In a recent study from Ritsumeikan Asia Pacific University in Japan, the sociocultural elements that influence Generation Z's acceptance of AI technology are looked at. The company believes it is essential to undertake a study on emotional AI's acceptance among Gen Z because they are the generation most susceptible to it. Over 50% of respondents expressed anxiety about using NCDC overall, although responses varied according to gender, income level, educational level, and religious affiliation. The sociocultural factors affecting Generation Z's acceptance of emotional AI technology are examined in a recent study from Ritsumeikan Asia Pacific University in Japan. Emotional AI, or artificial intelligence that engages with human emotions, is quickly developing to be useful in many applications.

AI has ubiquitous presence in technology. Yet, it had been lacking a crucial feature: the ability to engage human emotions. Algorithms that can sense human emotions and interact with them are quickly becoming mainstream as they come embedded in existing systems. Known as "emotional AI," the new technology achieves this feat through a process called "non-conscious data collection"(NCDC), in which the algorithm collects data on the user's heart and respiration rate, voice tones, micro-facial expressions, gestures, etc. to analyze their moods and personalize its response accordingly. However, the unregulated nature of this technology has raised many ethical and privacy concerns.

In this thesis, we introduce a novel formal framework to represent and reason about qualitative direction and distance relations between extended objects using Answer Set Programming (ASP). We take Cardinal Directional Calculus (CDC) as a starting point and extend CDC with new sorts of constraints which involve defaults, preferences and negation. We call this extended version as nCDC. Then we further extend nCDC by augmenting qualitative distance relation and name this extension as nCDC+. For CDC, nCDC, nCDC+, we introduce an ASP-based general framework to solve consistency checking problems, address composition and inversion of qualitative spatial relations, infer unknown or missing relations between objects, and find a suitable configuration of objects which fulfills a given inquiry.