Didactic principles and ethical strategies for assessment in language teaching using generative models

Abstract

The current stage of digitalization in education, characterized by the dominance of generative AI, requires scientific understanding and the development of didactic principles and ethical strategies. These are designed to ensure the methodologically sound application of generative models for assessment and evaluation, unlocking their pedagogical potential in a personalized environment, minimizing risks, and preserving the teacher’s leading role. In response to methodological and ethical challenges, basic principles (double verification, human-centeredness, adaptability) have been formulated, and ethical strategies (validity, fairness, transparency) have been systematized. The proposed multi-level model for integrating neural networks into language assessment is based on a cyclical sequence of stages, where each stage corresponds to teacher actions, didactic principles, and ethical strategies.

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