The search targeted Web of Science, Scopus, ERIC, and PubMed/PMC for English-language articles published from January 2023 to April 2026, using combinations of (“generative AI” OR “text-to-image” OR “diffusion”) AND (“art education” OR “design education” OR “creative learning” OR “painting”) AND (“intervention” OR “experiment” OR “quasi-experimental” OR “pre-test” OR “effect size”). Hand-searches of three recent systematic reviews ([23]; [42]; [31]) supplemented the database search.

Candidate studies were screened against three inclusion criteria: (i) primary empirical data, quantitative or mixed methods; (ii) involvement of generative AI in a creative or art/design context; and (iii) at least one outcome measure relevant to one of the CRAFT stages-ideation, prompting, critique, iteration, or curation/publication. Purely conceptual papers, opinion pieces, and bibliometric studies without extractable data were excluded. Secondary reviews were not eligible as primary studies; three systematic reviews ([23]; [42]; [31]) were nonetheless retained separately as secondary sources used to frame, but not to populate, the per-stage evidence map. Where a single article reported more than one independent experiment, each experiment was treated as a separate primary dataset. A review of microteaching in teacher education ([22]) does not satisfy criteria (i) or (ii) and is therefore excluded from the primary corpus; it is cited only as adjacent, non-studio evidence in Section 5.2.3.

The database searches returned 287 records, and hand-searching the reference lists of the three systematic reviews added a further 24, giving 311 records in total. After 63 duplicates were removed, 248 unique records were screened on title and abstract, of which 201 were excluded as off-topic (no generative AI, no creative or art/design context, or no CRAFT-relevant outcome). The remaining 47 records were assessed in full text; 37 were excluded, comprising 28 that lacked primary empirical data (conceptual, opinion, or bibliometric pieces), 6 that reported no outcome mappable to a CRAFT stage, and 3 that fell outside a creative or art/design context. Ten primary studies met all inclusion criteria. Because [29] reports two independent experiments (Study 1 and Study 2), these are treated as two primary datasets; the corpus therefore comprises eleven datasets drawn from ten articles. The three retained systematic reviews are used as secondary framing sources and are not counted among the primary studies.

From each included primary study the following items were extracted: setting and population, sample size, study design, key reported outcome statistics (means, standard deviations, t/F/β values, p-values, and reported effect sizes where available), and the dominant CRAFT stage(s) the study informs. Mapping of studies to CRAFT stages was performed by the sole author and is presented as an interpretive single-coder synthesis; no second coder, codebook, or inter-rater reliability check was used. This is a significant source of bias and is acknowledged here and in Section 5.4. Future evaluations of CRAFT should employ at least two independent coders mapping primary studies against an explicit codebook. A quantitative meta-analysis was not performed because the included studies are too heterogeneous in outcome measures to support a common effect-size metric; instead, the evidence was combined narratively, with a per-stage evidence-mapping table (Table 1 and Table 2).

CRAFT was constructed using a metamodelling procedure adapted from [2]: a) collection of candidate process descriptions from published syllabi and the included primary studies; b) concept extraction; c) reconciliation of synonymous concepts; and d) abstraction into a generalized model. This procedure was chosen rather than closer education-research equivalents (framework synthesis or thematic synthesis) because its concept-reconciliation step is unusually explicit, which is helpful when working with heterogeneous instructor and researcher vocabulary. Five canonical stages emerged: Conceive, Render, Articulate, reFine, Transmit. The pipeline is illustrated in Figure 1, including the iteration loop from reFine back to Render or Conceive.

Figure 1. The CRAFT pipeline.

This stage sets the emotional and intellectual tone for what follows. Students engage in mood-boarding, journaling, and thumbnail sketching without using any AI tools. The working pedagogical assumption behind this stage-treated here as a hypothesis rather than an established finding-is that protecting students’ initial creative intent before AI tools are introduced may support more authentic ideation; the indirect evidence bearing on this assumption is discussed in Section 5.2.1.

The output of Conceive is then converted into a stratified visual prompt that conveys mood, object, and style reference. Students learn prompt grammar (role, context, instruction, format) explicitly, as modelled by the teacher. AI operates as the picture creator. The Render stage rests on a small but growing corpus of scholarship on the teaching of prompt engineering ([32]; [26]).

The generated outputs are subjected to an organized process of critique that includes feedback from peers, the instructor, and the AI acting as captioner. Reflection is the dynamic part of the process that prevents superficial acceptance or rejection of AI-produced outputs. The concept draws on the practice of reflection in both design education and wider practice theory.

Insights gained from articulation inform either a fresh start in Render (with changed parameters) or in Conceive (changing the emotion to convey). The reFine stage combines re-prompting with brushwork, positioning the human as an equal partner in the iterative process rather than as an editor of the resulting AI product. Within CRAFT, reFine forms the framework’s core commitment; however, evidence from drawing/painting labs is scarce, and the relevant data are mostly borrowed from neighbouring creativity tasks (Section 5.2).

For closure, the process has students select certain works to curate, write artist statements, and distribute their outputs through a public exhibit or portfolio. The role of AI in this stage is documentation. As currently formulated, Transmit receives the least attention in the literature.

The ten included primary studies (eleven datasets) are summarized in Table 2. Among the studies that report a single participant count, sample sizes range from N = 10 ([5]) to N = 405 ([44]); several studies use cohort-, class-, or workshop-level designs for which Table 2 does not list a single headline N ([21]; [34]; [10]; [9]). Settings span K-12 ([39]), higher-education studios and design courses ([20]; [17]; [5]; [34]; [10]; [9]), and an online experimental platform ([29]). Designs include two-condition experiments ([29], Studies 1 and 2), pre/post quasi-experiments ([39]; [9]), cross-sectional surveys with mediation analysis ([20]), structural equation models ([44]), and small-sample qualitative studies ([17]; [5]). Reported effect-size families differ across studies (Cohen’s d, Hedges’ g, β coefficients, mean differences); this heterogeneity, together with the single-coder mapping noted in Section 3.4, means the synthesis is interpretive and precludes pooled meta-analysis.

Table 2. Included primary studies with reported sample sizes, designs, and key findings. References are listed in full in the bibliography.

5.2.1. Conceive

Direct studio-painting evidence for this stage is limited and largely qualitative; the strongest quantitative evidence comes from design-education surveys, and the most rigorous experimental evidence comes from an adjacent (online text) task. These sources should not be read as equivalent to painting-studio evidence. Within art-and-design education, the strongest direct evidence supporting Conceive comes from [20]: in N = 121 design students at universities in southern China, AI knowledge predicted innovative thinking (β = .610, p < .001), with creative self-efficacy as a significant mediator (standardized β = .256, 95% CI [.140, .418]) and anxiety reduction as a serial second mediator. A companion case-study workshop by the same authors ([21]) showed that students who developed a layered understanding of text, content, and context produced higher-quality posters using DALL-E and Midjourney. [9] extend this in a quasi-experimental design-and-art course: the GAI condition outperformed the control on student achievement, motivation, and self-efficacy. Counter-evidence comes from [17] (N = 26 pre-service art teachers in Türkiye) and [5] (N = 10 bachelor art students in Latvia): in both, students valued AI for ideation but reported frustration when asked to execute nuanced artistic tasks without explicit pre-prompt scaffolding. Adjacent-domain evidence ([29])—an online text-based co-creation experiment—is consistent with the same general pattern but is not from a studio art setting and is therefore cited as suggestive rather than as direct studio support. The convergent reading is that protecting authentic creative intent before tool use may be associated with better outcomes, with the strongest evidence coming from design education and the most rigorous experimental design from outside the studio. Figure 2 illustrates the kind of expressive subject treatment the Conceive and Render stages aim to support.

Figure 2. The kind of expressive subject treatment the Conceive and Render stages aim to support.

5.2.2. Render

The evidence for Render is drawn from design and graphic-design coursework rather than from drawing/painting studios specifically; it is adjacent but remains within art and design. [21] found that students who developed a layered understanding of text, content, and context produced higher-quality posters when using DALL-E and Midjourney for graphic-design coursework. [32] document that explicit teaching of prompt structure (role, context, instruction, format) reliably improves output alignment in educational settings. However, [5] (N = 10) and [17] (N = 26) both report that students who used AI without prompt scaffolding found the tools frustrating for executing nuanced artistic tasks. The combined evidence supports CRAFT’s Render stage but emphasizes that prompt instruction must be explicit and modelled.

5.2.3. Articulate

Direct studio-painting evidence for reflective critique is limited; the evidence below is drawn mainly from adjacent domains (K-12 lessons on AI, higher-education design discussion, and teacher education) and should not be equated with painting-studio critique. [39] (N = 209, Grades 4 and 7 in Finland) showed that a single workshop on algorithmic bias produced significant gains in students’ written reasoning about generative-model outputs. A companion study with K-9 learners co-creating digital art ([40]) reached similar conclusions about the importance of structured reflection. At the higher-education level, [34] reported increases in participation and motivation when AI integration was paired with structured discussion, and [14] documented how online studio-critique protocols externalize tacit knowledge in design education. Adjacent teacher-education evidence ([22], a review of microteaching outside studio art) converges on the same point: reflective scaffolding is the active ingredient in skill-development interventions. The most informative counter-evidence is summarized in [23]: across the reviewed corpus, short AI-supported interventions can produce gains in objective performance not matched by gains in self-efficacy. CRAFT’s Articulate stage is designed to close this gap.

5.2.4. reFine

This is the stage with the least direct studio-painting evidence; most support comes from adjacent creative domains (poetry, music) and should be read as suggestive only. Direct studio-art evidence on iterative reFine is sparse but instructive: [17] and [5] both report that art students value AI for ideation but find iteration without scaffolding frustrating, with the execution of nuanced artistic effects remaining stubbornly hard. [9] report converging quasi-experimental gains in achievement, motivation, and self-efficacy when iterative GAI use was structured into a design-and-art course. Experimental data from adjacent domains (poetry: [29]; music co-creation: [44]) support the general claim that human-in-the-loop iteration tends to be rated more favourably than one-shot generation, yet neither case represents a painting-studio test. The reFine phase of CRAFT implements the facilitative condition-re-prompting combined with manual painting-as the type of iteration most likely to transfer to studio work. This suggested transfer is a conjecture produced by the model and not a proven result at this point. Figure 3 shows the reFine cycle in diagrammatic form: a rough sketch on the left is iteratively transformed into an expressive painting by hand on the right. Figure 4 illustrates the kind of memory-laden content that pilot lessons in this phase can aim at.

5.2.5. Transmit

No direct studio-painting study specifically tests this stage; the two supporting studies are situated in higher-education art/design courses more broadly. Direct empirical evidence for the Transmit stage is the thinnest in the corpus, reflecting the broader assessment-design gap noted across the systematic-review literature ([23]; [31]). [34] document motivation gains tied to public sharing of AI-assisted work, and [10] report critical-thinking gains in a mind-mapping plus GAI condition culminating in a presentation phase. The Transmit stage, as currently formulated, is therefore the least directly supported and is the priority target for future research.

Figure 3. The reFine loop, shown schematically.

Figure 4. The kind of memory-laden subject matter that pilot teaching exercises in this stage might target.

Despite the importance of pedagogical structure, much of the available literature on generative AI in art education is either descriptive of the technology or prescriptive about general digital literacy, with comparatively little operational specification for the studio. CRAFT proposes integrating these strands into a holistic concept that takes an explicitly stage-by-stage, evidence-mapped path. Table 3 compares CRAFT with the dominant existing accounts.

Table 3. CRAFT compared with existing accounts of generative AI in art and design education.

CRAFT and its synthesis face several limitations. First, this is a critical integrative review rather than a full PRISMA-compliant systematic review; no protocol was preregistered, and dual independent screening of all candidate records was not performed. Second, the ten primary studies use heterogeneous outcome measures, precluding pooled meta-analysis. Third, the studies are concentrated in higher education and East Asian contexts ([23]), limiting generalizability, and equity considerations ([19]) are not addressed uniformly. Fourth, the strongest experimental evidence cited ([29]) is from an online text/poetry co-creation task with non-artist Prolific participants, not a studio drawing or painting study; the genuine art-and-design education evidence base, while real, is dominated by smaller and more qualitative studies ([17]; [5]). The synthesis is therefore stronger on whether CRAFT’s component mechanisms have any empirical support than on whether they have been demonstrated specifically in studio painting and drawing. Fifth, none of the included studies tests CRAFT directly; the synthesis evaluates the framework’s component mechanisms, not the framework as a whole. Sixth, AI tools evolve rapidly: improvements in diffusion-model training ([24]) and unresolved fair-use questions around training data ([25]) mean that 2023-2024 model-generation findings may not transfer cleanly to 2025-2026 generations. Seventh, the framework presumes equitable access to GPUs, stable internet, and licensed software-conditions not met in many art programs ([36]).

To validate CRAFT and move it from concept toward field use, six concrete next steps are proposed. (A) A direct quasi-experimental evaluation of CRAFT against an active control, with sufficient power for medium effects (n ≥ 25 per group), external blind raters scoring artifacts on a rubric that excludes intervention-confounded constructs, fidelity-of-implementation measurement, and pre-registration of the analysis plan. (B) Multi-site replication across at least two institutional contexts, to address the East-Asia and higher-education skew documented in [23]. (C) Targeted Render-stage research on prompt-grammar instruction grounded in the design guidelines of [26]. (D) Articulate-stage research on reflective scaffolds adapted from [8]. (E) Transmit-stage research developing curation rubrics for AI-assisted artworks, drawing on the wider AI-art review literature ([7]). (F) Face-validation of the framework by domain experts and cognitive-taxonomy alignment of CRAFT outcomes with revised Bloom levels ([3]).