Evidence map›Paper›PMID 42042245›Full record

ArticleJournal of Intelligence2026

Generative AI as an External Cognitive Tool for Developing Creative Intelligence in Visual Design: A Mixed-Methods Randomized Study Using Cognitive Load Indicators and Motivational Modeling.

Ziyang Huang, Jiajia Zhao, Xuan Fu

Abstract read
In one paragraph

Article in Journal of Intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Ziyang HuangDepartment of Communication Design, College of Design, Graduate School, Hanyang University, Seoul 04763, Republic of Korea.ORCID 0009-0000-8777-863X
Jiajia ZhaoDepartment of Communication Design, College of Design, Graduate School, Hanyang University, Seoul 04763, Republic of Korea.ORCID 0009-0009-4669-4320
Xuan FuDepartment of Communication Design, College of Design, Graduate School, Hanyang University, Seoul 04763, Republic of Korea.ORCID 0009-0009-1125-2805

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative artificial intelligence (GenAI) is rapidly transforming design education by enabling new forms of human-AI collaborative learning. However, how GenAI relates to cognitive and motivational processes in design learning contexts remains insufficiently understood. This study examines whether integrating GenAI into visual design instruction is associated with improvements in domain-specific creative performance and explores the relationships among cognitive load, learning motivation, and learning outcomes. A six-week randomized instructional experiment was conducted with 120 undergraduate students majoring in visual communication design. Creative performance was evaluated through blind expert ratings, and the relationships among key variables were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that GenAI-integrated instruction is associated with higher levels of learning motivation, engagement, and expert-rated creative performance compared with traditional instruction, whereas cognitive-load indicators show comparatively limited predictive strength within the overall model. In addition, Integrated Teaching Alignment (ITA) significantly moderates the relationship between perceived relevance and learning satisfaction. These findings suggest that GenAI may function as an external cognitive support tool, with learning outcomes appearing to be associated with motivational and instructional factors, while cognitive-load indicators show comparatively limited associations within this instructional context.

Indexed as

cognitive loadcreative intelligencedomain-specific intelligenceexpert-rated performancegenerative AIhuman–AI co-creationlearning motivationPLS-SEMpsychometric modeling

Identifiers

PMID42042245
PMCPMC13117136

What OpenQuestion holds

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LicenceCC BY
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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.