Abstract
The framework presented in the paper identifies the promises and pitfalls of Artificial Intelligence (AI) and End-User Development (EUD) approaches by focusing on two basic system components: (1) adaptive systems (grounded in AI) that change their behavior automatically driven by context-aware mechanisms including models of their users and specific task contexts, and (2) adaptable systems (grounded in EUD) that can be adjusted, modified, and extended by their users in order to capture unforeseen and important emergent user needs and aspects of problems. Grounded in an analysis of design trade-offs between the two approaches, arguments, and examples for creating a desirable symbiosis between adaptive and adaptable systems are described and design guidelines for future socio-technical environments are explored contributing to the development of theoretical concepts for the future of EUD.
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