We anticipate the following key outcomes:

Preliminarily Validated Scale: Release the General AI Attitude Scale (GAIAS) with versions in Chinese, English, French, German, and Japanese, along with item lists, administration manuals, and standardized scoring guidelines for academic and applied use.

Reliability & Validity Evidence: Demonstrate structural, content, and construct validity through EFA, CFA, and IRT analyses; confirm internal consistency (Cronbach’s α ≥ 0.8 for each factor) and measurement invariance across languages, establishing a robust tool for cross-cultural comparisons.

AI-Augmented Methodology: Quantify the GPT-4 API’s impact on item-generation efficiency (e.g., ≥50% reduction in generation time), decreased expert review workload (fewer review rounds), and improved content quality (enhanced terminology consistency and clarity ratings), providing empirical support for generative AI in psychometrics.

Open Resources & Case Studies: Publish the item bank, prompt templates, and validation scripts on GitHub, accompanied by case studies to guide other researchers and organizations in efficiently developing psychometric instruments with the GPT-4 API.

Scholarly & Societal Impact: Publish at least two papers on generative-model interpretability and fairness in scale development; offer policymakers, market researchers, and educational evaluators a reliable public AI-attitude monitoring tool, facilitating evidence-based AI deployment strategies and outreach.