Findings in current use context

One of the main issues with the systems described in this research is that they make their users feel that they did not have control over their environment. This has been shown to result from invisibility of the system, which meant the system was difficult to understand (Badia et al., 2009). Inability to understand

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the system’s logic results in loss of trust in the system (Lim et al., 2009). To counter these effects, Bellotti & Edwards (2001) called for intelligibility and accountability in ambient systems. They defined and discussed the former as follows: “context-aware systems that seek to act upon what they infer about the context must be able to represent to their users what they know, how they know it, and what they are doing about it.” (Bellotti and Edwards, 2001, p. 201) Self-explanation has been further explored, including development of 10 explanation types used by these systems (Lim and Dey, 2009);

effectiveness of some of these explanations, notably ‘why’ and ‘why not’ explanations (Kulesza et al., 2009; Lim et al., 2009); development of a toolkit that automatically produces such explanations (Lim and Dey, 2010); visual depictions of correct predictions versus known failures (Talbot et al., 2009), and confidence of system making predictions (Kulesza et al., 2010; Mcnee et al., 2003). Enhanced intelligibility in the system, thus, increases people’s understanding of the system’s working, and has also been suggested to allow the user to tell the system how it should work (Kulesza et al., 2012, p. 10). Indeed, there exists a body of research on such debugging, in which

debugging refers to explicitly correcting system’s reasoning to match user’s expectations (Amershi et al., 2010; Kapoor et al., 2010; Kulesza et al., 2010; Lim and Dey, 2010). This argument, therefore, shows the true value of intelligibility – with increased understanding, the interactions that users had with the system, become more meaningful and more aligned with the users’ expectations. The users are able to co-operate with the system as a joint system; and they are able to maximise the system’s functionality to the fullest.

Increasing intelligibility is not, however, straightforward as designing

explanations for an ambient intelligent system could be a complex task (Bunt et al., 2007; Herlocker et al., 2000). For example, Bunt et al. (2012, p. 173) found that information about the system’s functionality is only wished if it gives benefits such as enhanced utility of the system. The essence of enhanced utility is likely to vary from user to user and differences can be

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slight, yet pose a great threat to user experience of the interaction. For example, user satisfaction may be lowered by too much information if users are experienced with the product (Mcnee et al., 2003). These factors thus raise a plethora of questions such as when does a user become experienced, does the provision of information need to stop immediately when the user has become experienced, or what do users classify as “enhanced utility” of the system? Kaasinen et al. (2012, p. 2) have argued it is important to understand people’s expectations of intelligent environments. It can be suggested that those expectations could even affect the whole user

experience. Users of ambient systems have expectations of the environment that originate from historical usage without the intelligent system in the environment. These expectations are a combination of large-scale

expectations of what the system will ultimately deliver such as ‘increased comfort at home’; as well as small-scale expectations in terms of specifics that the system should be undertaking at that point in time to achieve comfort expectations. This highlights the second point in Bellotti & Edwards’ (2001) work – accountability. The authors discussed this element in terms of allowing users to take charge of their actions and choices. While intelligent systems reduce the user’s burden of choosing and carrying out tasks; they also ‘claim’ those tasks and the user may not see them as their own responsibility. With a successful alignment of expectations and system performance, this ownership of choice could be given back to the user alongside enhanced control

capabilities.

Furthermore, as Vermeulen et al. (2009, p. 197) pointed out, ubicomp and ambient intelligence applications offer users little support regarding traditional user interface concerns such as feedback, control, and as mentioned, visibility. Traditionally, these elements have had a strong presence in explicit interactions with specific interfaces utilising input and output methods of buttons or screens. With ubiquitous computing, the interfaces and interaction disappear into the fabric of everyday life. If one considers time in the interaction with products, the process can be divided

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into pre-action, action and post-action. In traditional interfaces, feedback occurs in the latter two stages: during action (taking the form of clicks and haptic feedback to notify the user that button presses etc. have been successful) and post-action (the result of the action would notify the user of the successfulness of the undertaken action). It has been suggested that in the first stage, pre-action, another form of info transfer occurs – feedforward (Djajadiningrat et al., 2002). According to the authors, this “informs the user about what the result of his actions will be.” (Djajadiningrat et al., 2002, p. 286) In the current context, this concept is deemed extremely valuable as it can replace feedback and thus eliminate the time delay between action and feedback. If this is the case, feedforward would not occur prior to action, but would more likely be occurring during action, giving the user an opportunity for trial-and-error-type experimentation with the eventual outcomes of their actions in the future. This in turn would enhance accountability as users have direct comparison between their informed decisions and outcomes. Without such feedforward, outcomes of user actions may even be falsely attributed to system functionality, causing loss of trust and rejection of the system, as discussed above.

Exploration of ambient intelligence devices seems to be a key issue in learning their functionality as people are currently used to this type of ‘fiddling’ with their products to uncover their capabilities and ways to manipulate them. Rehman et al. (2005) developed an augmented reality system that visualised a context-aware ubiquitous computing device. The authors concluded that the visualisation of the device’s location was exceptionally useful for users to investigate and explore the system regarding its performance and whether it was fulfilling its goals, as well as facilitating the creation of context around the functionality that helped users answer ‘what if’ questions (Rehman et al., 2005). Building on this work, Vermeulen et al. (2009) presented another augmented reality system that overlaid the occupant’s physical environment with a projected graphical interface to communicate system’s functioning and reasoning. This application is of particular interest as, although the authors

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recognised several issues with mainstream use, this form of communication with the user is felt to enhance users’ understanding of a system in situ. This author argues that interactions of such type are especially useful for large segments of the population such as the elderly and computer illiterate people to show specific functions of real-life objects. While a lot of attention has been paid to visibility of the system, it has also been noted that in some applications, transparency is not important when the system works or is perceived to work (Bunt et al., 2012). This suggests that the amount of visibility of the systems’ inner workings varies greatly and a successful interface must facilitate user customisation of detail level.

As mentioned above and in the Mental Models section below, information gathered from devices by users facilitates the build-up of mental models. Research in ambient intelligence has shown contradicting results on the matter. Some suggest that relatively little change occurs in mental models over time (Tullio et al., 2007) whilst others have found users may change their mental models if the system communicates its functionality (Kulesza et al., 2010). The depth of the mental model formed by system explanation has also been discussed (Stumpf et al., 2007), however, any characteristic of a mental model is difficult to measure and thus caution must be exercised. Regardless, recent research has highlighted several interesting nuances of mental models in ambient systems regarding system-provided help and how this could lead to a better user experience. It has been shown that users with ‘scaffolding’ help in explaining the system build more accurate models of system

functionality than users without help; and through receiving that help, people experience higher self-efficacy and less anxiety when tackling issues with the system (Kulesza et al., 2012). This shows that interfaces should assist the user in a non-demanding way when they are first introduced to the user’s

environment to simulate the ideal usage of user manuals. Furthermore, it has also been shown that this assistance allows people to feel more positive about their experiences with the system and; people who are most successful in aligning the system’s thinking to their own experience greatest

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‘improvements’ in their mental model (Kulesza et al., 2012). These findings suggest that support should not only be given at the start but throughout to facilitate the alignment of people’s thinking to the machine’s and reassure users their control over the system.