Students prefer personalized, AI-generated educational videos over non-personalized, human-recorded videos.

Bill Tomlinson, Rebecca W Black, Donald J Patterson, André van der Hoek, Julie Ferguson, Matthew J Bietz

Abstract read

In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Bill TomlinsonDepartment of Informatics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA, USA. wmt@uci.edu.

Rebecca W BlackDepartment of Informatics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA, USA.

Donald J PattersonDepartment of Informatics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA, USA.

André van der HoekDepartment of Informatics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA, USA.

Julie FergusonDepartment of Earth System Science, University of California, Irvine, CA, USA.

Matthew J BietzDepartment of Informatics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA, USA.

Funding

National Science Foundation DRL-2418715

6 · The paper itself

Abstract

Personalization is a well-established driver of student engagement, yet delivering individualized instruction at scale remains a challenge in online education. Recent advances in generative AI make scalable personalization feasible, but AI-generated educational videos are often perceived as inferior to human-recorded content. This tension raises the question: how does the value of personalization compare to that of human presence? We investigated this question through a field deployment in two offerings of a large undergraduate online course (493 respondents). AI-generated personalized videos served as the primary instructional modality, alongside a smaller set of non-personalized human-recorded and AI-generated videos. At the end of the course, students ranked preferences across personalized and non-personalized formats and human-recorded versus AI-generated content. In a direct comparison, students preferred AI-generated personalized videos over human-recorded non-personalized videos (mean rank 2.26 vs. 2.69; Wilcoxon signed-rank test, [Formula: see text]). Across analyses, students preferred personalized over non-personalized content, and human-recorded over AI-generated content. The magnitude of the personalization effect substantially exceeded the effect of human presence. Open-ended responses highlighted perceived benefits of relevance and conciseness in personalized AI videos, alongside concerns about naturalness and expressiveness. Together, these findings suggest that personalization can outweigh human presence in students' evaluations of educational video.

Indexed as

Education, DistanceStudentsVideo RecordingAdultFemaleGenerative Artificial IntelligenceHumansMaleYoung AdultEducational videoField deploymentGenerative AIOnline educationPersonalized learningStudent preference

Identifiers

PMID42129511

DOI10.1038/s41598-026-52798-9

PMCPMC13357537

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.