Date
Publisher
DASH Harvard
I review opportunities and threats that widely accessible Artificial Intelligence (AI)-powered services present for educational statistics and measurement. Algorithmic and computational advances continue to improve approaches to item generation, scale maintenance, test security, test scoring, and score reporting. Predictable misuses of AI for these purposes will result in biased scores, construct underrepresentation, and differential impact over time. Recent efforts to develop standards for AI use in testing like those of Burstein are promising. I argue that similar efforts to develop AI standards for educational measurement will benefit from increased attention to the context of test use and explicit commitment to ongoing monitoring of bias and scale drift over time.
Study at a glance
- Question: What opportunities and threats does artificial intelligence present for educational testing and measurement?
- Setting and data: Not stated in the abstract
- Method: Literature review
Why it matters
Widely accessible AI presents opportunities to improve educational measurement. However, predictable misuses of AI will result in biased scores, construct underrepresentation, and differential impact over time.
Key findings
- In educational measurement, artificial intelligence advances continue to improve test question creation, test security, automated scoring, score reporting, and score scale maintenance.
- Predictable misuses of AI in educational testing will result in biased test scores, incomplete measurement of target skills, and differential impacts over time.
- Efforts to develop AI standards for educational testing are promising and benefit from ongoing monitoring of bias, score drift, and testing context.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
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Study design
