From recorded to <scp>AI</scp> ‐generated instructional videos: A comparison of learning performance and experience

Abstract Generative AI (GAI) and AI‐generated content (AIGC) have been increasingly involved in our work and daily life, providing a new learning experience for students. This study examines whether AI‐generated instructional videos (AIIV) can facilitate learning as effectively as traditional recorded videos (RV). We propose an instructional video generation pipeline that includes customized GPT (Generative Pre‐trained Transformer), text‐to‐speech and lip synthesis techniques to generate videos from slides and a clip or a photo of a human instructor. Seventy‐six students were randomly assigned to learn English words using either AIIV or RV, with performance assessed by retention, transfer and subjective measures from cognitive, emotional, motivational and social perspectives. The findings indicate that the AIIV group performed as well as the RV group in facilitating learning, with AIIV showing higher retention but no significant differences in transfer. RV was found to offer a stronger sense of social presence. Although other subjective measures were similar between the two groups, AIIV was perceived as slightly less favourable. However, the AIIV was still found to be moderately to highly attractive, addressing concerns related to the uncanny valley effect. This research demonstrates that AIGC can be an effective tool for learning, offering valuable implications for the use of GAI in educational settings. Practitioner notes What is already known about this topic Instructional videos, especially those featuring a teacher's presence, have been widely used in second language learning to facilitate learning. Producing instructional videos is costly and burdensome. Generative AI has great potential for generating educational content. What this paper adds An AI‐generated instructional video (including generated lecture text, voice and appearance) demonstrated greater improvement in students' retention performance in English word learning than a traditional recorded video. Students perceived no significant differences between the AI‐generated instructional video and recorded video in satisfaction, motivation, trust, cognitive load, emotions and parasocial interaction dimensions, although the AI‐generated instructional video group reported slightly lower values. Despite AI‐generated instructional video eliciting a significantly lower value of social presence than recorded video, it led to a reduction in cognitive load and better performance. Implications for practice and/or policy We recommend using the AI‐generated instructional video in both physical and online classes for its positive effects on both learning achievement and learning experience. The findings indicate the equivalence principle in AI‐generated content, highlighting that the appearance, voice and lecture text generated by current AI technology have reached a certain level of quality.

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Institutions

Publication Details

Journal
British Journal of Educational Technology
Published
2024-10-28
DOI
https://doi.org/10.1111/bjet.13530
Citations
27
Primary Topic
Visual and Cognitive Learning Processes
Type
article
Field-Weighted Citation Impact
13.70

Funders

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article

From recorded to AI ‐generated instructional videos: A comparison of learning performance and experience

Wenlan Zhang, Tao Xu, Yu Liu, Jie Bai et al.
27 citations
British Journal of Educational Technology
Visual and Cognitive Learning Processes
13.70
article

From recorded to AI ‐generated instructional videos: A comparison of learning performance and experience

Wenlan Zhang, Tao Xu, Yu Liu, Jie Bai, Yun Zhou, Yaru Jin, Yu-Yuan Qu
article en
27 citations

Abstract

Abstract Generative AI (GAI) and AI‐generated content (AIGC) have been increasingly involved in our work and daily life, providing a new learning experience for students. This study examines whether AI‐generated instructional videos (AIIV) can facilitate learning as effectively as traditional recorded videos (RV). We propose an instructional video generation pipeline that includes customized GPT (Generative Pre‐trained Transformer), text‐to‐speech and lip synthesis techniques to generate videos from slides and a clip or a photo of a human instructor. Seventy‐six students were randomly assigned to learn English words using either AIIV or RV, with performance assessed by retention, transfer and subjective measures from cognitive, emotional, motivational and social perspectives. The findings indicate that the AIIV group performed as well as the RV group in facilitating learning, with AIIV showing higher retention but no significant differences in transfer. RV was found to offer a stronger sense of social presence. Although other subjective measures were similar between the two groups, AIIV was perceived as slightly less favourable. However, the AIIV was still found to be moderately to highly attractive, addressing concerns related to the uncanny valley effect. This research demonstrates that AIGC can be an effective tool for learning, offering valuable implications for the use of GAI in educational settings. Practitioner notes What is already known about this topic Instructional videos, especially those featuring a teacher's presence, have been widely used in second language learning to facilitate learning. Producing instructional videos is costly and burdensome. Generative AI has great potential for generating educational content. What this paper adds An AI‐generated instructional video (including generated lecture text, voice and appearance) demonstrated greater improvement in students' retention performance in English word learning than a traditional recorded video. Students perceived no significant differences between the AI‐generated instructional video and recorded video in satisfaction, motivation, trust, cognitive load, emotions and parasocial interaction dimensions, although the AI‐generated instructional video group reported slightly lower values. Despite AI‐generated instructional video eliciting a significantly lower value of social presence than recorded video, it led to a reduction in cognitive load and better performance. Implications for practice and/or policy We recommend using the AI‐generated instructional video in both physical and online classes for its positive effects on both learning achievement and learning experience. The findings indicate the equivalence principle in AI‐generated content, highlighting that the appearance, voice and lecture text generated by current AI technology have reached a certain level of quality.

British Journal of Educational TechnologyVol. 56(4)
Northwestern Polytechnical University (CN), Shaanxi Normal University (CN)
Northwestern University, National Natural Science Foundation of China, Northwestern Polytechnical University, National Key Research and Development Program of China
Quality Education
Openalex Percentile: Top 1%
Visual and Cognitive Learning Processes
13.70
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