From Linguistic Rights to Explainable Learning Analytics: A Cross-jurisdictional Policy Framework for Generative AI Feedback in Professional Interpreter Education

From Linguistic Rights to Explainable Learning Analytics: A Cross-jurisdictional Policy Framework for Generative AI Feedback in Professional Interpreter Education | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From Linguistic Rights to Explainable Learning Analytics: A Cross-jurisdictional Policy Framework for Generative AI Feedback in Professional Interpreter Education Ran Yi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7848799/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This methodological article proposes a cross-jurisdictional framework that links linguistic rights and procedural justice to explainable learning analytics for GenAI feedback in interpreter education. We define Procedural-Justice Consistency (PJC) indicators, speech-act preservation, presupposition fidelity, and tone/force consistency, and operationalise them through a minimal validation model. Using a bilingual dataset from criminal court interpreting (≈ 3,250 minutes of recordings; ≈112,500 transcribed words), we reanalyse lawyer questioning and witness testimony stylistics with interpretable decision trees and SHAP summaries, and triangulate findings with participant reflections on fairness and trust. Results show that light, discourse-oriented features can learn holistic “manner-of-speech” risks, while micro-errors remain better suited to human review. We provide force-aware prompts, indicator-linked explanations, and lean audit logging that map directly to institutional governance. The framework supports cross-jurisdictional alignment by keeping a stable PJC core and tunable thresholds for different regulatory profiles. Generative AI explainable learning analytics procedural justice court interpreting speech acts Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7848799","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571141559,"identity":"c22b1f3c-8af7-4f0f-8bc4-383c0e1bf761","order_by":0,"name":"Ran 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