AI versus human-generated voices and avatars: rethinking user engagement and cognitive load
Abstract As AI-generated content becomes increasingly prevalent in educational settings, understanding its impact on students remains an urgent yet underexplored issue. Although recent studies have begun to explore AI-generated videos, few have specifically examined the pedagogical potential of combining AI voices and avatars. This study investigates the differences in user engagement and cognitive load induced by AI-generated short videos compared to human-based ones in the context of foreign language learning. Using a 2 × 2 experimental design, the study explored four combinations: AI voice & AI avatar, AI voice & Human avatar, Human voice & AI avatar, and Human voice & Human avatar. Results from linear mixed models and Fuzzy C-Means clustering revealed that while both AI voice and AI avatar can improve user engagement independently, a significant improvement in engagement is only observed when both voice and avatar are AI-generated. Similarly, while AI voice and AI avatar can reduce extraneous cognitive load independently, a significant reduction in extraneous cognitive load is only observed when both voice and avatar are AI-generated. This study offers valuable insights for integrating AI-generated short videos into foreign language learning and the broader educational landscape, paving the way for AI-generated content and innovative approaches to multimedia learning.
Authors
- Pedro Bem‐Haja (ORCID: https://orcid.org/0000-0002-7547-5743)
- Luís Pedro (ORCID: https://orcid.org/0000-0003-1763-8433)
- Margarida Lucas (ORCID: https://orcid.org/0000-0002-7438-5287)
- Yidi Zhang (ORCID: https://orcid.org/0000-0001-5557-0906)
Institutions
- Centre for Health Technology and Services Research (PT)
- University of Aveiro (PT)
Publication Details
- Journal
- Education and Information Technologies
- Published
- 2025-06-18
- DOI
- https://doi.org/10.1007/s10639-025-13654-x
- Citations
- 5
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 8.47
Funders
- Universidade de Aveiro
- Fundação para a Ciência e a Tecnologia