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Technological revolution in evaluation: Artificial intelligence and the adherence to evaluation standards

Kai Rompczyk

Evaluation2025https://doi.org/10.1177/13563890251331066article
AJG 2ABDC B
Weight
0.41

Abstract

In the advent of revolutionary technological change, perpetuated by the publishing of Large Language Models like ChatGPT, the article discusses the transformative effects of artificial intelligence on the application of evaluation standards. Amid a debate on the integration of artificial intelligence technology in evaluation processes, there is a lack of understanding about how artificial intelligence usage impacts adherence to these standards. This article endeavors to bridge this gap by examining the future trends of the ongoing technological revolution, assessing the potential for evaluation tasks to be augmented or replaced by artificial intelligence systems and conducting a thorough opportunity and risk analysis regarding the adherence to evaluation standards. The article addresses both opportunities, such as increased efficiency and analytical depth, and challenges, including structural errors and biases. It concludes with suggestions for evaluators, emphasizing the balance between leveraging artificial intelligence’s capabilities and maintaining critical oversight and ethical responsibility in evaluation practices.

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https://doi.org/https://doi.org/10.1177/13563890251331066

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@article{kai2025,
  title        = {{Technological revolution in evaluation: Artificial intelligence and the adherence to evaluation standards}},
  author       = {Kai Rompczyk},
  journal      = {Evaluation},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/13563890251331066},
}

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Technological revolution in evaluation: Artificial intelligence and the adherence to evaluation standards

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.