{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T23:25:36Z","timestamp":1791242736909,"version":"4.1.0"},"reference-count":88,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Educ"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Video-sharing sites such as YouTube (Google) and TikTok (ByteDance) have become indispensable resources for learners and educators. The recent growth in generative artificial intelligence (AI) tools, however, has resulted in low-quality, AI-generated material (commonly called \u201cslop\u201d) cluttering these platforms and competing with authoritative educational materials. The extent to which slop has polluted science education video content is unknown, as are the specific hazards to learning from purportedly educational videos made by AI without the use of human discretion.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to advance a formal definition of slop (based on the recent theoretical construct of \u201ccareless speech\u201d), to identify its qualitative characteristics that may be problematic for learners, and to gauge its prevalence among preclinical biomedical science (medical biochemistry and cell biology) videos on YouTube and TikTok. We also examined whether any quantitative features of video metadata correlate with the presence of slop.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n An automated search of publicly available YouTube and TikTok videos related to 10 search terms was conducted in February and March 2025. After exclusion of duplicates, off-topic, and non-English results, videos were screened, and those suggestive of AI were flagged. The flagged videos were subject to a 2-stage qualitative content analysis to identify and code problematic features before an assignment of \u201cslop\u201d was made. Quantitative viewership data on all videos in the study were scraped using automated tools and compared between slop videos and the overall population.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n We define \u201cslop\u201d according to the degree of human care in production. Of 1082 videos screened (814 YouTube, 268 TikTok), 57 (5.3%) were deemed probably AI-generated and low-quality. From qualitative analysis of these and 6 additional AI-generated videos, we identified 16 codes for problematic aspects of the videos as related to their format or contents. These codes were then mapped to the 7 characteristics of careless speech identified earlier. Analysis of view, like, and comment rates revealed no significant difference between slop videos and the overall population.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n We find slop to be not especially prevalent on YouTube and TikTok at this time. These videos have comparable viewership statistics to the overall population, although the small dataset suggests this finding should be interpreted with caution. From the slop videos that were identified, several features inconsistent with best practices in multimedia instruction were defined. Our findings should inform learners seeking to avoid low-quality material on video-sharing sites and suggest pitfalls for instructors to avoid when making high-quality educational materials with generative AI.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/80084","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T20:25:53Z","timestamp":1763670353000},"page":"e80084-e80084","source":"Crossref","is-referenced-by-count":16,"title":["AI-Generated \u201cSlop\u201d in Online Biomedical Science Educational Videos: Mixed Methods Study of Prevalence, Characteristics, and Hazards to Learners and Teachers"],"prefix":"10.2196","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9994-8859","authenticated-orcid":false,"given":"Eric M","family":"Jones","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4389-3182","authenticated-orcid":false,"given":"Jane D","family":"Newman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8348-5237","authenticated-orcid":false,"given":"Boyun","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6041-3830","authenticated-orcid":false,"given":"Emily J","family":"Fogle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"issue":"4","key":"R1","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.1007\/s40670-020-01016-w","article-title":"YouTube as an educational resource in medical education: a scoping review","volume":"30","author":"Curran","journal-title":"Med Sci Educ"},{"issue":"2","key":"R2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10639-023-11904-4","article-title":"Microlearning through TikTok in higher education. 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