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Assessing Training Outcomes From Levels to Learning Ecosystems in the Age of AI: Thirty Years After Critiquing “The Flawed Four‐Level Evaluation Model”.

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Názov: Assessing Training Outcomes From Levels to Learning Ecosystems in the Age of AI: Thirty Years After Critiquing “The Flawed Four‐Level Evaluation Model”.
Autori: Egan, Toby1 (AUTHOR), Kim, Sewon2 (AUTHOR) sewon.kim@sunyempire.edu, Ghosh, Rajashi3 (AUTHOR)
Zdroj: Human Resource Development Quarterly. Nov2025, p1. 5p.
Predmety: *ARTIFICIAL intelligence, *QUANTITATIVE research, *COST benefit analysis, EVALUATION methodology, EDUCATION research, OUTCOME assessment (Education), DIGITAL technology
Abstrakt: The article focuses on the evolution of training evaluation frameworks in the context of advancements in artificial intelligence (AI) and the enduring critique of the traditional four-level evaluation model established by Donald Kirkpatrick. It highlights Elwood F. Holton III's influential critique of this model, which he argued lacked theoretical grounding and failed to adequately explain the complexities of training outcomes. The discussion traces the development of alternative evaluation approaches, including the introduction of Return on Investment (ROI) and Return on Expectations (ROE), and emphasizes the potential of AI to enhance training evaluation by providing continuous, data-driven insights. The article ultimately calls for a re-evaluation of training evaluation practices to ensure they remain relevant and effective in a rapidly changing technological landscape. [Extracted from the article]
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Abstrakt:The article focuses on the evolution of training evaluation frameworks in the context of advancements in artificial intelligence (AI) and the enduring critique of the traditional four-level evaluation model established by Donald Kirkpatrick. It highlights Elwood F. Holton III's influential critique of this model, which he argued lacked theoretical grounding and failed to adequately explain the complexities of training outcomes. The discussion traces the development of alternative evaluation approaches, including the introduction of Return on Investment (ROI) and Return on Expectations (ROE), and emphasizes the potential of AI to enhance training evaluation by providing continuous, data-driven insights. The article ultimately calls for a re-evaluation of training evaluation practices to ensure they remain relevant and effective in a rapidly changing technological landscape. [Extracted from the article]
ISSN:10448004
DOI:10.1002/hrdq.70007