AI & Animation Education

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Research

Peer-reviewed and pre-print work that informs AI use in animation production and education, grouped by research type. Filter or search the full research catalogue below.

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Research

What UK university AI policies actually do: a study of 96 institutions (HEPI Policy Note 71)

Higher Education Policy Institute (HEPI) · Illingworth, S.

  • Analysis of UK university AI policies finds 41 per cent of 163 degree-awarding institutions have no easily findable public AI policy.
  • Four exemplar policies are named, including creative-arts specialist Arts University Plymouth.
  • A policy's hosting location predicts its function: misconduct-framework policies enforce compliance, learning-and-teaching policies educate.

Teaching, curriculum & assessment Governance & guidance

Research

Artificial Intelligence in Fine Arts Education: A Systematic Literature Review

SAGE Open · Yance Zeng, Harrinni Md Noor, Muhammad Faiz Sabri

  • PRISMA systematic review of 78 peer-reviewed studies on artificial intelligence in fine arts education, covering 2019 to 2024.
  • Categorises the AI techniques most used in the field, spanning generative adversarial networks, convolutional neural networks, large language models and VR/AR.
  • Finds AI adoption concentrated in visual arts education, with gaps in non-visual arts domains and in primary and secondary teaching.

Teaching, curriculum & assessment

Research

Art, Design and AI Educator's Toolkit: values-led generative AI in design education

Association for Learning Technology (ALT) blog

  • Openly licensed toolkit for values-led generative AI teaching in art and design higher education, funded by QAA and developed across three UK institutions.
  • Named contributors include Ruth Powell (UAL) and Lucy-Ann Pickering (NTU), with Norwich University of the Arts as a partner.
  • Structured around four design-cycle stages: research, ideation, experimentation and iteration, and design communication, with classroom-ready activities, case studies and values-based prompt cards.

Teaching, curriculum & assessment

Research

HEPI Student Generative AI Survey 2026

Higher Education Policy Institute (HEPI) · Stephenson, R., Armstrong, C.

  • HEPI's 2026 survey finds 95 per cent of UK undergraduates use AI, and 94 per cent use generative AI for assessed work.
  • Arts and humanities students are particularly likely to feel under-supported in developing AI skills.
  • Students directly including AI-generated text in assessed work rose to 12 per cent, from 8 per cent in 2025 and 3 per cent in 2024.

Teaching, curriculum & assessment Governance & guidance

Research

Path of practical teaching of AI technology in animation majors

Discover Artificial Intelligence (Springer Nature) · Zhang, J., Guan, X.

  • Empirical study constructing an animation practice teaching path that integrates generative AI with a Transformer-based human pose estimation model.
  • The optimised pose estimation model reduced error to 0.15 and lifted animation generation quality scores to 92 points.
  • The AI-integrated teaching group significantly outperformed the traditional group on both technical application and artistic creation indicators.

Production & craft Teaching, curriculum & assessment

Research

Integrating generative AI in higher art education: a systematic review

SN Computer Science (Springer Nature) · Fang, Z.

  • PRISMA systematic review of 27 studies on generative AI integration in higher art education across visual arts, design and STEAM.
  • Generative AI consistently supports early-stage creative exploration, enhancing divergent thinking and reducing affective barriers for novice learners.
  • The review identifies a persistent gap: AI accelerates conceptual generation but offers limited support for execution phases requiring embodied practice.

Teaching, curriculum & assessment

Research

OECD Digital Education Outlook 2026: Exploring Effective Uses of Generative AI in Education

Organisation for Economic Co-operation and Development (OECD)

  • The OECD's 2026 Outlook examines generative AI across three scenarios: independent student use, joint classroom use, and teacher-only use.
  • Heavy student reliance on generative AI without pedagogical structure tends to reduce metacognitive engagement, separating task performance from learning.
  • It distinguishes replacement, complementarity and augmentation as teacher-AI modes, arguing augmentation best preserves professional judgement.

Teaching, curriculum & assessment Governance & guidance

Research

Generative AI in higher education assessment: a scoping review

Interactive Learning Environments (Taylor and Francis) · Ng, S. H. S., Chan, H. Y., Wong, J. H. K., Sam, L., Privitera, A. J.

  • Scoping review mapping generative AI in higher education assessment from June 2020 to February 2024 across 68 documents.
  • It identifies generative AI roles spanning assessment design, grading, feedback and academic integrity.
  • The evidence base remains nascent and unevenly distributed, with narrow disciplinary coverage and limited longitudinal data.

Teaching, curriculum & assessment

Research

Generative AI in art education: a systematic review 2019-2025

Education Sciences (MDPI)

  • PRISMA systematic review of 19 peer-reviewed empirical studies on generative AI in art education from January 2019 to August 2025.
  • Research volume accelerated from two studies in 2023 to 14 in 2025, concentrated in higher education and East Asian contexts.
  • Text-to-image models and ChatGPT were the most studied tools, applied across creative production, pedagogical scaffolding and instructional design.

Generative techniques Teaching, curriculum & assessment

Research

AI in the Screen Sector: perspectives and paths forward (BFI / CoSTAR Foresight Lab)

British Film Institute (BFI)

  • First UK-wide study of generative AI's impact across the screen sector, produced with the CoSTAR Foresight Lab (Goldsmiths, Loughborough and Edinburgh universities).
  • Makes nine recommendations across collaborative frameworks, targeted support for small and micro businesses, and removal of barriers to growth.
  • Documents both the scale of AI adoption across the sector and the structural disadvantages facing smaller screen-sector companies in accessing AI capabilities.

Production & craft Labour & workforce

Research

DifFRelight: Diffusion-Based Facial Performance Relighting

Eyeline Studios (Netflix)

  • Netflix Eyeline Studios research enables free-viewpoint facial relighting from flat-lit performance captures.
  • Combines a subject-specific diffusion model with dynamic Gaussian splatting reconstruction.
  • Reproduces eye reflections, subsurface scattering and self-shadowing previously requiring manual VFX work.

Production & craft