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The paper examines the positive and the possible adverse effects of adaptive user interfaces (AUIs) in the context of an in-vehicle telematic system as a function of four factors: (1) four different levels of adaptivity (ranging from manual to fully adaptive with intermediate levels); (2) different tasks; (3) routine (familiar) and non-routine (unfamiliar) situations; and (4) different user age groups. Both experiments included three sessions during which participants drove a simple driving simulator and performed tasks with the telematic system at one of the adaptivity levels. We measured task performance times and lane position variance. Adaptivity was not always equally beneficial, and its benefits depended on a number of factors, including the frequency in which the tasks were performed, the user’s age, the difficulty of the task and the user's involvement in the task. In familiar, routine situations, a fully adaptive system was beneficial for all participants, particularly older ones. In unfamiliar situations, to which the AUI was not adjusted, cognitive workload increased substantially, adversely affecting performance. Intermediate levels of adaptivity keep users involved in the task and help them become more proficient when performing both routine and non-routine tasks. However, intermediate levels of adaptivity should also be implemented with care, because they may also have adverse effects when users encounter non-routine situations.

Adaptive user interfaces (AUIs) are defined as systems that adapt their displays and available actions to the user’s current goals and abilities by monitoring user status, the system state and the current situation (Rothrock et al., 2002). The use of AUIs supposedly helps to improve user interaction with systems by facilitating user performance, minimizing the need to request help, easing system use, helping users deal with complex systems and avoiding cognitive overload problems (Browne et al., 1990; Edmonds, 1993; Hook, 1998 and Hook, 1999; Trumbly et al., 1994). These benefits are apt to disappear (or are minimal) when AUIs violate usability design principles. For instance, AUIs are almost inherently inconsistent over time i.e., their interface or functionality may change. For additional examples of the possible usability problems that may arise from adaptivity, see Jameson (2003), Hook (1999), Keeble, Macredie (2000), Kuhme (1993) and Shneiderman (1997). In spite of major progress in AUI research, we still lack a methodology for determining when and how adaptivity should be implemented. These decisions should be based on understanding the conditions in which benefits from adaptivity outweigh possible costs. In this paper we propose that AUI properties cannot be evaluated in isolation. Instead, one must consider the circumstance in which the system is used, the user population and other factors. For instance, the same algorithms for adaptation and the same interface may be very efficient in some contexts in which the system is always used the same way, and they may be very inefficient in other contexts in which system use varies more. We refer to this complex set of variables as the ecology of the system. Rather than focusing on one specific factor for determining whether adaptivity will be beneficial, we maintain that it is necessary to look at the whole ecology of system use. By looking at the wide range of relevant factors, system designers will be in a better position to provide users with the systems they truly need and which serve their interests.

The main objective of this research was to gain a systematic understanding of some of the consequences from the use of AUIs. Our research points to a number of important issues. First, adaptivity is not equally beneficial under all conditions, and since AUIs are usually used in complex, changing environments, it is important to consider the different variables affecting the interaction. We have shown that the preferred type of system depends on a number of factors, such as the frequency at which the tasks are performed, the user’s age, the difficulty level of the task and the level of user involvement in the task. We also found that it may be beneficial to consider intermediate levels of adaptivity, rather than seeing the introduction of adaptivity as an all-or-none decision. Intermediate levels of adaptivity keep users involved in the task and help them become more proficient when performing both routine and non-routine tasks. However, even when intermediate levels of adaptivity are implemented, one needs to consider the following aspects: (1) The proportion of routine and non-routine tasks: AUIs utilizing intermediate levels of adaptivity may be more suitable as the proportion of non-routine tasks, relative to the routine tasks, increases. In situations in which only routine tasks are performed, intermediate levels of adaptivity may be less advantageous compared to the fully adaptive system. (2) The type of task: users have to invest more effort to perform more complex tasks, so higher levels of adaptivity may be more beneficial for these tasks. Intermediate levels of adaptivity may not provide any benefits when performing easy tasks. (3) The users: intermediate levels of adaptivity were less useful for older users than for younger users.