Human-centered artificial intelligence in education: Seeing the invisible through the visible

• Yang, J.H., Stephen

  Department of Computer Science and Information Engineering, National Central University

• Ogata, Hiroaki

  Academic Center for Computing and Media Studies, Kyoto University

• Matsui, Tatsunori

  School of Human Sciences, Waseda University

• Chen, Nian-Shing

  Department of Applied Foreign Languages, National Yunlin University of Science and Technology

抄録

The inevitable rise and development of artificial intelligence (AI) was not a sudden occurrence. The greater the effect that AI has on humans, the more pressing the need is for us to understand it. This paper addresses research on the use of AI to evaluate new design methods and tools that can be leveraged to advance AI research, education, policy, and practice to improve the human condition. AI has the potential to educate, train, and improve the performance of humans, making them better at their tasks and activities. The use of AI can enhance human welfare in numerous respects, such as through improving the productivity of food, health, water, education, and energy services. However, the misuse of AI due to algorithm bias and a lack of governance could inhibit human rights and result in employment, gender, and racial inequality. We envision that AI can evolve into human-centered AI (HAI), which refers to approaching AI from a human perspective by considering human conditions and contexts. Most current discussions on AI technology focus on how AI can enable human performance. However, we explore AI can also inhibit the human condition and advocate for an in-depth dialog between technology- and humanity-based researchers to improve understanding of HAI from various perspectives.

収録刊行物

• Computers and Education: Artificial Intelligence

  Computers and Education: Artificial Intelligence 2 100008-, 2021

  Elsevier BV

キーワード

• Human-centered AI
• Sustainable AI
• Explainable AI
• Interpretable ML
• Smart learning

詳細情報

• CRID: 1051131408423100032
• NII論文ID: 120006954091
• ISSN: 2666920X
• DOI: 10.1016/j.caeai.2021.100008
• HANDLE: 2433/261148
• Web Site: https://api.elsevier.com/content/article/PII:S2666920X21000023?httpAccept=text/xml
• Web Site: https://api.elsevier.com/content/article/PII:S2666920X21000023?httpAccept=text/plain
• 本文言語コード: en
• 資料種別: journal article
• データソース種別:
  • IRDB
  • Crossref
  • CiNii Articles
  • KAKEN