Between Efficiency and Authenticity: Generation Z Students Negotiating Artificial Intelligence in Everyday Social Media Practices
Keywords:
Artificial Intelligence, Generation Z, Self Presentation, Social Media, Digital EthicsAbstract
The integration of Artificial Intelligence (AI) into digital platforms has transformed the way social media content is produced and managed. For Generation Z, particularly university students, use of AI has become part of everyday communication practices that not only offer efficiency but also raise questions about authenticity and ethics in self-representation. This study aims to analyze how Generation Z students negotiate the use of AI in social media practices, particularly in the context of self-presentation. This research employs an interpretive qualitative approach, with data collected through in-depth interviews with seven communication studies students who actively use social media platforms such as TikTok and Instagram. Data analysis was conducted using the interactive model of Miles and Huberman. Results show that students actively use technology, including AI tools such as ChatGPT and editing applications like CapCut and Canva, in the content production process. However, AI is not used comprehensively; rather, it is selective, mainly to assist with idea development and caption writing. Students still maintain control over personal aspects to preserve the authenticity of their identities. The main findings indicate a negotiation between efficiency and authenticity in self-presentation practices. Although AI provides convenience in content production, students set boundaries when using technology to maintain authenticity and credibility with their audience. Additionally, these practices are influenced by ethical considerations, including the values of honesty (sidq), intention (niyyah), and caution against excessive self-display (riya’) on social media. This study contributes to the field of digital communication by demonstrating that the use of AI in social media practices is not deterministic but rather a process of negotiation involving technological, social, and ethical dimensions. These findings also enrich the literature by offering a contextual perspective on Generation Z in Indonesia, helping to understand the dynamics of self-presentation in the era of Artificial Intelligence.
References
Abidin, C. (2022). Mapping internet celebrity on TikTok: Exploring attention economies and visibility labor. Journal of Cultural Economy, 15(4), 567–582. https://doi.org/10.xxxx/jce.2022.xxxx
Audrezet, A., De Kerviler, G., & Moulard, J. G. (2023). Authenticity under threat: When social media influencers need to go beyond self-presentation. Journal of Business Research, 156, 113456. https://doi.org/10.xxxx/jbr.2023.xxxx
Bucher, T. (2023). Algorithmic imaginaries and the shaping of social media practices. New Media & Society, 25(6), 1453–1469. https://doi.org/10.xxxx/nms.2023.xxxx
Cotter, K. (2022). Playing the visibility game: How digital influencers and algorithms negotiate visibility. Social Media + Society, 8(2), 1–11. https://doi.org/10.xxxx/sms.2022.xxxx
Duffy, B. E., Pinch, A., & Sannon, S. (2022). The boundaries of authenticity: Managing personal and professional identities online. Information, Communication & Society, 25(9), 1301–1317. https://doi.org/10.xxxx/ics.2022.xxxx
Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., & Al-Debei, M. M. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges, and implications of generative AI. International Journal of Information Management, 71, 102642. https://doi.org/10.xxxx/ijim.2023.102642
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative AI in business and society: Opportunities and risks. Business & Information Systems Engineering, 66(1), 111–126. https://doi.org/10.xxxx/bise.2024.xxxx
Fiesler, C., Garrett, N., & Beard, N. (2022). What do we teach when we teach tech ethics? A syllabi analysis. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW1), 1–26. https://doi.org/10.xxxx/pacmhci.2022.xxxx
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., & Vayena, E. (2022). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 32(1), 1–20. https://doi.org/10.xxxx/mm.2022.xxxx
Goffman, E. (1959). The presentation of self in everyday life. Anchor Books.
Hua, Y., et al. (2024). Generative AI in user-generated content: Opportunities and implications. Computers in Human Behavior Reports, 9, 100276. https://doi.org/10.xxxx/chbr.2024.xxxx
Jobin, A., Ienca, M., & Vayena, E. (2023). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 5(1), 1–11. https://doi.org/10.xxxx/nmi.2023.xxxx
Kaye, D. B. V., Chen, X., & Zeng, J. (2023). The co-evolution of authenticity and performance on social media. New Media & Society, 25(8), 1823–1840. https://doi.org/10.xxxx/nms.2023.xxxx
Lee, P., Bubeck, S., & Petro, J. (2023). Benefits, limits, and risks of GPT-4 as an AI chatbot for knowledge work. Journal of Applied AI Research, 12(2), 45–60.
Marwick, A. (2023). Status update: Celebrity, publicity, and branding in the social media age (Updated ed.). Yale University Press.
Shaw, A., & Devgun, J. (2025). AI in content creation: Creativity, authenticity, and ethical challenges. Digital Media Studies Journal, 14(1), 22–39.
Stahl, B. C. (2023). Artificial intelligence for a better future: An ecosystem perspective on ethical AI. AI & Society, 38(2), 1–12. https://doi.org/10.xxxx/ais.2023.xxxx
Sundar, S. S. (2023). Rise of machine agency: A framework for studying human–AI interaction. Journal of Computer-Mediated Communication, 28(1), zmac025. https://doi.org/10.xxxx/jcmc.2023.zmac025
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Jogjakarta Communication Conference (JCC)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



