Learning Through AI-Clones: Enhancing Self-Perception and Presentation Performance
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Abstract (arXiv)
This study examines the impact of AI-generated digital clones with self-images 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. Through the lens of social comparison theory, the results showed that AI clones functioned as positive "role models" for facilitating social comparisons. When comparing the effects on 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, broader observational coverage, 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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 2 of its 121 references that are in the map. First posted on arXiv 23 Oct 2023.
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2 of 121 references.
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Citing papers per year: 2026: 4.
4 papers.
- 2026AI-exhibited Personality Traits Can Shape Human Self-concept through Conversationscited by 3
- 2026Voice "Cloning" is Style Transfercited by 0
- 2026An Interpretable Closed-Loop Intelligent Tutoring System for Multimodal Affective Feedback in Asynchronous Presentation Trainingcited by 0
- 2026Towards Trust Calibration in Socially Interactive Agents: Investigating Gendered Multimodal Behaviors Generation with LLMscited by 0
- 2026Towards Trust Calibration in Socially Interactive Agents: Investigating Gendered Multimodal Behaviors Generation with LLMs19 May 2026
- 2026An Interpretable Closed-Loop Intelligent Tutoring System for Multimodal Affective Feedback in Asynchronous Presentation Training17 May 2026
- 2026Voice "Cloning" is Style Transfer15 May 2026
- 2026AI-exhibited Personality Traits Can Shape Human Self-concept through Conversations19 Jan 2026