{"resourceId":"genai-creativity-meta-analysis","versions":[{"version":"legacy/2026-09-01/genai-creativity-meta-analysis","resource":{"id":"genai-creativity-meta-analysis","title":"Meta-analysis finds a moderate positive effect on creativity in art and design education","organization":"Kütahya Dumlupınar University","sector":"K-12 and higher education","geography":"International","publishedAt":"September 1, 2026","sourceName":"The Impact of Generative Artificial Intelligence on Student Creativity in Art and Design Education: A Meta-Analysis","sourceLabel":"Journal of Curriculum Studies Research article","sourceUrl":"https://curriculumstudies.org/index.php/CS/article/view/1108","evidenceClass":"academic-research","outcomeClass":"effective","topics":["knowledge-work","accessibility-workforce","operating-model"],"finding":"A meta-analysis of 31 independent experimental, quasi-experimental, and correlational studies published from 2020 through 2026 found a moderate, statistically significant association between GenAI use and student creativity in art and design education, with an overall standardized effect of d = 0.61.","sledRelevance":"This is a more specific and defensible educational effectiveness claim than broad assertions that AI improves learning. It identifies a domain and outcome—creative production—where GenAI appears useful while showing that effects vary by learner level, creative dimension, and tool type.","evidence":"The analysis found the largest effect among graduate students, followed by undergraduates and K-12 learners; the strongest creativity dimension was originality or innovation; and text-to-image tools produced larger effects than LLMs, GAN or deep-learning tools, and hybrid tools. The authors report no significant moderating effect from study type or cultural context and no significant publication bias in their analyses.","architectureImplications":"Education platforms should support multimodal creation, artifact provenance, version history, age-appropriate access, and comparison of AI-assisted with independent work. Evaluation should capture the particular creative construct, not use generic engagement or completion metrics as a proxy.","governanceImplications":"Procurement and curriculum decisions should define the target creative outcome, comparison condition, allowed assistance, attribution expectations, accessibility requirements, and how teachers assess student contribution. Preserve non-AI practice where independent skill development is an instructional objective.","securityPrivacyImplications":"Protect student prompts and artwork, clarify intellectual-property and model-training rights, apply age-appropriate content controls, and ensure creative tools do not expose students to unsafe generation or public-by-default sharing.","caveats":"The synthesis is limited to art and design education and combines experimental, quasi-experimental, and correlational evidence. The public abstract does not expose all heterogeneity and study-quality statistics, effects were smaller for K-12 students than graduate students, and a moderate average effect does not predict results for a particular curriculum or tool."}},{"version":"enrichment/2026-09-05T02:42:45.193Z/genai-creativity-meta-analysis","resource":{"id":"genai-creativity-meta-analysis","title":"Meta-analysis finds a moderate positive effect on creativity in art and design education","organization":"Kütahya Dumlupınar University","sector":"K-12 and higher education","geography":"International","publishedAt":"September 1, 2026","publicationDate":"2026-09-01","eventDate":null,"sourceName":"The Impact of Generative Artificial Intelligence on Student Creativity in Art and Design Education: A Meta-Analysis","sourceLabel":"Journal of Curriculum Studies Research article","sourceUrl":"https://curriculumstudies.org/index.php/CS/article/view/1108","evidenceClass":"academic-research","outcomeClass":"effective","topics":["knowledge-work","accessibility-workforce","operating-model"],"finding":"A meta-analysis of 31 independent experimental, quasi-experimental, and correlational studies published from 2020 through 2026 found a moderate, statistically significant association between GenAI use and student creativity in art and design education, with an overall standardized effect of d = 0.61.","sledRelevance":"This is a more specific and defensible educational effectiveness claim than broad assertions that AI improves learning. It identifies a domain and outcome—creative production—where GenAI appears useful while showing that effects vary by learner level, creative dimension, and tool type.","evidence":"The analysis found the largest effect among graduate students, followed by undergraduates and K-12 learners; the strongest creativity dimension was originality or innovation; and text-to-image tools produced larger effects than LLMs, GAN or deep-learning tools, and hybrid tools. The authors report no significant moderating effect from study type or cultural context and no significant publication bias in their analyses.","architectureImplications":"Education platforms should support multimodal creation, artifact provenance, version history, age-appropriate access, and comparison of AI-assisted with independent work. Evaluation should capture the particular creative construct, not use generic engagement or completion metrics as a proxy.","governanceImplications":"Procurement and curriculum decisions should define the target creative outcome, comparison condition, allowed assistance, attribution expectations, accessibility requirements, and how teachers assess student contribution. Preserve non-AI practice where independent skill development is an instructional objective.","securityPrivacyImplications":"Protect student prompts and artwork, clarify intellectual-property and model-training rights, apply age-appropriate content controls, and ensure creative tools do not expose students to unsafe generation or public-by-default sharing.","caveats":"The synthesis is limited to art and design education and combines experimental, quasi-experimental, and correlational evidence. The public abstract does not expose all heterogeneity and study-quality statistics, effects were smaller for K-12 students than graduate students, and a moderate average effect does not predict results for a particular curriculum or tool.","streamIds":["student-success","k12"],"roles":{"sales":"Interpretation — Problem and stakeholders: Arts faculty, curriculum leaders, teachers, students, accessibility specialists, and procurement may want AI-assisted creation without a precise learning objective. Discovery: Is the intended gain originality, another creative dimension, or independent skill, and what student contribution must remain visible? Value hypothesis: A bounded art or design activity could improve a specified outcome under suitable teaching conditions. Potential engagement: Design and evaluate a small curriculum pilot with an explicit comparison and attribution approach. Evidence boundary: The meta-analysis reports an average effect across mixed designs, varying by learner level and tool. It does not predict a product's results, establish broad learning gains, or justify transferring graduate-level effects directly to K12 learners.","engineering":"Interpretation — Fit: Evaluate multimodal creation against a named art or design objective. Architecture: Preserve versions, assistance provenance, student contribution, and non-AI paths within existing course systems. Prerequisites: Creative-outcome rubric, comparison tasks, educator assessment, and age-appropriate access. Constraints: Effects varied by level and tool category; polished outputs can obscure independent capability. Security: Protect prompts and artwork, verify intellectual-property and training permissions, restrict public sharing, and test generation safety. Proposed validation: Compare assisted and independent artifacts using a defined rubric and assess students' explanation of creative decisions, accessibility, and safety. Review subgroup findings and tool changes rather than inheriting the average d = 0.61 as a local target or guaranteed improvement for a particular curriculum.","delivery":"Interpretation — Work and dependencies: Choose population and creative construct, agree assistance and attribution, configure tools, and preserve independent practice. Ownership: Arts educators own pedagogy and assessment; curriculum leadership approves the pilot; IT, accessibility, and privacy review access and artwork handling. Skills and adoption: Teach students to explain contributions and teachers to assess process as well as finished work. Governance checkpoints: Review content safety, rights, and assessment validity before use and after tool changes. Proposed acceptance: Locally scored creative outcomes, documented contribution, usable non-AI alternatives, and acceptable accessibility and safety findings against the comparison. Risks: Mixed correlational and experimental evidence, uneven age effects, and missing public study-quality detail limit transfer to any particular course or product."},"retrievedAt":null,"enrichedAt":"2026-09-05T02:42:45.193Z","enrichmentBasis":"archived evidence"}}]}