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Cited by 73Art Education and DevelopmentDigital Education and SocietyArtificial Intelligence in Education

Co-creating digital art with generative AI in K-9 education: Socio-material insights

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Vartiainen, Henriikka, Tedre, Matti, Jormanainen, Ilkka·2023·International Journal of Education Through Art
DOI 10.1386/eta_00143_1
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Abstract

The rise of image-generating artificial intelligence (AI) tools has triggered changes in digital art and graphic design, provoking debates in the creative industry. However, scant research exists about children’s and youths’ insights into and encounters with generative AI. Building on sociocultural and new materialist perspectives, this exploratory study proposed to address this gap by exploring middle schoolers’ ( N = 10) creative interaction with generative AI, particularly with text-to-image generative models. Qualitative content analyses of emerging learning activities evidenced how generative AI-formed relations were externalized through novel digital artefacts and collaborative discussions. Ideas evolved through peer collaboration organized around creative making with AI. Teachers facilitated relations between people and technology using dialogic teaching, providing room for unpredictability and critical reflection on the impacts of generative AI, especially authorship and copyright. The study concludes with a discussion of the potential uses of generative AI in future art education research and practice.

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Crossref · 10.1386/eta_00143_1

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  • Text-to-image systems are employed to enhance visual originality, learning motivation, and awareness of copyright and ethics.

    “Text-to-image systems are employed to enhance visual originality, learning motivation, and awareness of copyright and ethics [111-114].”

    From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [112]

    The workshop description (indexed as References; page unavailable) says students created visual images with a text-to-image tool and reflected on authorship and copyright. The paper also reports that students enjoyed making images, but it does not establish increased learning motivation or improved visual originality. Its discussion supports engagement with ethical questions, not a demonstrated increase in awareness. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • In visual arts, learners employ Midjourney, DALL·E, and related text-to-image tools, repeatedly adjusting prompts to refine visual detail and aesthetics.

    “In visual arts, learners employ Midjourney, DALL·E, and related text-to-image tools, repeatedly adjusting prompts to refine visual detail and aesthetics [111-116,132,143]”

    From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [112]

    The workshop description says students used Midjourney to make images, and the paper mentions DALL·E as another text-to-image tool. But its critical reflections say students made only slight iterative changes and preferred inventing new prompts; it does not show them using DALL·E or repeatedly refining prompts for visual detail and aesthetics. Page numbers are not supplied in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Teachers play a critical role as facilitators and supporters, working alongside AI to analyze learning data, refine creative or experimental processes, and provide timely guidance for topic selection, content iteration, and project reflection.

    “Teachers play a critical role as facilitators and supporters, working alongside AI to analyze learning data, refine creative or experimental processes, and provide timely guidance for topic selection, content iteration, and project reflection [112,113,116,136].”

    From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [112]

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Studies document limited understanding of model training and datasets, which impairs students' ability to detect errors or deceptive content and leads to weak knowledge/analysis or no significant improvement.

    “Studies document limited understanding of model training and datasets [112,122,135,136], which impairs students' ability to detect errors or deceptive content [105,133] and leads to weak knowledge/analysis [109] or no significant improvement [98].”

    From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [112]

  • AI strengthens the social dimension of practice by facilitating peer collaboration and parent-child dialogue.

    “Finally, AI strengthens the social dimension of practice by facilitating peer collaboration [103,112] and parent-child dialogue [71,89].”

    From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [112]

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Authors
Vartiainen, H., Tedre, M., & Jormanainen, I.
Affiliations
University of Eastern Finland (FI)
ORCID
Venue
International Journal of Education Through Art
Publisher
Intellect
Published
2023-09-01
Volume
19
Issue
3
Pages
405-423
ISSN
  • 1743-5234 (ISSN-L)
  • 2040-090X
DOI
10.1386/eta_00143_1
OpenAlex
W4387190119
Cited by
73
References
24
Citations by year
2026: 23 · 2025: 34 · 2024: 15 · 2023: 1
FWCI
208.10
Type
journal article
Language
en
Open access
Licence
Locations
International Journal of Education through Art (publishedVersion, cc-by)
Funders
Strategic Research Council (352859, 352876)
Topics
  • Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art Education and Development
  • Physical Sciences › Computer Science › Information Systems › Digital Education and Society
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Keywords
generative AI, text-to-image models, digital art, art education, junior high school students, co-creation, new materialism, sociocultural perspective, peer collaboration, authorship, copyright, dialogic teaching

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