FairnessBiasTransparencycs.CY
The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges
Okan Bulut, Maggie Beiting-Parrish, Jodi M. Casabianca, Sharon C. Slater, Hong Jiao, Dan Song, Christopher M. Ormerod, Deborah Gbemisola Fabiyi, Rodica Ivan, Cole Walsh, Oscar Rios, Joshua Wilson, Seyma N. Yildirim-Erbasli, Tarid Wongvorachan, Joyce Xinle Liu, Bin Tan, Polina Morilova
June 27, 2024
Chinese/English Journal of Educational Measurement and Evaluation
82 citations
8 influential
Key Takeaways
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AI can improve educational assessment through automation and personalization, enabling faster scoring, content analysis, and customized feedback that provides students with timely insights into their performance.
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Algorithmic bias and lack of transparency in AI systems pose serious risks to educational equity, as opaque decision-making processes can perpetuate existing inequalities and unfairly affect student assessment outcomes.
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Validity and reliability of AI-powered assessments require careful scrutiny, as the paper identifies concerns about whether these tools actually measure what they claim to measure and whether results are consistent and trustworthy.
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Multiple stakeholders—including educators, policymakers, and professional organizations like the National Council of Measurement in Education—are developing ethical guidelines to establish standards for responsible AI use in education, indicating growing recognition of the need for oversight.
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Beyond fairness concerns, AI in education raises broader challenges including automation bias and environmental impact, suggesting that responsible AI deployment requires attention to multiple ethical dimensions beyond just preventing discrimination.
Abstract
The integration of artificial intelligence (AI) in educational measurement has revolutionized assessment methods, enabling automated scoring, rapid content analysis, and personalized feedback through machine learning and natural language processing. These advancements provide timely, consistent feedback and valuable insights into student performance, thereby enhancing the assessment experience. However, the deployment of AI in education also raises significant ethical concerns regarding validity, reliability, transparency, fairness, and equity. Issues such as algorithmic bias and the opacity of AI decision-making processes pose risks of perpetuating inequalities and affecting assessment outcomes. Responding to these concerns, various stakeholders, including educators, policymakers, and organizations, have developed guidelines to ensure ethical AI use in education. The National Council of Measurement in Education's Special Interest Group on AI in Measurement and Education (AIME) also focuses on establishing ethical standards and advancing research in this area. In this paper, a diverse group of AIME members examines the ethical implications of AI-powered tools in educational measurement, explores significant challenges such as automation bias and environmental impact, and proposes solutions to ensure AI's responsible and effective use in education.