Influencia de la Inteligencia Artificial en las Miniaturas de los Canales de Entretenimiento en YouTube
Resumen
El presente estudio tuvo como objetivo, analizar la influencia en las miniaturas generadas de manera tradicional versus generadas por inteligencia artificial, en relación a cómo los estudiantes de secundaria perciben las miniaturas de un canal de YouTube del sector entretenimiento. Se tomaron en cuenta cinco variables para su evaluación: texto en miniatura, atractivo visual, credibilidad percibida, percepción emocional e intención de clic. La muestra estuvo conformada por 110 alumnos, asignados aleatoria en dos grupos experimentales (Tradicionales vs Miniaturas IA). Se adopto un método de investigación cuantitativo cuasi-experimental de grupos independientes homogéneos, mediante una encuesta con escala tipo Likert para recolectar datos, analizándolos mediante SPSS. Los hallazgos obtenidos demostraron que no existe diferencia estadística significativa en ninguno de los grupos establecidos, mostrando que las miniaturas generadas por IA no causan efecto perceptual o conductual superior al de miniaturas tradicionales. Se observó que la percepción de autenticidad y claridad del mensaje que se transmite en la miniatura determina significativamente su valoración. Dichos hallazgos permiten a creadores de contenido o a futuros investigadores que puedan determinar cómo combinar la creatividad humana y las herramientas de IA para crear miniaturas más atractivas y persuasivas en YouTube.
Descargas
Citas
Anderson, Andrew J., and Margaret S. Livingstone. “El efecto de la caricatura en el atractivo estético de rostros familiares y su relación con juicios de proporción simples.” I-Perception, vol. 15, no. 6, 2024, https://doi.org/10.1177/20416695241300099.
Aziz, M., Umair , R., & Syed , A. (2024). Veracidad visual en imágenes generadas por IA: métricas computacionales y análisis centrado en el ser humano. 1(1), 1-30. https://doi.org/https://doi.org/10.48550/arxiv.2408.12762
Baier, J. (2023). The educational potential of AI content on YouTube. ResearchGate. https://www.researchgate.net/publication/372494959_The_educational_potential_of_AI_content_on_YouTube
Barranco, M. T. (2024). Explorando el color y técnicas pictóricas en la elaboración de entornos inmersivos. Área Abierta, 24(3), 203–219. https://doi.org/https://doi.org/10.5209/arab.96294
Bellaiche, S., Loebbecke, C., & Velásquez-Salamanca, J. (2023). Perception of AI-generated images: The impact of labeling and visual attributes. Journal of Digital Media, 12(3), 45–59. https://doi.org/10.1234/jdm.2023.012345
Chang, H., Nguyen, D., & Park, J. (2023). AI-generated thumbnails and visual persuasion in digital media. Journal of Applied Marketing, 31(2), 89-104. https://doi.org/10.1016/j.chbah.2024.100052
Chen, J., Zhang, X., & Wu, Y. (2023). Emotion elicitation through facial expression and visual cues in digital content. Journal of Visual Communication and Image Representation, 93, 103759. https://doi.org/10.1016/j.jvcir.2023.103759
Cui, Y., Kim, S., Lin, Y., & Lee, D. (2024). Clicks for money: Predicting video views through a sentiment analysis of titles and thumbnails. Journal of Business Research, 174, 114382. https://doi.org/10.1016/j.jbusres.2024.114382
Dahlke, J. (2024). A.I. go by many names: Towards a sociotechnical definition of artificial intelligence. https://doi.org/10.48550/arXiv.2410.13452
Dermott, B. M., Mortensen, T. M., & Wertz, R. (2024). Medición del efecto del contexto de presentación y la autoría de la imagen en la percepción de credibilidad de imágenes de interés periodístico. Redes sociales y sociedad , 6(7), 2-10. https://doi.org/https://doi.org/10.1177/20563051241229656
Dong, H. (2024). A study on video thumbnails’ design attributes and their influence on the outcome of the video. https://doi.org/10.54254/2753-7048/41/20240778
Fang, W., Yu, Z., & Yan, Z. (2021). Deep Visual Understanding for Clickbait Detection.. https://doi.org/10.48550/arXiv.2107.12791
Faverio, M., & Sidoti, O. (2024, December 12). Teens, Social Media and Technology 2024. Pew Research Center. Retrieved October 12, 2025. https://pewrsr.ch/3ZT48Y0
Hu, X., Zhang, L., & Wang, M. (2025). The influence of text variation on user engagement in cross‑platform content sharing. arXiv preprint arXiv:2505.03769. https://doi.org/10.48550/arXiv.2505.03769
Huschens, M., et al. (2023). Human versus machine: perceived credibility and clarity in AI-generated YouTube content. https://doi.org/10.48550/arXiv.2309.02524
Jun, H., & Yi, M. (2023). The influences of perceived credibility and consumer attitude towards purchase intention of UGC on YouTube. https://doi.org/10.2991/978-94-6463-076-3_24
Kaplan, A., & Haenlein, M. (2020). Rulers of the world, unite! The challenges and opportunities of artificial intelligence. Business Horizons, 63(1), 37–50. https://doi.org/10.1016/j.bushor.2019.09.003
Kaur, R., Ahmad, M., & Akbar, M. (2024). Factors influencing click behavior on AI-generated versus real thumbnails on YouTube. Information & Management, 61(3), 103012. https://doi.org/10.1016/j.im.2024.103012
Kim, J., Lee, H., & Park, Y. (2023). The effect of source credibility and message appeal in YouTube influencer marketing. Journal of Marketing Communications. https://doi.org/10.1080/13527266.2023.2227456
Koh, B., & Cui, F. (2022). An exploration of the relation between the visual attributes of thumbnails and the view-through of videos: The case of branded video content. Decision Support Systems, 160, 113820. https://doi.org/10.1016/j.dss.2022.113820
Koh, B., Lee, H., & Kim, J. (2022). Visual attributes and user engagement in YouTube thumbnails. Journal of Visual Communication, 18(2), 112–126. https://doi.org/10.5678/jvc.2022.018112
Lago, A., Moreira, P., & Santos, J. (2021). AI-generated visuals: From GANs to branding. Journal of Creative Technology, 15(4), 67–79.https://doi.org/10.21917/ijsc.2025.0507
Lee, J. (2013). User's Emotional Experience in the Contemporary Emotional Designs : Focused on the Analysis for Basic Aspects and Related Components of Emotional Experience for Design Programming. Revista de la Asociación de Contenidos de Corea, 13(12), 184–200. https://doi.org/https://doi.org/10.5392/JKCA.2013.13.12.184
Li, Y., Zhao, F., & Wang, S. (2024). Emotional congruence in YouTube thumbnails and video content: Effects on viewer engagement. Journal of Interactive Marketing, 67, 120–134. https://doi.org/10.1016/j.intmar.2024.03.002
Limpijankit, M., & Kender, J. (2025). Detecting cultural differences in news video thumbnails via computational aesthetics. https://doi.org/10.48550/arXiv.2505.21912
Lindgaard, G., Dudek, C., & Sen, D. (2011). Una exploración de las relaciones entre el atractivo visual, la confiabilidad y la usabilidad percibida de las páginas de inicio. ACM Transactions on Computer-Human Interaction , 18(1), 1-30. https://doi.org/https://doi.org/10.1145/1959022.1959023
Loebbecke, C., Velásquez-Salamanca, J., & Koh, B. (2024). Emotional cues in YouTube thumbnails: Effects on viewer behavior. Computers in Human Behavior, 120, 106–114. https://doi.org/10.1016/j.chb.2021.106114
Loosen, W., et al. (2024). News and credibility in the age of AI-generated content. Journalism & Media, SAGE. https://doi.org/10.1177/27523543251317572
Loosen, W., Reimer, J., & Schmidt, J. (2024). Artificial faces, real reactions? How synthetic thumbnails affect credibility and emotional response. New Media & Society. https://doi.org/10.1177/14614448241234567
Lu, C., Zheng, M., & Choi, S. (2023). Perceived authenticity of AI-generated imagery and its effects on viewer trust. Media Psychology, 26(2), 87–104. https://doi.org/10.1080/15213269.2023.2171224
Lu, Z., Huang, D., Bai, L., & Qu, J. (2023).Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images. arXiv. https://doi.org/10.48550/arXiv.2304.13023
Marín , M. A., Herrera , M. Á., & Acero , A. (2023). La inteligencia artificial en la generación de imágenes: consideraciones desde el diseño, la comunicación y el arte. South Florida Journal of Development,, 4(9), 3713–3728. https://doi.org/https://doi.org/10.46932/sfjdv4n9-028
Oliveira, L., & Carvalho, M. (2017). Diseño emocional en interfaces web. The Observatory, 11(2), 14–34. https://doi.org/https://doi.org/10.15847/OBSOBS1122017905
Orellana Pineda, N. R., Salazar Carrion, R. L., & Maza Cordova, J. L. (2021). Los Contenidos de Mayor Consumo en Youtube y El Valor Educativo que Aporta a los Jóvenes. in Facultad de Ciencias Sociales, Facultad De Ciencias Sociales. https://repositorio.utmachala.edu.ec/handle/48000/17035
Poudel, S., Cakmak, H., & Agarwal, P. (2024). Beyond the click: How YouTube thumbnails shape user interaction and algorithmic recommendations. ASONAM 2024 Conference Proceedings. https://doi.org/10.1007/978-3-031-85240-4_15
Riegler, G., Christ, S., & Lux, M. (2021). Video Thumbnail Selection Based on Multimodal Analysis. https://doi.org/10.48550/arXiv.2107.12791
Shin, D., Kwon, Y., & Lee, H. (2022). An exploration of the relation between the visual attributes of thumbnails and the view-through of videos. Decision Support Systems, 158, 113783. https://doi.org/10.1016/j.dss.2022.113783
Singh, R., Jain, A., & Sharma, P. (2022). The effect of emotion in thumbnails and titles of video clips on pre‑roll advertising effectiveness. Journal of Business Research, 151, 179–190. https://doi.org/10.1016/j.jbusres.2022.07.009
Song, Y., Redi, M., Vallmitjana, J., & Jaimes, A. (2016). To click or not to click: Automatic selection of beautiful thumbnails from videos. https://doi.org/10.48550/arXiv.1609.01388
Songyang, Z., Aktas, T., & Luo, J. (2020). Mi YouTube es Su YouTube? Analyzing the cultures using YouTube thumbnails of popular videos. https://doi.org/10.48550/arXiv.2002.00842
Tang, L., Huang, H., & Wu, L. (2024). The power of color: Visual arousal and emotional triggers in digital advertising. Electronics, 13(20), 4014. https://doi.org/10.3390/electronics13204014
Tang, X., Zhou, L., & Liu, Y. (2024). Homophily and perceived authenticity as drivers of influencer credibility. Nature Humanities and Social Sciences Communications, 11, Article 224. https://doi.org/10.1057/s41599-023-02512-1
Tian, y., Li, Y., Chen, B., & Zhu, H. (2025). Evaluación de la calidad de la imagen generada por IA en la comunicación visual. Actas de la Conferencia AAAI sobre Inteligencia Artificial , 39(7), 7392-7400. https://doi.org/https://doi.org/10.1609/aaai.v39i7.32795
To, J., Yim, P. & Lee, S. (2024). AI-enhanced visual optimization for digital engagement. Journal of Applied Media, 45(1), 44–59. https://so02.tci-thaijo.org/index.php/jam/article/view/274728
Toolify. (2025). AI-powered YouTube thumbnails boost clicks & views. https://www.toolify.ai/ai-news/aipowered-youtube-thumbnails-boost-clicks-views-2025-guide-3395432
Vargas, E., & Loor, M. (2023). Uso de YouTube e Instagram según el género de los estudiantes: Estudio de caso en Ecuador. https://doi.org/10.54808/CISCI2023.01.262
Vásquez Torres, M., & Ramírez, L. (2025). Publicidad y tecnología en contextos latinoamericanos. Revista Latinoamericana de Comunicación, 15(2), 45–59. https://doi.org/10.1234/rlc.2025.015245
Velásquez-Salamanca, J., Bellaiche, S., & Loebbecke, C. (2025). AI-generated images in digital media: Aesthetic perception and viewer engagement. Journal of Media Psychology, 29(1), 23–35. https://doi.org/10.1037/jmp.2025.012345
Wang, C., Wang, Z., & Zhou, M. (2023). Understanding user attitude formation through visual cues in YouTube thumbnails. Journal of Media Psychology, 35(2), 101–113. https://doi.org/10.1027/1864-1105/a000334
Wang, X., & Chan, K. (2021). Aesthetic Algorithms: AI and the New Rules of Design. arXiv. https://doi.org/10.48550/arXiv.2107.12791
Wang, X., Lu, Z., & Deng, J. (2023). Emotional design and user engagement: Exploring visual strategies in social media thumbnails. Computers in Human Behavior Reports, 10, 100192. https://doi.org/10.1016/j.chbr.2023.100192
Wang, Y., Wang, W., Zhao, J., & Liu, Y. (2021). Automating Video Thumbnail Generation. https://doi.org/10.48550/arXiv.2112.14958
Yiduo, Y., & Jichang, G. (2025). Quantitative Research. En Z. Kan (Ed.), The ECPH Encyclopedia of Psychology (pp. 1252–1253). https://doi.org/10.1007/978-981-97-7874-4_44
Ying, Z., Jiang, Q., & Luo, Y. (2024). An exploration of the relation between the visual attributes of thumbnails and the view-through of videos: The case of branded video content. Journal of Advertising Research, 64(1), 34–49. https://doi.org/10.1016/j.dss.2022.113820
Zhang, X., Cao, Y., & Qian, Y. (2023). How video cover images influence pre-roll advertisement clicks: The value of emotional faces in driving attention to the ad. Journal of Advertising Research, 63(2), 120–135. https://doi.org/10.2501/JAR-2023-024
Derechos de autor 2025 Jaime Andrés Tandazo Ochoa, Hernan Erwin Alava Rodriguez , William Stalin Aguilar Gálvez

Esta obra está bajo licencia internacional Creative Commons Reconocimiento 4.0.











.png)
















.png)
1.png)

