Capturing Food Culture for Adaptive AI: Generative Insights from a Multimodal Profiling Study

Abstract

Artificial intelligence (AI) is creating new possibilities for digital systems that adapt to users in culturally sensitive ways. This study examines multimodal profiling as an early step for capturing culturally meaningful information in the context of healthy and sustainable eating. The aim is to provide a foundation for future interface adaptations that better support healthier and more sustainable practices. Building on earlier qualitative work involving interviews and cultural probes, we drew on insights that revealed complex personal food frameworks in which health and sustainability intersect with relationships, place, habit, and emotion. Using these insights as a foundation, we explored how different modalities of sharing information help people express these cultural dimensions in ways that are accessible to AI systems. 15 participants across diverse European contexts participated in this study. They described their everyday food practices, motivational drivers, and food heritage through spoken audio reflections, visual tasks, and written text. The results show that each modality elicits distinct types of cultural expression and that organising content into three dimensions gives the AI system a clear interpretive structure. Then, the AI generated summaries based on participants’ multimodal inputs across the three cultural dimensions, reflecting their food culture. These summaries were rated as highly familiar and accurate, indicating strong cultural resonance. Overall, the findings suggest that multimodal profiling supports the capture of culturally grounded aspects of food practice that are difficult to obtain through single input formats and can serve as an early foundation for adaptive interfaces that personalise interactions in culturally meaningful ways.

Keywords: Human-centered AI, Cultural Adaptivity, Multimodal Profiling, Adaptive Interfaces, User Experience, Cultural Resonance, Food Culture, Design Research

DOI: 10.54941/ahfe1007162