An AI Teaching Assistant for Motion Picture Engineering

• URL: http://arxiv.org/abs/2604.04670v1
• Date: Mon, 06 Apr 2026 13:30:11 GMT
• Title: An AI Teaching Assistant for Motion Picture Engineering
• Abstract summary: This article describes implementation of an AI Teaching Assistant (AI-TA) at Trinity College Dublin's Master's Motion Picture Engineering (MPE) course.<n>We describe our survey instrument and report on the impact of the AI-TA through a number of quantitative metrics.<n>Student feedback revealed that the AI-TA was beneficial, while students had mixed feelings about preferring it over human tutoring.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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).

Related papers

• AI-Assisted Peer Review at Scale: The AAAI-26 AI Review Pilot [45.90272517740283]
  We report the first large-scale field deployment of AI-assisted peer review.<n>Every main-track submission at AAAI-26 received one clearly identified AI review from a state-of-the-art system.
  arXiv  Detail & Related papers  (2026-04-15T14:51:07Z)
• AI Meets Mathematics Education: A Case Study on Supporting an Instructor in a Large Mathematics Class with Context-Aware AI [18.81288652134873]
  Large-enrollment university courses face persistent challenges in providing timely and scalable instructional support.<n>We present a human-centered case study of AI-assisted support in a Calculus I course, implemented in close collaboration with the course instructor.<n>We developed a system to answer students' questions on a discussion forum, fine-tuning a lightweight language model on 2,588 historical student-instructor interactions.
  arXiv  Detail & Related papers  (2026-03-09T10:59:52Z)
• Let the Barbarians In: How AI Can Accelerate Systems Performance Research [80.43506848683633]
  We term this iterative cycle of generation, evaluation, and refinement AI-Driven Research for Systems.<n>We demonstrate that ADRS-generated solutions can match or even outperform human state-of-the-art designs.
  arXiv  Detail & Related papers  (2025-12-16T18:51:23Z)
• Reflection-Satisfaction Tradeoff: Investigating Impact of Reflection on Student Engagement with AI-Generated Programming Hints [16.757426904379212]
  One promising approach involves pairing AI-generated hints with reflection prompts.<n>This study investigates the interplay between AI-generated hints and different designs of reflection prompts in an online programming course.
  arXiv  Detail & Related papers  (2025-12-04T10:01:33Z)
• ELAIPBench: A Benchmark for Expert-Level Artificial Intelligence Paper Understanding [49.67493845115009]
  ELAIPBench is a benchmark curated by domain experts to evaluate large language models' comprehension of AI research papers.<n>It spans three difficulty levels and emphasizes non-trivial reasoning rather than shallow retrieval.<n>Experiments show that the best-performing LLM achieves an accuracy of only 39.95%, far below human performance.
  arXiv  Detail & Related papers  (2025-10-12T11:11:20Z)
• AI-University: An LLM-based platform for instructional alignment to scientific classrooms [12.733784667573211]
  We introduce AI University (AI-U), a flexible framework for AI-driven course content delivery.<n>At its core, AI-U fine-tunes a large language model (LLM) with retrieval-augmented generation (RAG) to generate instructor-aligned responses from lecture videos, notes, and textbooks.<n>We present a scalable pipeline to systematically construct training data, fine-tune an open-source LLM with Low-Rank Adaptation (LoRA), and optimize its responses through RAG-based synthesis.
  arXiv  Detail & Related papers  (2025-04-11T01:26:34Z)
• Supporting Students' Reading and Cognition with AI [12.029238454394445]
  We analyzed text from 124 sessions with AI tools to understand users' reading processes and cognitive engagement.<n>We propose design implications for future AI reading-support systems, including structured scaffolds for lower-level cognitive tasks.<n>We advocate for adaptive, human-in-the-loop features that allow students and instructors to tailor their reading experiences with AI.
  arXiv  Detail & Related papers  (2025-04-07T17:51:27Z)
• Use Me Wisely: AI-Driven Assessment for LLM Prompting Skills Development [5.559706293891474]
  Large language model (LLM)-powered chatbots have become popular across various domains, supporting a range of tasks and processes.<n>Yet, prompting is highly task- and domain-dependent, limiting the effectiveness of generic approaches.<n>In this study, we explore whether LLM-based methods can facilitate learning assessments by using ad-hoc guidelines and a minimal number of annotated prompt samples.
  arXiv  Detail & Related papers  (2025-03-04T11:56:33Z)
• ExACT: Teaching AI Agents to Explore with Reflective-MCTS and Exploratory Learning [78.42927884000673]
  ExACT is an approach to combine test-time search and self-learning to build o1-like models for agentic applications.<n>We first introduce Reflective Monte Carlo Tree Search (R-MCTS), a novel test time algorithm designed to enhance AI agents' ability to explore decision space on the fly.<n>Next, we introduce Exploratory Learning, a novel learning strategy to teach agents to search at inference time without relying on any external search algorithms.
  arXiv  Detail & Related papers  (2024-10-02T21:42:35Z)
• A Multi-Year Grey Literature Review on AI-assisted Test Automation [46.97326049485643]
  Test Automation (TA) techniques are crucial for quality assurance in software engineering but face limitations.<n>Given the prevalent usage of AI in industry, sources of truth are held in grey literature as well as the minds of professionals.<n>This study surveys grey literature to explore how AI is adopted in TA, focusing on the problems it solves, its solutions, and the available tools.
  arXiv  Detail & Related papers  (2024-08-12T15:26:36Z)
• Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants [176.39275404745098]
  We evaluate whether two AI assistants, GPT-3.5 and GPT-4, can adequately answer assessment questions.<n>GPT-4 answers an average of 65.8% of questions correctly, and can even produce the correct answer across at least one prompting strategy for 85.1% of questions.<n>Our results call for revising program-level assessment design in higher education in light of advances in generative AI.
  arXiv  Detail & Related papers  (2024-08-07T12:11:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.