Generative artificial intelligence (Gen-AI) tools have a significant impact on the creation of audiovisual content. Although these tools are still at an early stage in video production, there are tools such as Sora (OpenAI) that demonstrate the great potential of Gen-AI to create advanced audiovisual content. This study evaluates through a comparative analysis the level of realism, attractiveness and composition of the videos generated by Sora compared to real videos. Using a questionnaire validated by experts (n=12), a quasi-experiment was conducted with college students (n=62) who were divided into two groups: a control group that visualized real videos from YouTube and an experimental group that visualized videos created with the Sora tool. The results show that attractiveness, particularly the elements of lighting, saturation and color, are key factors in the recognition of a Gen-AI video. The paper concludes that Gen-AI tools should focus on improving the attractive elements to achieve more consistent and natural results.
Sanchez-Acedo, A., Carbonell-Alcocer, A., Cascarano, P., Hajahmadi, S., Gertrudix, M., Marfia, G. (2026). Exploring the impact of visual components on the perceived realism of generative AI videos. ONLINE JOURNAL OF COMMUNICATION AND MEDIA TECHNOLOGIES, 16(1), 1-21 [10.30935/ojcmt/17737].
Exploring the impact of visual components on the perceived realism of generative AI videos
Cascarano P.;Hajahmadi S.;Marfia G.
2026
Abstract
Generative artificial intelligence (Gen-AI) tools have a significant impact on the creation of audiovisual content. Although these tools are still at an early stage in video production, there are tools such as Sora (OpenAI) that demonstrate the great potential of Gen-AI to create advanced audiovisual content. This study evaluates through a comparative analysis the level of realism, attractiveness and composition of the videos generated by Sora compared to real videos. Using a questionnaire validated by experts (n=12), a quasi-experiment was conducted with college students (n=62) who were divided into two groups: a control group that visualized real videos from YouTube and an experimental group that visualized videos created with the Sora tool. The results show that attractiveness, particularly the elements of lighting, saturation and color, are key factors in the recognition of a Gen-AI video. The paper concludes that Gen-AI tools should focus on improving the attractive elements to achieve more consistent and natural results.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


