Evaluating Youtube™ videos on ruminal acidosis: integrating human and AI assessment for quality and reliability.

• Can Ayhan Kaya Dicle University, Vocational School of Agriculture. Dicle, Türkiye. https://orcid.org/0000-0001-8226-7603

DOI: https://doi.org/10.52973/rcfcv-e361811

Keywords: Ruminal acidosis, YouTube, Veterinary education, artificial intelligence, video content quality

Abstract

Ruminal acidosis is a common metabolic disorder in ruminants that carries significant health and economic consequences. As online platforms such as YouTube™ are increasingly used for veterinary guidance, assessing the quality and reliability of digital content has become essential. In this cross-sectional study, 71 publicly available English-language videos on ruminal acidosis were analyzed using a combined human–AI evaluation approach. Video content was assessed with three validated tools: the Video Content Quality Index, the Global Quality Scale, and a modified DISCERN instrument, measuring scientific accuracy, educational value, and reliability. Viewer engagement, including likes, comments,  and viewing rates, was also recorded. The average Video Content Quality Index, the Global Quality Scale , and DISCERN scores were 9.80, 3.12, and 2.87, respectively, reflecting moderate overall quality and reliability. Videos uploaded by professional associations scored highest, whereas content from commercial sources had the lowest Video Content Quality Index values. Inter rater comparison revealed systematic differences between human reviewers and AI, with AI assigned higher Video Content Quality Index, the Global Quality Scale, and DISCERN scores compared to the human reviewer highlighting its potential to standardize evaluations across large datasets. Importantly, viewer engagement metrics did not consistently correlate with video quality, emphasizing that popularity does not equate to scientific rigor. These findings underscore the necessity for veterinary professionals and educators to actively contribute accurate, evidence-based content online. Integrating AI- assisted evaluation provides a scalable, consistent approach to identifying  high-quality  educational  resources,  offering a promising tool for enhancing digital veterinary education and supporting informed decision-making among students, practitioners, and livestock producers.

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