Journalists and Machines: Applying the Technology Adaptation Model to Understand AI Use in TV Journalism

Asmarantika, Rossalyn Ayu (2024) Journalists and Machines: Applying the Technology Adaptation Model to Understand AI Use in TV Journalism. In: MediAsia2024 Conference Proceedings, Jepang.

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Abstract

This study explores the incorporation and interpretation of artificial intelligence (AI) technology in journalism through in-depth interviews with six journalists from three significant Indonesian news television channels: CNN Indonesia, Kompas TV, and TV One. This research seeks to gain insight into the manner in which journalists adjust to and perceive the integration of artificial intelligence (AI) into their workflow, using the Technology Adaptation Model (TAM). The results indicate that AI is primarily employed for administrative and basic functions, such as transcribing and initial research, which leads to faster completion times and allows journalists to concentrate on more crucial areas of newsgathering and reporting. Nevertheless, the journalists hold a contradictory viewpoint on AI, seeing it as a "paradoxical tool." Although AI improves productivity in repetitive jobs, it is not as effective in tasks that demand complex human judgement, such as comprehensive reporting and contextual interpretation. Considerable concerns around ethics and job security have arisen, indicating a broader disapproval about the potential displacement of traditional journalistic professions by AI. This study emphasizes the significance of maintaining a balanced approach when incorporating AI into media.

Item Type:	Conference or Workshop Item (Paper)
Creators:	Asmarantika, Rossalyn Ayu
Contributors:
Keywords:	AI in Journalism, Technology Acceptance Model, TV Journalism, Indonesian TV Journalist, Qualitative Research
Subjects:	000 Computer Science, Information and General Works > 070 News Media, Journalism and Publishing > 070 News, mass media, journalism, and publishing
Divisions:	Faculty of Communication > Digital Journalism
Date Deposited:	12 Mar 2026 04:14
URI:	https://kc.umn.ac.id/id/eprint/45339

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