Learning through AI-clones: Enhancing self-perception and presentation performance
This study examines the impact of AI-generated digital clones with self-images (AI-clones) on enhancing perceptions and skills in online presentations. A mixed-design experiment with 44 international students compared self-recording videos (self-recording group) to AI-clone videos (AI-clone group) for online English presentation practice. AI-clone videos were generated using voice cloning, face swapping, lip-syncing, and body-language simulation, refining the repetition, filler words, and pronunciation of participants' original presentations. The results, viewed through the lens of social comparison theory, showed that AI clones functioned as positive “role models” for encouraging positive social comparisons. Regarding self-perceptions, speech qualities, and self-kindness, the self-recording group showed an increase in pronunciation satisfaction. However, the AI-clone group exhibited greater self-kindness, a wider scope of self-observation, and a meaningful transition from a corrective to an enhancive approach in self-critique. Moreover, machine-rated scores revealed immediate performance gains only within the AI-clone group. Considering individual differences, aligning interventions with participants’ regulatory focus significantly enhanced their learning experience. These findings highlight the theoretical, practical, and ethical implications of AI clones in supporting emotional and cognitive skill development. • Immediate speech performance improvements were only detected in the AI-clone group. • The AI-clone group showed more self-kindness toward themselves. • AI-clones may help identify "unconscious" nuances in speech practice. • The self-recording group exhibited significantly greater pronunciation satisfaction. • Regulatory focus is important for speech practice using AI-clone videos.
Authors
- Yun Huang (ORCID: https://orcid.org/0000-0003-0399-8032)
- Qingxiao Zheng (ORCID: https://orcid.org/0000-0003-0368-0032)
- Zepeng Chen (ORCID: https://orcid.org/0009-0008-3560-605X)
Institutions
- University of Illinois Urbana-Champaign (US)
- Millennium Engineering and Integration (United States) (US)
- University at Buffalo, State University of New York (US)
Publication Details
- Journal
- Computers in Human Behavior Artificial Humans
- Published
- 2025-02-03
- DOI
- https://doi.org/10.1016/j.chbah.2025.100117
- Citations
- 5
- Primary Topic
- Educational Games and Gamification
- Type
- article
- Field-Weighted Citation Impact
- 17.22