Using AI-driven interventions in EAP classes: a mixed-method study on motivation and learning outcomes

In recent years, artificial intelligence (AI) technologies have permeated education, including English for academic purposes (EAP) classrooms. Given the growing relevance of AI for prospective educators, further empirical research in this field is crucial. Consequently, this study addresses the need for such research by examining AI-supported activities in EAP classrooms, where academic writing and discipline-specific literacies are crucial, with particular attention to English teacher trainees’ perceptions of their motivation and self-perceived learning outcomes, as well as factors relevant to the implementation of AI in EAP classes. This mixed-methods study was conducted at the University of Vienna, wherein approximately 100 students participated in a quantitative survey on AI in EAP classes. Focusing on self-reported perceptions rather than objective performance, the survey aimed to assess how participants perceived the integration of AI in relation to their motivation and self-perceived learning outcomes. Additionally, qualitative interviews – which were used illustratively – aimed to investigate their attitudes towards AI. The findings display context-bound perceptions rather than generalisable results. Findings indicate that while some scepticism towards AI technologies persists, especially in terms of ethical concerns, the participants generally reported some positive self-perceived outcomes in AI-supported EAP classes, whereas the motivational factor remained inconclusive. Interview responses underscored a cautious perspective, especially with regard to the verification of information, but also described perceived benefits for prospective teachers. However, the teacher trainees also stated that AI technologies could carry certain risks, such as over-reliance on AI. • Self-perceived learning outcomes of EAP teacher trainees may be supported by AI. • EAP teacher trainees report mixed effects of AI on their motivation. • EAP teacher trainees express mixed attitudes towards AI.

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Publication Details

Journal
Journal of English for Academic Purposes
Published
2026-03-03
DOI
https://doi.org/10.1016/j.jeap.2026.101656
Primary Topic
Educational and Psychological Assessments
Type
article
Field-Weighted Citation Impact
0.00
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article

Using AI-driven interventions in EAP classes: a mixed-method study on motivation and learning outcomes

Sophie Gaugl
Journal of English for Academic Purposes
Educational and Psychological Assessments
article

Using AI-driven interventions in EAP classes: a mixed-method study on motivation and learning outcomes

Sophie Gaugl
article en

Abstract

In recent years, artificial intelligence (AI) technologies have permeated education, including English for academic purposes (EAP) classrooms. Given the growing relevance of AI for prospective educators, further empirical research in this field is crucial. Consequently, this study addresses the need for such research by examining AI-supported activities in EAP classrooms, where academic writing and discipline-specific literacies are crucial, with particular attention to English teacher trainees’ perceptions of their motivation and self-perceived learning outcomes, as well as factors relevant to the implementation of AI in EAP classes. This mixed-methods study was conducted at the University of Vienna, wherein approximately 100 students participated in a quantitative survey on AI in EAP classes. Focusing on self-reported perceptions rather than objective performance, the survey aimed to assess how participants perceived the integration of AI in relation to their motivation and self-perceived learning outcomes. Additionally, qualitative interviews – which were used illustratively – aimed to investigate their attitudes towards AI. The findings display context-bound perceptions rather than generalisable results. Findings indicate that while some scepticism towards AI technologies persists, especially in terms of ethical concerns, the participants generally reported some positive self-perceived outcomes in AI-supported EAP classes, whereas the motivational factor remained inconclusive. Interview responses underscored a cautious perspective, especially with regard to the verification of information, but also described perceived benefits for prospective teachers. However, the teacher trainees also stated that AI technologies could carry certain risks, such as over-reliance on AI. • Self-perceived learning outcomes of EAP teacher trainees may be supported by AI. • EAP teacher trainees report mixed effects of AI on their motivation. • EAP teacher trainees express mixed attitudes towards AI.

Journal of English for Academic PurposesVol. 81
University of Vienna (AT)
Openalex Percentile: Top 39%
Educational and Psychological Assessments
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