for adaptive interaction.
Adaptive graphical user interfaces (GUIs) automatically tailor the presentation of functionality to better fit an individual user's tasks, usage patterns, and abilities. A familiar example of an adaptive interface is the Windows XP start menu, where a small set of applications from the "All Programs" submenu is replicated in the top level of the "Start" menu for easier access, saving users from navigating through multiple levels of the menu hierarchy (figure 1). The potential of adaptive interfaces to reduce visual search time, cognitive load, and motor movement is appealing, and when the adaptation is successful an adaptive interface can be faster and preferred in comparison to a nonadaptive counterpart (for example, Gajos et al. [2006], Greenberg and Witten [1985]).
In practice, however, many challenges exist, and, thus far, evaluation results of adaptive interfaces have been mixed. For an adaptive interface to be successful, the benefits of correct adaptations must outweigh the costs, or usability side effects, of incorrect adaptations. Often, an adaptive mechanism designed to improve one aspect of the interaction, typically motor movement or visual search, inadvertently increases effort along another dimension, such as cognitive or perceptual load. The result is that many adaptive designs that were expected to confer a benefit along one of these dimensions have failed in practice. For example, a menu that tracks how frequently each item is used and adaptively reorders itself so that items appear in order from most to least frequently accessed should improve motor performance, but in reality this design can slow users down and reduce satisfaction because of the constantly changing layout (Mitchell and Schneiderman [1989]; for example, figure 2b). Commonly...