Speaker
Description
Artificial Intelligence (AI) is transforming language education, particularly in the area of testing and assessment. This paper explores how AI can support teachers in creating differentiated language tests that better address learners’ diverse proficiency levels, learning needs, and cognitive abilities. In many EFL classrooms, traditional assessments often apply a “one-size-fits-all” approach, which may not accurately measure students’ abilities or support inclusive learning environments. By leveraging AI tools, teachers can generate multiple versions of test items, adjust difficulty levels, and design tasks targeting different language skills while maintaining alignment with learning objectives.
The study presents practical strategies for using generative AI to develop differentiated reading, vocabulary, and grammar test items. Examples include generating graded reading passages, modifying question complexity, and creating parallel test forms to ensure fairness and accessibility. The paper also discusses potential benefits such as increased efficiency, improved test validity, and enhanced learner engagement, as well as challenges related to reliability, teacher digital literacy, and ethical considerations.
The findings suggest that AI can function as a supportive tool that empowers teachers to design more inclusive and adaptive assessments rather than replacing teacher judgment. This presentation provides practical guidelines for integrating AI into language testing practices to promote equitable assessment in EFL classrooms.
