Implementing an AI Teaching Assistant for a Class in Motion Picture Engineering

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Publisher: SMPTE — Pasadena, CA
Doc Type: Conference Paper
Content Type: Original Research
Volume: 00, pp. 1–23
Abstract: Since 2015, Trinity College Dublin has delivered a Master's-level “Motion Picture Engineering” (MPE) course uniquely combining core principles of video compositing and streaming with cutting-edge developments in the field. In 2025, to address diverse student backgrounds - from deep learning practitioners to image processing newcomers - we developed a domain-specific AI Teaching Assistant (AI-TA) using Retrieval Augmented Generation (RAG). While RAG is a well-established paradigm, this paper addresses practical implementation challenges and classroom integration constraints in real-world educational deployment. Our technical overview includes details about architecture, security, compliance, document embedding, vector store tuning, prompt engineering and UI integration. The AI-TA was deployed to 43 MPE students over 7 weeks during Spring 2025. Anonymous telemetry provided insight into students' engagement patterns. We further leveraged AI for post-deployment exploration of student queries with the goal of identifying commonly misunderstood topics and knowledge gaps, enabling data-informed curriculum refinement. Assessment outcomes showed no significant exam performance differences across varying AI-TA usage levels, indicating that with thoughtfully designed open-book assessments, AI tools may enhance learning without compromising academic integrity. Our case study, however, reveals “muddy waters” in practical AI-TA implementation, from questioning the benefits of student anonymity to management of an operationally intensive lecturer's workflow. Student feedback revealed that they found the AI-TA useful for clarifying and exploring MPE topics but remained cautious about its use as a substitute for human tutoring and skeptical of its suitability in open-book exams. This work provides practical guidance for AI-TA deployment, addressing real-world implementation challenges applicable in MPE and broader technical education contexts. Our code is available on GitHub 1 .
Publication Date: 2025-10-13
ISBN: Electronic: 978-1-61482-966-9
Author(s): Deirdre O'Regan
Anil C. Kokaram
Keyword(s): Motion Picture Engineering, Education, Artificial Intelligence, Retrieval Augmented Generation, RAG, Production
Copyright: © 2025 SMPTE

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Deirdre O'Regan and Anil C. Kokaram; Implementing an AI Teaching Assistant for a Class in Motion Picture Engineering, MTS 2025, Article 28 (pp. 1 to 23); SMPTE, 2025, ISBN: 978-1-61482-966-9

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Deirdre O&#x27;Regan and Anil C. Kokaram; Implementing an AI Teaching Assistant for a Class in Motion Picture Engineering, MTS 2025, Article 28 (pp. 1 to 23); SMPTE, 2025, ISBN: 978-1-61482-966-9

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Deirdre O'Regan and Anil C. Kokaram; Implementing an AI Teaching Assistant for a Class in Motion Picture Engineering, MTS 2025, Article 28 (pp. 1 to 23); SMPTE, 2025, ISBN: 978-1-61482-966-9

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<span class="citation">Deirdre O&#x27;Regan and Anil C. Kokaram; <cite>Implementing an AI Teaching Assistant for a Class in Motion Picture Engineering</cite>, MTS 2025, Article 28 (pp. 1 to 23); SMPTE, 2025, ISBN: 978-1-61482-966-9</span>

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Deirdre O'Regan and Anil C. Kokaram; Implementing an AI Teaching Assistant for a Class in Motion Picture Engineering, MTS 2025, Article 28 (pp. 1 to 23); SMPTE, 2025, ISBN: 978-1-61482-966-9

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