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Given the current state of ES explanations research, it is evident that designers of ES explanation facilities must pay careful attention to a host of factors that impact the design process. They can be classified into four distinct categories relating to the characteristics of: (1) the task setting in which explanations are used, (2) the nature of the explanations that are provided, (3) the interface design and explanation provision strategies used, and (4) the individual users of the ES.

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5.1. TASK CHARACTERISTICS

The nature of the ES task and the context in which the ES is used is the first design factor that will influence the amount and the types of explanations that are used. The types of tasks that an ES performs can be categorized by various classifications, including analysis tasks vs. synthesis tasks, and heuristic classification tasks vs. heuristic configuration tasks. While these classifications overlap to some extent, each of them can be further decomposed into a larger hierarchy of many levels of tasks. For example, the heuristic classification task can be decomposed into the three inference processes (subtasks) of data abstraction, heuristic match, and solution refinement. Similarly, analysis tasks can be broken down into subcategories such as diagnosis, prediction, etc. The Ye and Johnson (1995) study directly investigated the influence of varying task types on the preference for the three types of explanations, by utilizing the data abstraction and heuristic match levels of heuristic classification as independent variables. However, it did not find significant differences in the preference for explanations between the two levels. The use of explanations that are provided by ES performing synthesis or heuristic configuration tasks, such as design or planning, has not been studied. Considering the critical differences between these tasks and the more common diagnostic tasks, it is reasonable to expect that they will result in different patterns of ES explanation use.

The context in which an ES is utilized will determine the purpose for which the explanation facility is used. Three contexts for the use of ES explanations can be identified: (1) by end-users in problem-solving contexts, (2) by knowledge engineers carrying out knowledge-base debugging activities, and (3) as part of ES validation activities carried out by domain experts and/or knowledge engineers. The distinctions between these three contexts are critical and stem from the fact that the use of ES explanations in systems development is motivated by a different set of objectives than when used as part of end-user applications. It can therefore reasonably be expected that end-users of ES applications will use

explanations differently from when they are used during debugging, validation, or other ES development activities.

While explanations are commonly incorporated into most end-user applications of ES, they also play a significant role in the development of ES by offering enhanced debugging and validation abilities. Most current ES development shells and environments include tools that utilize explanations to aid efficient and effective system development, e.g., the Knowledge Engineering Environment (KEE) from Intellicorp. Another example is the REPORT command in the VPExpert shell. This command lists in sequential order all the explanations attached (using the BECAUSE clause) to rules that "fired" as part of a consultation. Such a listing assists in the debugging of processing logic by knowledge engineers. It also allows users and domain experts, who may not be familiar with representation schemes and inference engines, to participate in the validation of a knowledge base.

In contrast to debugging and validation, the use of explanations by end-users of ES applications is motivated by a different set of reasons. For example, it has been suggested that an explanation facility is used: (1) by decision makers because it aids them in formulating problems and models for analysis, (2) by sophisticated users because it assures them that the system's knowledge and reasoning process is appropriate, and (3) by novice users because it can instruct them about the knowledge in the system as it is applied to solve a particular problem. There are also a variety of contexts in which end-user

applications of ES are used. For example, while some applications are used as tools for training novices in a domain, others are used by experts to support their own decision-making. The organizational context in which these end-user applications are used will also affect the use of the explanations. Some

organizations institutionalize the use of such ES applications for making certain critical decisions. The use of explanations when end-users are compelled to use the ES will certainly be different from the situation when end-users utilize the ES as a decision aid by choice. In summary, many different contexts of the use of ES can be identified as potentially influencing the use of ES explanations.

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5.2. CHARACTERISTICS OF THE EXPLANATIONS

The nature of the explanatory information provided by an ES to its users will certainly influence the explanations that are used. These can be divided into two major categories: explanation type and explanation content. While the types of explanations were discussed earlier, there is considerable overlap between these two categories. The three types of explanations are by definition different in content. For example, the Why explanations focus on providing declarative information about the task; the How explanations provide procedural task information; and the Strategic explanations present meta-knowledge of the task. Similarly, the various types of explanations will also differ in content in relation to whether they are provided as feedforward or feedback. For example, feedback explanations, being outcome specific, will by definition be more concrete and at a lower level of specificity than the more generalized

feedforward explanations.

While the influence of the types of explanations is potentially more relevant, largely because both ES developers and users distinguish clearly between them, it is also important to consider the influence of various dimensions of content. Some relevant dimensions of explanation content include the following. The informational content of the explanations in terms of the number of signals that are incorporated represents the first dimension. The second dimension is the abstraction level of the explanations, i.e., how concrete or abstract they are from the perspective of users. The third dimension is the granularity and specificity of the explanations, e.g., the lowest level will have the most amount of detail and vice versa. Fourth, explanations can be focused toward particular user groups, such as knowledge engineers, domain experts, and end-users, or they can have a more general focus. Terminological differences in explanations can be expected depending on who are the target users of the explanations. Fifth,

explanations can emphasize different aspects of that which is being explained, e.g., procedural aspects in contrast to declarative aspects.

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5.3. INTERFACE DESIGN AND PROVISION STRATEGIES

The design features of the interface used to provide explanations, as well as the strategies used for providing explanations, will also influence the patterns of ES explanation use. Specific aspects of the interface design include the following. First, the amount of effort required for users to access the explanations, i.e., the accessibility of the explanations, will influence their use. Two possible classes of strategies for accessing explanations can be identified. These include an active strategy where the ES presents explanations without the user having to request them, and a range of passive strategies that require the user to make varying levels of explicit physical effort to access the explanations. Such effort can range from clicking on specialized explanation icons presented on the screen to hitting predesignated function keys for accessing and scanning the explanations. Generally, an active strategy has the system interrupt the dialog to provide explanations or makes them available continuously as part of every screen of the ES. In the design of ES explanation facilities, it is important that interface designers consider the amount of effort required for users to request and access the explanations. The results of studies on cost- benefit models of the effort involved in utilizing computerized decision aids suggest that the accessibility of explanations, i.e., the cost of accessing them, will exert a salient influence on the use of explanations. Second, the communication mode used for presenting the explanations, e.g., audio and/or visual modes, will also influence the use of explanations. Third, the presentation format utilized for the explanations is also a factor, e.g., text explanations in contrast to image-based explanations that use graphical, iconic, and animation formats.

Considering that the primary reason for the provision of ES explanations is to improve users'

understanding of the ES and its domain, the feedforward and feedback explanation provision strategies presented earlier will also influence the use of explanations. The importance of these explanation provision strategies becomes obvious if one considers the analogy of a child engaged in a learning process to improve his or her understanding. While "an explanation machine," in the form of a child's parents, may be continuously available to provide explanations about some phenomenon that is the target of the learning, the child will only seek, attend to, and benefit from explanations provided at particular stages of the learning process. At different stages of the process, different types of explanations will be sought, and it can be expected that children at varying stages of cognitive development will seek different amounts and types of explanations. As well, it is also likely that explanations provided automatically without being requested, will at times impede rather than encourage the learning that takes place. This analogy therefore suggests that any evaluation of the influence of the explanation provision strategies must therefore take into consideration the other design factors.

5.4. USER CHARACTERISTICS

Three distinct categories of user characteristics that will impact the use of explanations can be identified: user expertise, individual differences, and the level of user agreement with the ES. Of these, user expertise is potentially the most significant to the design and use of ES explanations. Various theories of skill acquisition support this belief. As well, the empirical studies discussed in the last section, have also found significant effects for this factor. All these studies employed users' knowledge of the task domain to operationalize user expertise. The human-computer interface literature reveals another aspect of user expertise that can be considered as being just as relevant. This is the level of users' expertise or familiarity with expert systems themselves. This is termed systems expertise.

Various types of cognitive and personality-based individual differences can also be identified, primarily from the literature on decision support systems, as potentially influencing the use of ES explanations. However, while much is known of their influence on human cognitive functioning, they suffer from the lack of an adequate and coherent theoretical basis. Additionally, as is now recognized in the study of decision support systems, there is only a small likelihood that an individual differences approach to the design of decision aids will yield practical and cost-beneficial design requirements.

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The final category of user characteristics that can be identified is the level of user agreement with the ES. Studies found that the use of explanations increased the level of user agreement with ES conclusions. Together with the finding that experts were more likely to agree with an ES's conclusions than novices, this suggests that there could potentially be a reverse effect as well. The level of initial user agreement with ES conclusions would influence, to some extent, the amount of explanations used. As the differences in the level of domain expertise can result in different levels of agreement with an ES's conclusions, this suggests that the level of user agreement potentially moderates the influence of user expertise on the use of explanations.

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