{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132553"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132553","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Exploring AI integration in graduate interpreter training: a mixed methods study on pedagogical adaptation and professional futures","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Bargat, Aurore"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ed.D.","degree_level":"Dissertation","degree_discipline":"Educ Policy, Orgzn & Leadrshp","degree_department":null,"school":null,"contributors":["Kalantzis, Mary","Cope, William","Dhillon, Pradeep","You, Yu-Ling"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["interpreter training","AI integration"],"languages":["en"],"rights":["Copyright 2025 Aurore Bargat"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132553","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kalantzis, Mary","Cope, William","Dhillon, Pradeep","You, Yu-Ling"]},{"key":"dc:creator","label":"Author","values":["Bargat, Aurore"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-12-02"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Educ Policy, Orgzn & Leadrshp"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ed.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["interpreter training","AI integration"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Aurore Bargat"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132553"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Aurore Bargat, accepted the attached license on 2025-12-01 at 15:53.","The student, Aurore Bargat, submitted this Dissertation for approval on 2025-12-01 at 16:28.","This Dissertation was approved for publication on 2025-12-02 at 10:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #23010 on 2026-02-19 at 18:25:56"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Exploring AI integration in graduate interpreter training: a mixed methods study on pedagogical adaptation and professional futures"]}]}],"canonical_facts":{"dc:contributor":["Kalantzis, Mary","Cope, William","Dhillon, Pradeep","You, Yu-Ling"],"dc:creator":["Bargat, Aurore"],"dc:date":["2025-12","2025-12-02"],"dc:description":["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.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Aurore Bargat, accepted the attached license on 2025-12-01 at 15:53.","The student, Aurore Bargat, submitted this Dissertation for approval on 2025-12-01 at 16:28.","This Dissertation was approved for publication on 2025-12-02 at 10:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #23010 on 2026-02-19 at 18:25:56"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132553"],"dc:language":["en"],"dc:rights":["Copyright 2025 Aurore Bargat"],"dc:subject":["interpreter training","AI integration"],"dc:title":["Exploring AI integration in graduate interpreter training: a mixed methods study on pedagogical adaptation and professional futures"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Educ Policy, Orgzn & Leadrshp"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ed.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}