Are adaptive information agents the answer to our online overload? This paper examines the development and performance of adaptive information agents designed to alleviate information overload. It explores key issues in their development, including document representation, learning mechanisms, and classification techniques. The study reviews various paradigms used in creating these agents, highlighting their differing performance in terms of computational efficiency, classification effectiveness, learning autonomy, exploration capability, and explanatory power. By examining representative information agents, the authors aim to provide a basic understanding of the pros and cons of these paradigms, identifying directions for the development of next-generation adaptive information agents. The research offers important insights for **digital competency** with AI.
Published in the International Journal on Artificial Intelligence Tools, this paper directly aligns with the journal’s focus on AI applications. The review of adaptive information agents and their underlying paradigms contributes to the understanding and advancement of AI technologies for information retrieval and management, falling squarely within the journal’s scope, Science: Mathematics: Instruments and machines: Electronic computers. Computer science, and tool development. The identification of future research directions further enhances its relevance.