Knowledge Affordances for Hybrid Human-AI Information Seeking

2026-04-30

AI Summary

This study addresses the critical challenge of systematically determining whom to consult for knowledge and why in human–AI collaborative environments to enhance coordination efficiency. It introduces, for the first time, the concept of “Knowledge Affordance” (KA) by adapting affordance theory to human–AI knowledge acquisition. KA characterizes what a knowledge source can provide, the types of problems it addresses, and its contextual properties, while emphasizing its dynamic alignment with task objectives, user preferences, and situational context. Drawing on affordance theory, semantic web services, and knowledge engineering, the authors develop a declarative modeling approach for KA that yields a semantically precise, relation-driven mechanism for describing knowledge sources. This framework enables transparent, adaptive, and mutually intelligible information exchange, laying a theoretical and technical foundation for intelligent systems capable of perceiving and leveraging knowledge affordances.

Abstract

As information ecosystems grow more heterogeneous, both humans and artificial agents increasingly face a simple yet unresolved question: when seeking knowledge, whom should we ask, and why? Inspired by how people intuitively "read a room", this paper introduces the concept of knowledge affordance (KA) to systematize how agents identify meaningful opportunities for information seeking in hybrid human-AI environments. Rather than introducing a fully formed framework, we propose KAs as declarative, semantically grounded descriptions of what a knowledge source can offer, for which kinds of questions, and with which contextual properties. Additionally, we suggest that KAs are relational, possibly emerging from the interplay between the agent's task, preferences and situational factors. Our contribution is thus a conceptual proposal that connects different research streams, including affordances, semantic web services, knowledge engineering and querying, and mutual intelligibility. We sketch possible research directions to build KA-aware systems that navigate information spaces with greater transparency, adaptability and shared understanding.

Problem

• knowledge affordance
• human-AI collaboration
• information seeking
• hybrid information environments
• knowledge source selection

Innovation

• knowledge affordance
• hybrid human-AI systems
• semantic grounding
• information seeking
• mutual intelligibility