Evidence map›Paper›PMID 42707338›Full record

ArticleFrontiers in psychology2026

A collaborative creation framework for experimental animation pedagogy integrating generative artificial intelligence and vibe coding.

Yadan Mao

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Yadan MaoDepartment of Digital Media Art, School of Art, Soochow University, Suzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The growing use of generative artificial intelligence (GAI) in art and design has accelerated image and video automation. In this landscape, preserving media experimentation, authorial judgment, and generative unpredictability has emerged as a critical focus in experimental animation education. While existing studies mainly focus on production efficiency, limited attention has been paid to how GAI supports concept development, motion translation, media organization, and creative decision-making in classroom practice. Addressing this gap through design-based research (DBR), this study introduced GAI and Vibe Coding into a five-week undergraduate experimental animation course. It further proposed a human-AI co-creation framework based on "generation-feedback-evolution" and examined students' creative processes and cognitive changes through process logs, creative portfolios, reflective texts, and three-stage questionnaires. The findings show that GAI extends far beyond mere production efficiency and actively intervenes in the decision-making process, prompting students to critically evaluate and revise their creative intentions, motion logic, and media expression. As human-AI interaction deepened, students moved from tool-based utility towards active selection, recombination, and transformation of AI-generated outcomes, reflecting a heightened awareness of their own creative agency. Questionnaire results indicate that, by the end of the course, students' perception of AI as a collaborative partner and their germane load had both increased, extraneous load had decreased, and intrinsic load remained relatively stable. Qualitative analysis reveals that students began to engage in critical negotiations with AI regarding creative intentions, motion rules, media specificity, and authorial position. Preliminary findings suggest that the proposed framework can effectively support human-AI collaboration in experimental animation pedagogy, offering empirical, classroom-based evidence for understanding the shifting dynamics of authorship and media relations under AI intervention.

Indexed as

animation pedagogycognitive loadexperimental animationgenerative artificial intelligencehuman-AI collaborationvibe coding

Identifiers

PMID42707338
PMCPMC13547086

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.