The Glossary-Informed Machine Translation and Multilingual Access Initiative at SUNY Empire State University was developed to address linguistic and accessibility barriers faced by Spanish-dominant students in online programs. Funded through an internal innovation grant, the project created a scalable translation workflow that integrates Microsoft Translator with a custom bilingual glossary and structured ChatGPT prompts, aligning translation with institutional terminology, tone, and accessibility standards (WCAG 2.1; UDL).
The initiative’s core success lies in combining human expertise with AI-driven efficiency. Two part-time linguists – both PhDs in Linguistics and certified translators – collaborated with the Digital Accessibility Coordinator to design and evaluate the process. Their glossary of more than 500 academic and administrative terms ensures consistency across course materials, website content, and student communications.
Evidence of success:
- Translations produced through the glossary-informed workflow required 50% less post-editing time than traditional manual translation.
- The workflow enabled the rapid localization of Brightspace course shells, reducing turnaround from 4–6 weeks to less than two.
- Research emerging from this initiative has been presented the ALBUS conference and won an award for best student paper.
The initiative reduced outsourcing costs by integrating Microsoft Azure’s existing enterprise license and leveraging open-source AI tools for post-editing support.
Lessons learned:
- Human oversight is essential: MT accuracy can increase dramatically when linguists supervise and train the system using curated glossaries.
- Terminology consistency builds trust: Institutionally coherent translations as more credible and welcoming.
- Sustainability requires integration: Embedding translation into existing workflows, rather than treating it as an afterthought, ensures continuity and scalability.
- Collaboration is key: Success depended on cooperation between accessibility staff, linguists, and instructional designers, reflecting the interdisciplinary nature of inclusive design.
When accessibility, language, and AI intersect intentionally, institutions can achieve cost-efficient, equitable, and replicable multilingual models that directly enhance student belonging and academic success.