Who Owns the Pitch? AI, Ethics, and Co-Creation in Television Student Practice
Sengstaken, Katarina (2026) Who Owns the Pitch? AI, Ethics, and Co-Creation in Television Student Practice. In: Artificial Intelligence (AI) in Screen Education Conference, March 31st - 1st April 2026, Flux Innovation Lounge, London. (Unpublished)
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This paper outlines a forthcoming case study on the integration of generative AI into a 15-credit undergraduate module on factual entertainment television production, running in January 2026. In this module, first-year students will study genre conventions, concept development, and the pitching process before presenting their original ideas to industry professionals. As part of their assessment, students will be invited to use AI tools to generate supporting materials such as logos, visual mock-ups, theme music, and sample footage for a sizzle reel to strengthen their pitches.
The study adopts a pedagogical lens, examining how students engage with AI as part of their learning process, while addressing the conference theme “Ethics, Authorship, and Automation: Who owns the work?”. Students are expected to encounter “troublesome knowledge” (Meyer & Land, 2005) as they grapple with originality, authorship, and creative agency. AI offers inclusive benefits by helping students without strong design skills clarify ideas and produce polished work, yet it also risks over-reliance, reducing opportunities to develop independent strategies and resilience. At the same time, students must confront ethical and legal uncertainties, including potential copyright overlap (UKIPO, 2021; Gervais, 2023; Zhong et al., 2023), accountability in authorship (Yusuf, 2024), and bias or homogenisation in outputs (Sun et al., 2023).
While pedagogy is central, the study recognises that ethical and legal considerations are integral to preparing students for professional practice. By embedding AI into a credit-bearing module, this case study aims to generate timely insights into how creative, ethical, and legal tensions can be leveraged pedagogically to equip students for a screen industry increasingly shaped by automation. In line with recent calls to frame AI as a collaborator rather than a shortcut (DeSoto, 2024; Henriksen, Mishra and Stern, 2024), preliminary data from the January module will be presented at the conference, offering fresh insights into how students adapt AI outputs, assert originality, and critically reflect on authorship.
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