Journal of Information Technology Review Sopida Tuammee 17 4 2026 https://doi.org/10.6025/jitr/2026/17/4/227-243 https://www.dline.info/jitr/fulltext/v17n4/jitrv17n4_3.pdf Integrating Artificial Intelligence (AI) into language assessment offers transformative opportunities for scalability and personalised feedback, but it also raises critical concerns about validity, equity, privacy, and pedagogical value. This study evaluates the responsible deployment of AI-generated language assessments using the Responsible AI Language Education Benchmark, an analysis-ready dataset comprising 960 learner profiles across eight countries and four CEFR proficiency levels. We assessed three anonymized generative AI models (Model_A, Model_B, and Model_C) using a multidimensional, non compensatory framework requiring a minimum threshold score of 70 across four pillars: Validity, Equity, Privacy, and Pedagogical Value. Results indicate that Model_A provides the most balanced profile, excelling in equity (92.86) and pedagogical value (79.22), yielding the highest learning revision and transfer gains. Model_B shows exceptional privacy protection (95.48) and perfect policy transparency, making it well suited for privacysensitive contexts. Conversely, Model_C fails the deployment criterion because of inadequate privacy (66.78) and pedagogical value (69.16), along with the highest bias incident rate and the weakest learning outcomes. The findings revealed that technical accuracy alone is insufficient for educational deployment; a noncompensatory, multi-pillar evaluation is essential to ensure AI assessments are equitable, secure, and pedagogically sound.