This paper develops frameworks to help Internet media designers address end-user information presentation preferences by advancing structures for assessing metadata design variables. Design variables are then linked to user cognitive styles. An underlying theme is that AI methodologies may be used to help automate the Internet media design process and to provide personalized and customized experiences. User preferences concerning knowledge acquisition in online experiences provide the basis for discussions of cognitive analysis, and are extended into structural implications for media design and interaction. The assumption is made that frameworks for the alignment of design metadata with user metacognitive elements may serve as a reference to aid information design for Internet-based media.

Discusses strategies for implementing modern knowledge management curricula in academic programs for adult professionals. References the perspectives of multidisciplinary curricula covering information and society; multimedia and hypermedia; electronic information design and presentation; and infrastructure development and implementation. The analysis assumes the increasing involvement of highly trained professionals in adult education programs; the continuing growth of corporate universities in scope and breadth; the integration of corporate programs with traditional colleges and universities; and the increasing use of the Internet as a mechanism to coordinate, supplement, support, and integrate learning experiences. Advances historical and pedagogical methodologies as a means to provide perspective and structure for program development and future research. References an information technology (IT) program for mid‐career information managers in Northern California and serving the high‐technology area known as Silicon Valley.

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Knowledge management, Artificial intelligence, Knowledge-based systems Government agencies carry out many events each year designed to determine future requirements and capabilities. These events indude field experiments, surveys, interviews, simulations and workshops. Similar themes are evident across many of these events. Unfortunately, mechanisms for passing information from one event to the next, or for developing bodies of knowledge in the topical areas they address, have yet to be fully developed. The task is difficult on two fronts. In response to this need a knowledge management capability was developed to help provide structure for dynamic and static data and thereby, aid in the analysis of complex experimentation. The system warehouses qualitative and quantitative data and supports mining operations through a number of traditional and artificial intelligence-based techniques. Described are the information architecture of the system, the knowledge processing methodologies, and the structure of the thematic data sets that form the knowledge ontologies.The research register for this journal is available at