This study introduces CAILE, a design thinking-driven conceptual framework for a Creative AI Learning Environment, designed to enhance programming skills. Evaluates clarity, appropriateness, and feasibility through expert judgment. Phase 1 synthesized 34 peer-reviewed studies (2019-2025) to articulate CAILE's structure across three layers: Inputs (Generative AI platform, Design Thinking framework, Creative learning environment, Computational-thinking foundation), Learning Process (Empathize & Define; Ideate with AI; Prototype & Create; Test & Evaluate; Implement & Scale), and Outputs (Programming proficiency, Creative innovation, Critical thinking, 21st-century skills). Phase 2 operationalized these components into a 28-item instrument and gathered ratings from eight experts on a 5-point Likert scale. Descriptive analyses and publication-ready visualizations (heatmap, section distributions, item-level forest plots, and a dumbbell comparison) were used to summarize evidence. Results show uniformly high to very high appropriateness across items (overall mean [approximately equal to] 4.71) with short dispersions. Section means were likewise high, led by Theoretical Alignment & Rigor ([approximately equal to] 4.79) and Implementation Feasibility ([approximately equal to] 4.75). A modestly wider spread for Output items indicates where indicator definitions and exemplars can be sharpened without altering favorable central tendencies. Collectively, the findings suggest that CAILE is both conceptually robust and practically actionable, offering a coherent pathway that weaves together generative AI, design thinking, and computational thinking, from empathic problem framing to scalable implementation. Future work will involve conducting design-based classroom trials and psychometric validation of the instrument, as well as examining equity and ethical considerations in AI-supported programming education.