AI-Assisted Audiovisual Production: A Scoping Review of Workflow Efficiency, Quality, and Cognitive Load
DOI:
https://doi.org/10.64747/w9kh2z31Keywords:
audiovisual production, generative artificial intelligence, creative workflow, post-production, cognitive workload, scoping reviewAbstract
Introduction: Artificial intelligence is rapidly entering ideation, pre-production, animation, editing, and post-production. Claims of faster work, however, are often treated as evidence of simultaneous improvements in quality, cognitive workload, and creative control. Objective: To map empirical evidence published through 31 August 2025 on AI assistance in audiovisual production regarding workflow efficiency, quality, cognitive workload, and creative control. Method: A JBI-informed scoping review was reported according to PRISMA-ScR. Four Crossref queries retrieved 80 records, of which 75 remained after deduplication. Thirty-one reports identified through screening and targeted searching were assessed, and 14 studies were included: ten direct audiovisual studies and four adjacent creative studies. A descriptive thematic synthesis was conducted without meta-analysis. Results: Direct evidence indicates that AI is most useful in standardized tasks, exploration, and post-production, but the association between AI intensity and efficiency is not linear. Across ten animation projects, post-production achieved a mean efficiency of 0.91275, whereas the correlation between AI use and overall efficiency was weak (r = 0.23). In filmmaking education, nine of ten students spent 30–120 minutes on an AI-assisted stage and all believed they would have finished sooner without AI. In a perceptual experiment with 62 participants, manually authored animation received higher appeal ratings than AI motion-capture animation. No audiovisual study used a standardized cognitive workload measure. In adjacent evidence, an experiment with 58 design students found higher mental demand, effort, and frustration under AI assistance. Conclusion: AI is not a universal accelerator. Benefits depend on production stage, technological maturity, integration quality, and the role retained by humans. Comparative measurement of cognitive workload and creative control is the central research gap.
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