Samuel Yana, Brennan-Tonetta Margaret, Samuel Jim, Kashyap Rajiv, Kumar Vivek, Krishna Kaashyap Sri, Chidipothu Nishitha, Anand Irawati, Jain Parth
Middlesex County College, Edison, NJ, United States.
Rutgers, The State University of New Jersey, New Brunswick, NJ, United States.
Front Artif Intell. 2023 Dec 1;6:1198180. doi: 10.3389/frai.2023.1198180. eCollection 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, can lead to several undesirable effects including confusion, AI-phobia, cultural biases to AI, increased resistance toward 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 serving as a basis for adapting and contextualizing AI to their sociocultural needs.
人工智能（AI）在人类社会中已无处不在，但全球仍有很大一部分人对人工智能没有、几乎没有或仅有适得其反的信息。大规模开展人工智能主题教学很有必要。尽管人们急于实施学术倡议，但对于向全球范围内文化背景各异、对隐私、技术自主性、风险偏好和知识共享有着不同期望的受众教授人工智能课程所面临的独特挑战，却很少有人关注。我们的研究通过关注一个名为“人工智能教育中的文化适应性思维”（CATE-AI）的新框架中的人工智能元素来填补这一空白，以便能够向文化背景各异的学习者传授人工智能概念。如果不能将人工智能教育与文化及其他人类思维类别相结合并使其具有敏感性，可能会导致一些不良影响，包括困惑、人工智能恐惧症、对人工智能的文化偏见、对人工智能技术和人工智能教育的抵触情绪增加。我们讨论并整合了人类行为理论、人工智能应用研究、教育框架和以人为本的人工智能原则，以阐述CATE-AI。在本文的第一部分，我们介绍了一个显著增强版的CATE的开发情况。在第二部分，我们分析了与人工智能相关的新闻文章中的文本数据，以得出见解，为CATE-AI奠定基础并支持我们的研究结果。CATE-AI框架可以作为将人工智能与其社会文化需求相适应和情境化的基础，帮助学习者更有效地学习人工智能主题。
