Using the LLM-Cabin framework, use your own product benchmarking framework to define and analyze a research subject concerning AI models in automobiles.

Because my partner and I are both international students, we decided to tackle this prompt from a culture POV, to see how/if culture plays a part in users' preferences in AI models (specifically American, Chilean, and Chinese cultures.)

Culture will affect people's preferences of AI models in automobiles. More specifically, US and Chile will prefer functions they can influence, emphasizing the feeling of control. China will prefer functions that will influence them, emphasizing the feeling of connection.

To build our own benchmarking framework, we looked into the following models: Horizontal vs Vertical Individualism and Collectivism scale and the Censydiam model.

Regarding the literature on how culture affects choices, we found that many of them classified individuals and societies, so we chose this scale as one of the bases for cultural classification.

Because the previous model's classification of culture is more biased towards the whole society, and not detailed enough or linked to a user's personal needs, we found a model that also divides social and personal dimensions, which gives these 8 dimensions behind user motivation. It  also investigates the impact of culture on user motivation and behavior, since different cultural backgrounds will lead to different needs and motivations.