In human-AI interaction, users often begin with vague, incomplete, or imprecise queries that limit the system’s ability to provide meaningful support. While much attention has been given to generating accurate answers, the capacity of intelligent systems to guide users in formulating better questions remains underexplored. This paper proposes a methodology that supports users in the co-construction of knowledge through context-aware clarification questions. By combining semantic representations from knowledge graphs with the generative capabilities of large language models, the approach dynamically detects informational gaps and offers targeted prompts that refine user intent. This method fosters more natural, adaptive, and cognitively aligned interactions, improving user engagement, decision-making, and exploratory search. We discuss implications for designing intelligent systems that not only respond, but also proactively assist users in asking better questions.