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AI in education risk assessment mechanism analysis

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  • Zhuo Luo
  • Xuedong Zhang

Abstract

Artificial intelligence (AI) has had a significant impact on several sectors and fields. Failure Modes and Effects Analysis (FMEA) is a powerful risk management and prevention tool that can help companies identify and address any weaknesses in their practices. The Risk Priority Number (RPN) approach has been criticized for its flaws since it calculates the product of severity, occurrence, and detection ratings of threats. By considering a variety of criteria and aspects, and by thoughtfully and carefully combining expert perspectives, the proposed FMEA model may more accurately evaluate the risks associated with AI in Education. Picture Fuzzy Sets (PFSs) and Grey Relational Analysis-TOPSIS (GRA-TOPSIS) are combined to achieve this. The goal of this research is to use an updated FMEA model to assess and rank the risk hierarchy of the seven identified hazards. According to the data, algorithmic risk is the most significant issue that requires urgent attention. Relevant constraints and suggestions are also provided to encourage research on the dangers of AI in Education. Suggestions include creating a national or international regulatory authority to control the use of artificial intelligence in education, promoting the use of the FMEA technique as a common framework for assessing the risks connected to AI in Education.

Suggested Citation

  • Zhuo Luo & Xuedong Zhang, 2025. "AI in education risk assessment mechanism analysis," Applied Economics, Taylor & Francis Journals, vol. 57(16), pages 1949-1961, April.
    • Handle: RePEc:taf:applec:v:57:y:2025:i:16:p:1949-1961
    DOI: 10.1080/00036846.2024.2321835
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