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212 Kalyuga Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. data available from tracing user interactions with the system are usually imprecise, incomplete, and uncertain. Applying modern artificial intelligence approaches and methods (e.g., machine learning, Bayesian inference networks, neural networks, etc.) could help increase the precision of adaptive technologies. For example, intelligent solution analyses could diagnose missing or defective components of knowledge and skill, and provide learners with more accurate feedback and support. On the other hand, quality of adaptive environments could also be improved by developing new cognitive diagnostic techniques to replace traditional assessment methods used in constructing user models. The following sections describe a possible implementation of this approach. Rapid Diagnostic Method for Tailoring Multimedia to Levels of User Expertise The research on expertise emphasizes the importance of diagnosing domain-specific organized knowledge structures when evaluating levels of proficiency. Traditional methods of knowledge assessment are usually lengthy and limited in their ability to rapidly diagnose different levels of knowledge acquisition. They are not suitable for realtime, on-line adaptation of multimedia formats to dynamically changing levels of expertise. Available methods of cognitive diagnosis used in cognitive laboratory studies (e.g., concurrent and retrospective reporting, observations, etc.) are also unfit for realtime monitoring of user performance in adaptive digital multimedia environments because they are very time-consuming. Therefore, no appropriate, cognitively-oriented diagnostic methods are available to be used in adaptive procedures for user-tailored multimedia environments. The content of users knowledge base could not be accessed directly. Usually, we are able to obtain some evidence of that knowledge from results of various cognitive activities (e.g., solving test problem) and make probabilistic inferences about possible underlying cognitive constructs. This evidence could be inadequate in many situations. For example, students answers to a series of test problems would not tell us if those problems were solved by using a novice-like search approaches or an expert-like method based on knowledge of appropriate solution procedures (or, in the latter case, what level of knowledge was applied). We could do better in cognitive diagnosis if we were able to rapidly register immediate traces of individuals use of their knowledge structures while they approach a problem or situation. The diagnostic power of this method could approach that of concurrent reporting or think-aloud diagnostic techniques; however, it could work on a considerably shorter time scale. The rapid tracing of currentlyactivated knowledge structures essentially means accessing and monitoring content of working memory or, more accurately LTWM, since we are diagnosing knowledge-based cognitive performance. Therefore, to evaluate user levels of expertise in real-time, we may need to rapidly diagnose the content of LTWM during complex cognitive activities. With this approach, LTWM characteristics are used to determine relevant components of knowledge base held in long-term memory.

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