Design Space and Evaluation Challenges of Adaptive Graphical User Interfaces

By Findlater, Leah; Gajos, Krzysztof Z. | AI Magazine, Winter 2009 | Go to article overview

Design Space and Evaluation Challenges of Adaptive Graphical User Interfaces


Findlater, Leah, Gajos, Krzysztof Z., AI Magazine


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 cited issues with adaptive interfaces include the lack of control the user has over the adaptive process and the difficulty that users may have in predicting what the system's response will be to a user action (Hook 2000).

User evaluation of adaptive GUIs is more complex than evaluation of traditional, nonadaptive interfaces due to the increased variability of interaction. To guide researchers and designers in developing effective adaptive GUIs, particularly adaptive control structures such as menus and toolbars, we first briefly summarize the design space of these interfaces before discussing three evaluation issues in detail: (1) the control of adaptive algorithm characteristics and their potential impact; (2) the user's familiarity with the task and interface; (3) measures that are appropriate for adaptive interaction, particularly the impact of adaptive behavior on overall awareness of features in the interface. The focus of this article is on GUI control structures, such as menus and toolbars, and we specifically do not cover systems that adapt information content to the user's needs, such as recommender systems.

Relationships to the Theme Articles

This article is related to all three theme articles in this issue. In terms of the usability benefits of intelligence (Lieberman, in this issue), graphical adaptive user interfaces have the promise of allowing technology to adapt itself to the users, making efficient use of user input, and providing methods of input and output that are natural for the user. We also include many examples of the general concepts and analysis patterns introduced in the usability side-effects theme article (Jameson, in this issue), but by focusing on a specific type of system intelligence, we are able to offer a more tightly knit analysis, including some well-established empirical results and concepts that are specific to graphical adaptive user interfaces but that may offer general insights for the design of other types of intelligent interactive systems. Finally, our analysis illustrates some of the subtleties involved in the evaluation of interactive intelligent systems (see the theme article on usability engineering methods) that result from trade-offs among usability goals and from the influences of properties of users, tasks, and contexts. …

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