Date
Publisher
arXiv
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.
Study at a glance
- Question: What are the opportunities and ethical challenges of using artificial intelligence in educational measurement?
- Setting and data: Not stated in the abstract
- Method: Not stated in the abstract
Why it matters
AI has revolutionized educational measurement with automated scoring and personalized feedback, but it also raises significant ethical concerns. Algorithmic bias and opaque decision-making processes pose risks of perpetuating inequalities and affecting assessment outcomes.
Key findings
- In educational measurement, artificial intelligence enables automated scoring and rapid analysis, providing students and teachers with timely, consistent feedback on performance.
- Biased algorithms and hidden decision-making processes in educational assessment tools risk perpetuating inequalities and compromising the fairness of student evaluations.
- Using artificial intelligence for testing introduces critical challenges surrounding test validity, reliability, automation bias, and negative environmental impacts.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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