This report demonstrates that an artificial intelligence-based content tagging approach offers a viable, efficient, and scalable alternative to the traditional human-driven classification process used for the O*NET Reference List.
By leveraging a structured LLM-prompt design, refined content category definitions, and a multi-run consensus strategy, the proposed method achieves high levels of consistency and strong alignment with SME judgments while substantially increasing tagging flexibility through multi-category assignment.
The introduction of a dedicated Artificial Intelligence content category enables the O*NET Program to systematically capture and highlight the growing body of research leveraging Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) in conjunction with O*NET data, products, and/or tools, ensuring the Reference List remains aligned with emerging technological trends.
Updated content tags for 2,850 references are included in the file below.
