AI-Resistant Assessment Reform in Teaching Foreign Languages (TFL)

Abstract

Artificial Intelligence (AI) continues to reshape education worldwide, offering interactive, personalized, and highly efficient learning opportunities. In Teaching Foreign Languages (TFL), tools such as chatbots, grammar checkers, and translation systems support learners by providing instant feedback and tailored instruction. However, alongside these benefits emerge serious concerns: reduced human interaction, compromised academic integrity, data privacy risks, and possible algorithmic bias. This paper examines both the opportunities and challenges of AI integration in TFL, with a specific focus on Uzbekistan’s educational context. Responding to rising cases of AI-generated assignments and the difficulty teachers face in evaluating authentic student output, the study proposes an AI-resistant assessment model. This model prioritizes human participation, authentic communication, and fair evaluation, while maintaining the efficiency principles outlined by Kolegova and Levina (2024).