Assessing Language Competence in Adaptive AI Learning

Abstract

This article examines outlines an integrated model and foundational principles for the responsible implementation of adaptive artificial intelligence (AI) in higher education language proficiency assessment. It addresses critical ethical and practical challenges, focusing on fairness, accountability, validity, and reliability. The text warns against "metric dominance" and unintended biases caused by continuous algorithmic adjustments. To counter this, it proposes a dual-control architecture where pedagogical principles and human oversight govern automated processes.