AI Conversational Tutors in Foreign Language Learning: A Mixed-Methods Evaluation Study

This paper focuses on AI tutors in foreign language learning, a field of application of AI tutors with great development, especially during the last years, when great advances in natural language understanding and processing in real time, have been achieved. These tutors attempt to address needs for improving language skills (speaking, or communicative competence, understanding). In this paper, a mixed-methos empirical study on the use of different kinds of state-of-the-art AI tutors for language learning is reported. This study involves a user experience evaluation of typical such tools, with special focus in their conversation functionality and an evaluation of their quality, based on chat transcripts. This study can help establish criteria for assessing the quality of such systems and inform the design of future tools, including concerns about data privacy and secure handling of learner information.

Publication Details

Journal
ArXiv.org
Published
2025-08-07
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
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article

AI Conversational Tutors in Foreign Language Learning: A Mixed-Methods Evaluation Study

ArXiv.org
Intelligent Tutoring Systems and Adaptive Learning
article

AI Conversational Tutors in Foreign Language Learning: A Mixed-Methods Evaluation Study

article en

Abstract

This paper focuses on AI tutors in foreign language learning, a field of application of AI tutors with great development, especially during the last years, when great advances in natural language understanding and processing in real time, have been achieved. These tutors attempt to address needs for improving language skills (speaking, or communicative competence, understanding). In this paper, a mixed-methos empirical study on the use of different kinds of state-of-the-art AI tutors for language learning is reported. This study involves a user experience evaluation of typical such tools, with special focus in their conversation functionality and an evaluation of their quality, based on chat transcripts. This study can help establish criteria for assessing the quality of such systems and inform the design of future tools, including concerns about data privacy and secure handling of learner information.

ArXiv.org
Quality Education
Openalex Percentile: Top 73%
Intelligent Tutoring Systems and Adaptive Learning
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