Exploring ethical dimensions of AI-enhanced language education: A literature perspective

Abstract

Advances in artificial intelligence (AI), particularly in generative AI, continue to affect language education paradigms. The integration of AI in language education raises deep-seated ethical concerns such as privacy and data security, potential biases and hidden ideologies in the output, transparency and accountability, dependency and autonomy, digital divide, and job displacement and professional development. The article analyzes these ethical concerns and introduces the multifaceted dimensions of ethics associated with AI in language education. This article comprehensively examines the potential biases of AI in language education. These biases can be algorithmic, demographic, cultural, linguistic, temporal, confirmation, ideological and political. The analysis includes factors contributing to biases, such as training data , labelling and annotation, product design decisions, policy decisions, and algorithms. This paper analyzes algorithmic transparency and advocates for more transparent AI systems to address bias in algorithms. Violations of student privacy emerge as one of the profound ethical issues in the discourse on AI-enhanced language education. The article also examines the challenges and risks associated with the protection of student data privacy, emphasizing the need for robust privacy frameworks to alleviate concerns regarding privacy, human agency and the lack of transparency in the collection of an excessive amount of personal information. By synthesizing the key findings, the paper will conclude with a potential framework of ethical guidelines for the responsible and ethical integration of AI in language education.

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