@article{4841,
  author = {Sopida Tuammee},
  title = {AI-Generated Language Assessment Validity, Reliability, Equity, Privacy, and Pedagogical Value},
  journal = {Journal of Information Technology Review},
  year = {2026},
  volume = {17},
  number = {4},
  doi = {https://doi.org/10.6025/jitr/2026/17/4/227-243},
  url = {https://www.dline.info/jitr/fulltext/v17n4/jitrv17n4_3.pdf},
  abstract = {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.},
}