Authors :
Rakesh Kumar; Barkha Samania; Dr. Rajeev Kumar Sharma
Volume/Issue :
Volume 11 - 2026, Issue 4 - April
Google Scholar :
https://tinyurl.com/yxx7x6rr
Scribd :
https://tinyurl.com/3pv3939u
DOI :
https://doi.org/10.38124/ijisrt/26apr300
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Artificial Intelligence (AI) has become a game changer in the digital media sector, changing how content is created,
enhanced, and distributed. Modern digital media platforms increasingly rely on AI-driven techniques to automate creative
workflows, improve content quality, and personalize user experiences. This paper investigates the impact of AI on digital
media content creation, focusing on AI-assisted text, image, video, and audio generation. A qualitative research approach
based on existing literature and secondary data analysis is employed to examine the advantages, challenges, and ethical
implications of AI-generated media. The study highlights how AI enhances productivity, reduces production costs, and
democratizes creativity, while also raising concerns related to authenticity, intellectual property, and algorithmic bias. The
findings emphasize the need for responsible AI adoption to ensure transparency, trust, and ethical compliance in digital
media ecosystems.
Keywords :
Artificial Intelligence, Digital Media Platforms, Content Creation Systems, Machine Learning Models, Ethical Implications.
References :
- T. B. Brown et al., “Language Models are Few-Shot Learners,” arXiv preprint arXiv:2005.14165, 2020.
- A. Vaswani et al., “Attention Is All You Need,” in Proceedings of the 31st Conference on Neural Information Processing Systems (NeurIPS), 2017, pp. 5998–6008.
- J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proceedings of NAACL-HLT, 2019, pp. 4171–4186.
- I. Goodfellow et al., “Generative Adversarial Nets,” in Proceedings of NeurIPS, 2014, pp. 2672–2680.
- T. Karras, S. Laine, and T. Aila, “A Style-Based Generator Architecture for Generative Adversarial Networks,” in Proceedings of CVPR, 2019, pp. 4401–4410.
- A. Ramesh et al., “Zero-Shot Text-to-Image Generation,” arXiv preprint arXiv:2102.12092, 2021.
- A. Radford et al., “Learning Transferable Visual Models from Natural Language Supervision,” in Proceedings of ICCV, 2021.
- N. Diakopoulos, “Algorithmic Accountability in Media,” Journal of Media Ethics, vol. 34, no. 2, pp. 1–15, 2019.
- C. O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York, NY, USA: Crown, 2016.
- N. Hassan, A. Dar, A. Qadir, M. Imran, and R. Nawaz, “Fake News Detection: A Deep Learning Approach,” Information Processing & Management, vol. 57, no. 2, 2020.
- K. Yu, W. Hu, J. Zhang, C. Zhang, and Y. Sun, “AI–Human Synergy System: A Survey,” ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 16, no. 4, 2020.
- M. D. Ekstrand et al., “Challenging Misinformation: Exploring Agents and Roles in an AI-Supported News Game,” in Proceedings of HT’20, 2020, pp. 205–214.
- S. Sedhain, A. K. Menon, S. Sanner, and L. Xie, “AutoRec: Autoencoders Meet Collaborative Filtering,” in Proceedings of WWW, 2015, pp. 111–112.
- J. Tang et al., “Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification,” in Proceedings of CIKM, 2015, pp. 1533–1542.
- K. Klöckner, S. Nemec-Begluk, and S. Heidenreich, “AI Storytelling and Algorithms in Creative Industries,” Technological Forecasting and Social Change, vol. 168, 2021.
- Q. Wang, J. Zhang, J. Wang, and L. Tian, “A Survey on Emotion Recognition Using Physiological Signals,” Mobile Networks and Applications, vol. 25, no. 4, pp. 1433–1449, 2020.
- W. Li and J. Huang, “Generating Personalized Recommendations Using Reinforcement Learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 8, pp. 1440–1454, 2019.
- A. Hussain, “The Impact of Artificial Intelligence on Digital Media Content Creation,” International Journal of Innovative Science and Research Technology (IJISRT), vol. 9, no. 7, July 2024.
Artificial Intelligence (AI) has become a game changer in the digital media sector, changing how content is created,
enhanced, and distributed. Modern digital media platforms increasingly rely on AI-driven techniques to automate creative
workflows, improve content quality, and personalize user experiences. This paper investigates the impact of AI on digital
media content creation, focusing on AI-assisted text, image, video, and audio generation. A qualitative research approach
based on existing literature and secondary data analysis is employed to examine the advantages, challenges, and ethical
implications of AI-generated media. The study highlights how AI enhances productivity, reduces production costs, and
democratizes creativity, while also raising concerns related to authenticity, intellectual property, and algorithmic bias. The
findings emphasize the need for responsible AI adoption to ensure transparency, trust, and ethical compliance in digital
media ecosystems.
Keywords :
Artificial Intelligence, Digital Media Platforms, Content Creation Systems, Machine Learning Models, Ethical Implications.