An AI Teaching Assistant for Motion Picture Engineering

Abstract:The rapid rise of LLMs over the last few years has promoted growing experimentation with LLM-driven AI tutors. However, the details of implementation, as well as the benefit in a teaching environment, are still in the early days of exploration. This article addresses these issues in the context of implementation of an AI Teaching Assistant (AI-TA) using Retrieval Augmented Generation (RAG) for Trinity College Dublin's Master's Motion Picture Engineering (MPE) course. We provide details of our implementation (including the prompt to the LLM, and code), and highlight how we designed and tuned our RAG pipeline to meet course needs. We describe our survey instrument and report on the impact of the AI-TA through a number of quantitative metrics. The scale of our experiment (43 students, 296 sessions, 1,889 queries over 7 weeks) was sufficient to have confidence in our findings. Unlike previous studies, we experimented with allowing the use of the AI-TA in open-book examinations. Statistical analysis across three exams showed no performance differences regardless of AI-TA access (p > 0.05), demonstrating that thoughtfully designed assessments can maintain academic validity. Student feedback revealed that the AI-TA was beneficial (mean = 4.22/5), while students had mixed feelings about preferring it over human tutoring (mean = 2.78/5).

Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Signal Processing (eess.SP)

K.3.1; K.3.2; I.2.7

arXiv:2604.04670 [eess.IV]

(or arXiv:2604.04670v2 [eess.IV] for this version)

https://doi.org/10.48550/arXiv.2604.04670 Focus to learn more arXiv-issued DOI via DataCite

IEEE Signal Processing Magazine, vol. 43, no. 4, pp. 76-84, July 2026

https://doi.org/10.1109/MSP.2026.3665423 Focus to learn more DOI(s) linking to related resources

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