Cultivation of human centered artificial intelligence: culturally adaptive thinking in education (CATE) for AI - Samuel, Yana; Brennan-Tonetta, Margaret; Samuel, Jim; Kashyap, Rajiv; Kumar, Vivek; Krishna Kaashyap, Sri; Chidipothu, Nishitha; Anand, Irawati; Jain, Parth (Frontiers in Artificial Intelligence, 2023)
Artificial Intelligence (AI) has become ubiquitous in human society, and yet vast segments of the global population have no, little, or counterproductive information about AI. It is necessary to teach AI topics on a mass scale. While there is a rush to implement academic initiatives, scant attention has been paid to the unique challenges of teaching AI curricula to a global and culturally diverse audience with varying expectations of privacy, technological autonomy, risk preference, and knowledge sharing. Our study fills this void by focusing on AI elements in a new framework titled Culturally Adaptive Thinking in Education for AI (CATE-AI) to enable teaching AI concepts to culturally diverse learners. Failure to contextualize and sensitize AI education to culture and other categorical human-thought clusters (Barsalou 1989; Murphy and Medin 1985), can lead to several undesirable effects including confusion, AI-phobia, cultural biases to AI, increased resistance towards AI technologies and AI education. We discuss and integrate human behavior theories, AI applications research, educational frameworks, and human centered AI principles to articulate CATE-AI. In the first part of this paper, we present the development a significantly enhanced version of CATE. In the second part, we explore textual data from AI related news articles to generate insights that lay the foundation for CATE-AI, and support our findings. The CATE-AI framework can help learners study artificial intelligence topics more effectively by adapting and contextualizing AI to their sociocultural needs.