What question did this study set out to answer?

This study aimed to compare the effectiveness and learner perception of AI-generated voice cloning against traditional audio recording in medical education.

July 4, 2026Open Access

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

Key Points

This study aimed to compare the effectiveness and learner perception of AI-generated voice cloning against traditional audio recording in medical education.
Randomized trial among fourth- and fifth-year medical students at a French university.
Participants accessed four 10-minute prerecorded lectures, with one group receiving teacher's audio and the other AI-generated audio.
Primary outcome measured was total score on online tests assessing knowledge acquisition and application.
Mean total test scores between AI voice cloning (51.2) and audio recording (51.8) were not significantly different (P=.60).
High satisfaction ratings were reported in both groups.
AI voice cloning reduced production time (22.5 vs 35 minutes).

Abstract

BACKGROUND: Prerecorded 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. OBJECTIVE: This study aimed to evaluate the usefulness and perception of AI-based voice cloning for prerecorded courses in medical pedagogy compared with traditional audio recording. METHODS: We 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. RESULTS: A 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). CONCLUSIONS: We 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.

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