Creativity in the traditional arts of Iran is fundamentally conceptualized not as an arbitrary departure from inherited conventions or unrestricted novelty, but rather as disciplined innovation and rediscovery occurring within an established framework of proportional rules, formal geometries, and sacred cultural meanings. In this paradigm, the traditional master is not a disruptive innovator, but an explorer who unveils profound layers of meaning through technical virtuosity, active imagination, and intuitive insight. Despite the centrality of this phenomenon, research in the field has long suffered from the absence of a unified, objective framework capable of simultaneously capturing adherence to tradition, material craftsmanship, semantic depth, and constrained original invention. To overcome the static limitations and reductionist risks of existing linear models, this study designed and empirically validated a dynamic, multidimensional evaluation framework tailored to Iranian traditional arts.

The investigation utilized an exploratory sequential mixed-methods research design. During the qualitative phase, interpretive thematic analysis was conducted across twelve classical and contemporary theoretical sources, twenty canonical masterworks spanning diverse historical epochs and media (including calligraphy, Persian miniature, lusterware ceramics, architectural plasterwork, and textile arts), and semi-structured interviews with seven prominent master practitioners and academic scholars possessing twenty to thirty years of professional experience. High inter-coder reliability was confirmed with a Krippendorff's alpha of 0.86. This qualitative synthesis yielded four interconnected analytical layers: aesthetic-traditional alignment, structural-geometric mastery, semantic-interpretive depth, and computational-quantitative measurability. Each layer was operationalized into three distinct indicators, establishing a comprehensive set of twelve measurable metrics covering proportional fidelity, constrained innovation, visual harmony, geometric density, technical execution, structural novelty, symbolic stratification, associative power, interpretive authenticity, algorithmic modelability, computational rarity, and organized information entropy.

To accurately represent the temporal evolution of artistic mastery—from foundational apprenticeship to transcendent improvisation—the systemic interactions across these four dimensions were formalized through a discrete-time difference equation:

X(t+1)=X(t)+αWX(t)+βϵ(t). In this state-space formulation, the vector X(t) denotes the four-dimensional profile of the artwork,

W represents a normalized directed-connectivity matrix derived from empirical network analysis, α controls the gradual rate of adaptation, and βϵ(t) introduces a normally distributed stochastic disturbance term (μ=0,σ=0.05). This stochastic component serves as the mathematical representation of artistic intuition, unpredictable emergence, and spontaneous creative insight, effectively preventing the reduction of traditional craftsmanship to a deterministic algebraic mechanism.

Rigorous quantitative evaluation procedures demonstrated the structural integrity, internal consistency, and predictive power of the proposed model. The empirical corpus comprised thirty authentic artworks evaluated by experts, alongside one thousand synthetic agents simulated over fifty iterative steps to examine convergence within the dynamic state space. Confirmatory factor analysis verified the four-factor latent structure, yielding standardized factor loadings above 0.72 (p<.001) and robust model fit indices (χ 2 /df=1.52, CFI = 0.95, RMSEA = 0.042). Internal-consistency testing established excellent reliability across all four dimensions, with Cronbach's alpha coefficients of 0.87 for the aesthetic layer, 0.91 for the structural layer, 0.85 for the semantic layer, and 0.89 for the computational layer. Furthermore, network stability tests confirmed that the empirical interaction weights remained highly consistent after expanding the sample size (r=0.94, p<.001, with a mean absolute difference of only 0.03).

Stepwise linear regression analysis identified the dynamic interaction between structural-geometric mastery and semantic-interpretive depth (X Geo ×X Sem) as the single most influential predictor of overall artistic creativity, with a standardized beta coefficient of 0.89 (p<.001). The aesthetic-traditional and computational-quantitative layers also made statistically significant contributions, with coefficients of 0.21 and 0.09, respectively. These results substantiate the theoretical principle that technical discipline and profound geometric knowledge do not restrict creative expression, but rather serve as the indispensable structural engine that propels authentic semantic discovery and spiritual meaning in Iranian art. Ten-fold cross-validation conducted on a broader testing set of fifty-five masterworks achieved an explanatory coefficient of determination of R 2 =0.84 and a mean absolute percentage error of only 8.3% (SD = 1.2%), confirming that the model accounts for over 91% of variance in human expert consensus.

Subsequent expert-informed cultural calibration incorporated two essential qualitative safeguards: an aesthetic gatekeeper threshold, which applies a penalty multiplier of 0.80 to overall creativity scores if adherence to traditional rules falls below 0.50, and the explicit preservation of the stochastic intuition term to maintain the living character of artistic judgment. Comparative contextualization demonstrated that while the overarching four-layer architecture is structurally transferable to other Asian traditions, interaction trajectories differ significantly; for instance, Chinese art prioritizes intuitive spontaneity over rigid mechanics, Indian traditions subordinate geometric structures entirely to cosmological symbolism, and Ottoman arts impose stricter aesthetic boundaries on innovation. In conclusion, the proposed framework successfully bridges mathematical modeling, data-driven analytics, and cultural connoisseurship, providing an objective, transparent, and culturally grounded diagnostic instrument for academic research, festival curation, educational curriculum design, and the institutional preservation of Iranian artistic heritage. Consequently, this dynamic approach effectively bridges ancient artistic wisdom with modern computational evaluation methods.