Evidence map›Paper›PMID 40436985›Full record

ArticleScientific reports2025

The analysis of generative artificial intelligence technology for innovative thinking and strategies in animation teaching.

Xu Yao, Yaozhang Zhong, Weiran Cao

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

3 authors.

Xu YaoSchool of Animation and Digital Arts, Communication University of Zhejiang, Hangzhou, 310000, China.
Yaozhang ZhongJiangxi College of Applied Technology, Ganzhou, 341000, China. zhongyaozhang@jxyy.edu.cn.
Weiran CaoSchool of Art and Archaeology, Hangzhou City University, Hangzhou, 310015, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This work examines the application of Generative Artificial Intelligence (GAI) technology in animation teaching, focusing on its role in enhancing teaching quality and learning efficiency through innovative instructional strategies. Compared to traditional animation teaching methods, GAI technology introduces a novel pedagogical paradigm characterized by adaptive personalized learning pathways, intelligent teaching resource optimization, and immersive interactive learning models. A mixed-methods research approach is adopted, integrating quantitative analysis (experimental data and questionnaire surveys) and qualitative analysis (behavioral observations) to systematically assess the educational effectiveness of GAI technology. The experiment, conducted over 12 weeks, involved 120 students divided into an experimental group and a control group. Data sources included pre- and post-test evaluations, learning feedback surveys, and classroom behavior analysis. The results indicate that, compared to conventional teaching methods, GAI technology significantly enhances learning outcomes, knowledge application abilities, learning motivation, and student satisfaction. The adaptive personalized learning pathway dynamically adjusts content based on students' progress, improving their mastery of foundational knowledge and skill transferability. Intelligent teaching resources automatically generate high-quality animation examples and provide dynamic feedback mechanisms, fostering creative expression and practical efficiency. The immersive interactive learning model effectively increases classroom engagement, teamwork skills, and problem-solving abilities. These findings demonstrate that GAI technology has the potential to transform animation teaching by optimizing the learning experience and advancing intelligent teaching methodologies. Beyond offering personalized learning solutions, GAI technology plays a crucial role in cultivating students' creativity, critical thinking, and autonomous learning abilities. This work provides theoretical support and practical guidance for the digital transformation of animation teaching while underscoring the broader applicability of GAI technology in the education sector, offering new directions for the future development of intelligent education.

Indexed as

Animation teachingGenerative artificial intelligenceInnovative thinkingInstructional strategies

Identifiers

PMID40436985
PMCPMC12119805

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.