Decoding AI ethics from Users' lens in education: A systematic review

Qin An, Jingmei Yang, Xiaoshu Xu, Yunfeng Zhang, Huanhuan Zhang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In recent years, Artificial Intelligence (AI) has witnessed remarkable expansion, greatly benefiting the education sector. Nonetheless, this advancement brings forth several ethical dilemmas. The existing research on these ethical concerns within the educational framework is notably scarce, particularly when viewed from a user's standpoint. This research systematically reviewed 17 empirical articles from January 2018 to June 2023, sourced from peer-reviewed journals and conferences, to outlined existing ethical framework in Artificial Intelligence in Education (AIED), identify related concerns from user's perspectives, and construct Ethics Guideline for AIED. The finding revealed that certain ethical aspects, including the ethics of learning analytics and the ethics of algorithms in AIED, are often neglected in the existing ethical frameworks, principles, and standards for AIED. Based on the blank between existing ethical frameworks and ethic concerns from user's perspectives, the research proposes more inclusive and thoughtfully Ethics Guideline for AIED. The study also provides actionable recommendations for multiple stakeholders, emphasizing the need for guidelines that address user-centered concerns. In addition, How this Ethics Guideline for AIED could be developed is discussed, along with outlining potential avenues for future research.

Original languageEnglish
Article numbere39357
JournalHeliyon
Volume10
Issue number20
DOIs
Publication statusPublished - 30 Oct 2024

Keywords

  • AI ethics
  • AI in education
  • AIED
  • Application of AI in education
  • Systematic review
  • perspective

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