Exploring AI integration in graduate interpreter training: a mixed methods study on pedagogical adaptation and professional futures

Bargat, Aurore

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https://hdl.handle.net/2142/132553 Copy

Description

Title

Exploring AI integration in graduate interpreter training: a mixed methods study on pedagogical adaptation and professional futures

Author(s)

Bargat, Aurore

Issue Date

2025-12-02

Director of Research (if dissertation) or Advisor (if thesis)

Kalantzis, Mary

Doctoral Committee Chair(s)

• Kalantzis, Mary
• Cope, William

Committee Member(s)

• Dhillon, Pradeep
• You, Yu-Ling

Department of Study

Educ Policy, Orgzn & Leadrshp

Discipline

Educ Policy, Orgzn & Leadrshp

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ed.D.

Degree Level

Dissertation

Keyword(s)

• interpreter training
• AI integration

Language

eng

Abstract

The exponential growth in demand for spoken-language interpreting services has been paralleled by rapid advances in artificial intelligence (AI) technologies that are reshaping professional and educational practice (Ferreira & Schwieter, 2022; Kalina, 2000; Kalina & Barranco-Droege, 2021). Despite increasing adoption of AI in related fields, graduate-level interpreter education remains under-explored with respect to systemic AI integration. This study investigates the pedagogical implications of AI adoption in spoken-language interpreting education through an explanatory sequential mixed methods design. The research addresses two guiding questions: (1) How do interpreting students and instructors envision the role of human interpreters in an era of simultaneous AI interpreting? and (2) What strategies can interpreting educators adopt to integrate AI responsibly while preserving core human competencies? In this study, quantitative data was collected from 18 student surveys and followed by in-depth interviews with ten experienced instructors across eight universities located in the United States and in Europe. The findings reveal limited but growing engagement with AI primarily in the domains of terminology extraction, self-directed practice, and formative assessment. Students mentioned the need to be up to date with technology tools supporting interpreting tasks while instructors articulated both optimism for increased learner autonomy and concern about the perceived incompatibility of current AI platforms with the nuanced demands of authentic interpreting practice. This dissertation extends the literature by providing evidence-based recommendations for interpreter education policy, including curricular models. It argues that sustained, critical engagement with AI can enhance, but not replace, the core humanistic values at the heart of interpreter training. The findings contribute to a nuanced understanding of how educators navigate technological transformation and inform future pathways for ethical and effective innovations in interpreter training.

Graduation Semester

2025-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/132553

Copyright and License Information

Copyright 2025 Aurore Bargat

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