Examining the Effects of Motivating Language and Personalization in AI Voice‐Over Educational Videos: From a Social‐Psychological Perspective

ABSTRACT Background The development of AI technology is transforming the production of videos. In particular, voice‐over video lectures can be easily produced with AI voice, which has been found to generate learning comparable to that of human voice. Further, verbal cues, which are social cues—critical factors impacting video‐based learning—expressed with words, can be readily implemented with AI voice. However, the influence of verbal cues in video lectures has been relatively underinvestigated, limiting the optimization of (AI) voice‐over educational videos. Objectives This study aims to explore the effects of motivating language and personalization, both of which are verbal cues—a common but underexplored type of social cues, on learning satisfaction, actual learning outcomes, perceived learning, and rapport in AI voice‐over video lectures. Methods A three (motivating language: autonomy‐supportive vs. controlling vs. neutral) × two (personalization: personalized vs. nonpersonalized) between‐subjects experiment was carried out among 182 university students. Results and Conclusions Motivating language impacted learning satisfaction, retention, perceived learning, and rapport, and the impacts on learning satisfaction and rapport varied with personalization; but collectively, autonomy‐supportive language was more effective than neutral language, whereas controlling language was ineffective, irrespective of personalization; besides, personalization showed benefits only under autonomy‐supportive language in rapport, and instead caused decreased learning satisfaction and rapport under neutral language. The findings have several theoretical implications, including deepening knowledge about verbal cues, extending theories about effects of social cues in multimedia learning, and broadening understanding of motivating teaching. Practical implications for the design of AI voice‐over video lectures were discussed.

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Publication Details

Journal
Journal of Computer Assisted Learning
Published
2026-11-10
DOI
https://doi.org/10.1002/jcal.70375
Primary Topic
Visual and Cognitive Learning Processes
Type
article
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article

Examining the Effects of Motivating Language and Personalization in AI Voice‐Over Educational Videos: From a Social‐Psychological Perspective

Xiaojun Sun, Yamei Zhang, Qingqing Ye
Journal of Computer Assisted Learning
Visual and Cognitive Learning Processes
article

Examining the Effects of Motivating Language and Personalization in AI Voice‐Over Educational Videos: From a Social‐Psychological Perspective

Xiaojun Sun, Yamei Zhang, Qingqing Ye
article en

Abstract

ABSTRACT Background The development of AI technology is transforming the production of videos. In particular, voice‐over video lectures can be easily produced with AI voice, which has been found to generate learning comparable to that of human voice. Further, verbal cues, which are social cues—critical factors impacting video‐based learning—expressed with words, can be readily implemented with AI voice. However, the influence of verbal cues in video lectures has been relatively underinvestigated, limiting the optimization of (AI) voice‐over educational videos. Objectives This study aims to explore the effects of motivating language and personalization, both of which are verbal cues—a common but underexplored type of social cues, on learning satisfaction, actual learning outcomes, perceived learning, and rapport in AI voice‐over video lectures. Methods A three (motivating language: autonomy‐supportive vs. controlling vs. neutral) × two (personalization: personalized vs. nonpersonalized) between‐subjects experiment was carried out among 182 university students. Results and Conclusions Motivating language impacted learning satisfaction, retention, perceived learning, and rapport, and the impacts on learning satisfaction and rapport varied with personalization; but collectively, autonomy‐supportive language was more effective than neutral language, whereas controlling language was ineffective, irrespective of personalization; besides, personalization showed benefits only under autonomy‐supportive language in rapport, and instead caused decreased learning satisfaction and rapport under neutral language. The findings have several theoretical implications, including deepening knowledge about verbal cues, extending theories about effects of social cues in multimedia learning, and broadening understanding of motivating teaching. Practical implications for the design of AI voice‐over video lectures were discussed.

Journal of Computer Assisted LearningVol. 42(6)
Zhengzhou University (CN), Central China Normal University (CN)
Visual and Cognitive Learning Processes
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