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Can Artificial Intelligence be Used to Create a Diagnostic Assessment for English Language Learners Entering School Mid-Year?
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2025-12
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EDI 722 Lit Review-2.pdf
Adobe PDF, 2.28 MB
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The mid-year enrollment of English Language Learners (ELLs) presents a recurring assessment and placement problem for K–12 educators: conventional placement instruments and procedures frequently lack sensitivity to diverse linguistic backgrounds, consume substantial time, and may yield misclassifications that impede instruction. This capstone examines whether artificial intelligence (AI) can be employed to construct a diagnostic assessment specifically for ELLs entering school mid year. Informed by a systematic review of literature on diagnostic assessment, natural language processing, fuzzy logic approaches, AI generated feedback, and implementation challenges, the study outlines the development of an AI driven diagnostic tool comprising adaptive tasks in reading, writing, listening, and speaking, coupled with transparent scoring methods. The study will evaluate validity, reliability, and classification accuracy through empirical comparison with human assessor ratings and existing placement measures, alongside practitioner feedback regarding usability, equity, and instructional utility. The extant literature indicates potential advantages of AI, including improved efficiency, reduced assessment related anxiety, and richer diagnostic profiles, alongside notable cautions regarding bias, overreliance, and the necessity of professional development and policy safeguards. The product pairs an AI assisted, ELL designed diagnostic for reading and writing with focused professional development that trains teachers to generate and verify AI produced translations and to translate results into immediate instructional supports. This combined approach can accelerate timely, equitable placement and instruction for mid‑year ELLs while emphasizing necessary human oversight, ongoing validation, and sustained teacher preparation.