In the era of GenAI, user attitude—shaped by cognition, emotion, and behavior—plays a critical role in the sustainable development of human–AI interaction. Human creativity and intelligence, as core drivers of social progress, are important factors influencing user attitudes. This paper systematically reviews 243 peer-reviewed studies on GenAI user attitudes published since 2019, identifying major research methods and theoretical perspectives, including the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the AI Device Use Acceptance (AIDUA) model. Drawing on contemporary creativity theories—such as Sternberg’s Theory of Successful Intelligence, the 4C Model by Kaufman and Beghetto, and the Dynamic Creativity Framework—we analyze how creativity and intelligence are conceptualized in current studies and how they affect user responses to GenAI. Through cross-cultural analysis and multimodal comparison, this review offers a comprehensive understanding of the interplay between GenAI and human creativity, aiming to support more inclusive and sustainable human–AI collaboration.

Food computing, a newly emerging topic, is closely linked to human life through computational methodologies. Meal recommendation, a food-related study about human health, aims to provide users a meal with courses constrained from specific categories (e.g., appetizers, main dishes) that can be enjoyed as a service. Historical interaction data, important user information, is often used by existing models to learn user preferences. However, if a user’s preferences favor less healthy meals, the model will follow that preference and make similar recommendations, potentially negatively impacting the user’s long-term health. This emphasizes the necessity for health-oriented and responsible meal recommendation systems. In this article, we propose a healthiness-aware and category-wise meal recommendation model called CateRec, which boosts healthiness exposure by using nutritional standards as knowledge to guide the model training. Two fundamental questions are raised and answered: (1) How can the healthiness of meals be evaluated? Two well-known nutritional standards from the World Health Organization and the United Kingdom Food Standards Agency are used to calculate the healthiness score of the meal. (2) How can the model training be guided in a health-oriented manner? We construct category-wise personalization partial rankings and category-wise healthiness partial rankings, and theoretically analyze that they meet the necessary properties and assumptions required to be trained by the maximum posterior estimator under Bayesian probability. The data analysis confirms the existence of user preferences leaning towards less healthy meals in two public datasets. A comprehensive experiment demonstrates that our CateRec effectively boosts healthiness exposure in terms of mean healthiness score and ranking exposure while being comparable to the state-of-the-art model in terms of recommendation accuracy.