Addressing the challenge of context-aware computing’s failure to fully appreciate and adapt to the diverse geographical, social, and cultural differences across settings, we introduce a novel system that utilizes a differentiated Term Frequency-Inverse Document Frequency (TF-IDF) model along- side a user-centric interface. At the heart of our solution is the ability to dynamically recognize and adapt to the unique cultural and social nuances of various regions, thereby making AI-driven solutions more relevant, accessible, and inclusive. This paper details the design, implementation, and evaluation of our system, focusing on its capacity to assist users in generating culturally and geographically sensitive concept queries, accurately identify expected categories, and facilitate the ideation process. Through a comprehensive study involving participants from diverse backgrounds, the results demonstrate a significant improvement in user engagement, conceptual accuracy, and the generation of innovative ideas. These findings validate our system’s effectiveness in bridging the gap between human concept expression and machine-understandable context features, thereby addressing the critical need for adaptability, equity, and inclusion in AI-driven experiences across varied settings.