A Prospective Evaluation Framework for Nlp-assisted Foreign- Language Grammar Teaching and Assessment

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This article develops an integrated framework for evaluating natural language processing systems used in foreign-language grammar teaching and assessment. It treats symbolic, statistical, recurrent, attention-based, sequence-to-sequence, and hybrid approaches as complementary components of an interpretable and pedagogically responsible system. The framework is operationalized through a prospective mixed-method, nonequivalent-groups quasi-experimental design for adult foreign-language learners; no completed empirical findings are reported. The proposed protocol integrates model comparison, learner-error annotation, calibration, teacher review, immediate and delayed assessment, qualitative inquiry, validity safeguards, and responsible data governance. Its central contribution is to distinguish technical performance from pedagogical effectiveness and to require evidence across error detection, correction quality, explanation, learner uptake, delayed transfer, interpretability, fairness, and human oversight. The article argues that NLP-assisted grammar-support systems should be evaluated not only by predictive accuracy or fluency, but also by their capacity to preserve meaning, communicate uncertainty, protect learner agency, and support durable learning across instructional contexts.