Evidence map›Paper›PMID 42390914›Full record

Trial reportJMIR medical education2026

Voice Cloning Using AI vs Traditional Audio Recording for Prerecorded Courses in Medical Pedagogy: Randomized Controlled Trial.

Antoine Gavoille, Fabien Subtil, Mikaïl Nourredine

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in JMIR medical education, 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

3 authors.

Antoine GavoilleLaboratoire de Biométrie et Biologie Evolutive, Université Claude Bernard Lyon 1, Lyon, Rhône-Alpes, France.ORCID 0000-0002-9118-0849
Fabien SubtilLaboratoire de Biométrie et Biologie Evolutive, Université Claude Bernard Lyon 1, Lyon, Rhône-Alpes, France.ORCID 0000-0003-1955-2019
Mikaïl NourredineLaboratoire de Biométrie et Biologie Evolutive, Université Claude Bernard Lyon 1, Lyon, Rhône-Alpes, France.ORCID 0000-0002-0952-0091

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrerecorded courses are increasingly used in medical education, and audio quality is known to influence learners' comprehension and engagement. Traditional audio recording, however, is time-consuming and may be uncomfortable for some educators. Advances in generative artificial intelligence (AI) now allow for realistic voice cloning, but its pedagogical value compared with conventional recording has not been assessed.

objectiveThis study aimed to evaluate the usefulness and perception of AI-based voice cloning for prerecorded courses in medical pedagogy compared with traditional audio recording.

methodsWe conducted a randomized trial among fourth- and fifth-year medical students at a French university. Participants accessed four 10-minute prerecorded lectures on critical appraisal of medical research. The control group received lectures with audio recorded by the teacher, whereas the intervention group received audio generated from the teacher's cloned voice using a commercial AI text-to-speech system, with identical slides and scripts. The primary outcome was the total score on 2 online tests (17 multiple-choice questions on knowledge acquisition and 15 multiple-choice questions on knowledge application). Secondary outcomes included satisfaction ratings, course viewing metrics, and production time.

resultsA total of 88 students were randomized, and 64 (72.7%) watched at least 15 seconds of video and, thus, were included in the modified intention-to-treat population. Mean total test scores did not differ significantly between the AI voice cloning and audio recording groups (51.2, SD 4.9 vs 51.8, SD 6.9 out of 100; adjusted mean difference -0.9, 95% CI -2.7 to 4.4; P=.60). Satisfaction was high in both groups. Production time was shorter with AI (22.5, SD 6.45 vs 35, SD 7.07 minutes per video).

conclusionsWe did not detect a significant difference in learning outcomes or satisfaction between AI voice cloning and conventional recording, whereas AI voice cloning reduced production time, making it a practical alternative for prerecorded medical courses. Nevertheless, some students may perceive synthetic voices as less authentic, representing a potential barrier to widespread adoption.

Indexed as

Artificial IntelligenceEducation, Medical, UndergraduateTeachingAdultEducational MeasurementFemaleGenerative Artificial IntelligenceHumansMaleStudents, MedicalAIartificial intelligenceaudio qualitygenerative artificial intelligencemedical pedagogyprerecorded coursevoice cloning

Identifiers

PMID42390914
PMCPMC13376843

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Registered trials

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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.