Sep 2026· Intelligent & Human Futures· Vol 2, pp. 1-19· 0 citations· 12 references
TL;DR
A conceptual instructional model is developed that embeds generative AI within the five-stage design thinking process for an eighth-grade visual communication course and indicates that the experimental group outperformed the control group on aesthetic judgment, creative problem-solving, and stylistic self-awareness.
Abstract
Middle school art curricula in many systems still center on skill reproduction, which sits uneasily with the rapid diffusion of generative AI. This study develops a conceptual instructional model that embeds generative AI within the five-stage design thinking process (empathize, define, ideate, prototype, test) for an eighth-grade visual communication course. AI is assigned differentiated functions across the stages—analytical, retrieval, generative, assistive, and evaluative—while students retain responsibility for aesthetic judgment, justification of selections, and iterative revision. Three learning outcomes anchor the design and are operationalized in parallel measurement instruments: aesthetic judgment, creative problem-solving in visual communication, and stylistic self-awareness. Communicative intent is treated as the core sub-dimension of creative problem-solving rather than as a separate outcome, since intent is observable only through the design products and decisions that enact it. A quasi-experimental pretest-posttest pilot was conducted with 68 eighth-grade students (experimental group n = 34, control group n = 34) over a ten-week intervention at a single school, with both classes taught by the same art teacher. ANCOVA indicated that the experimental group outperformed the control group on aesthetic judgment, F(1, 65) = 8.42, p = .005, ηp² = .115, and creative problem-solving, F(1, 65) = 9.17, p = .003, ηp² = .124, with smaller yet significant gains on stylistic self-awareness. Thematic analysis of journals and interviews surfaced three patterns: shifted attention from execution to selection, prompt-based reasoning, and unease about authorship. Findings are presented as encouraging pilot evidence under specific local conditions, pending replication beyond the present site.
The evidence supports a cautious conclusion: AI commonly expands ideation, visualization, feedback, and workflow options, while stronger claims about improved creativity or learning depend on controlled designs, objective/artefact-based assessment, and longer-term evidence.
Jin-Jin Zhu, Vimala Govindasamy· Journal of Educational and S...· 0 citations
Generative artificial intelligence is increasingly shaping creative learning in higher education art courses, yet its educational value remains contested when creative support, output generation, authorship, and skill development become intertwined. This study investigates AI-supported creative learning in an under...
Ning-Tao Shen, Lai Soon Lee, Aneng He· Frontiers in Psychology· 0 citations
Students’ selective and context-dependent AI use can be interpreted as boundary-setting around when and how AI participates in the design process, and value is suggested in a judgment-centered, process-based approach to AI pedagogy that emphasizes accountability and reflective decision-making.
M. B. Huh, Alp Tural, Elif Tural· Education sciences· 0 citations
This exploratory case study examines the impact of AI-assisted previsualization on creative decision-making, workflow efficiency, and skills development among 21 undergraduate students at an Indonesian film school that enrolled in Production Design course for whole semester. A 30-item questionnaire (Cronbach’s α = 0.82...
Meilani Meilani· International Conferences on...· 0 citations
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 m...
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Vasiliki Karampa, Vagia Kakava, Ageliki Mellou et al.· European Conference on Games...· 0 citations
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.