A Design Thinking-Driven Conceptual Framework for a Creative AI Learning Environment to Enhance Programming Skills (CAILE)
Bibliographic record
Abstract
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 ≈ 4.71) with short dispersions. Section means were likewise high, led by Theoretical Alignment & Rigor (≈ 4.79) and Implementation Feasibility (≈ 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".