
Beyond captions: how to build truly inclusive environments for deaf students

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Universities have made real progress towards digital accessibility. We now record lectures and switch captions on more often. From an administrative perspective, the compliance box is often ticked. However, for many deaf and hard-of-hearing (D/HH) students, the learning experience is still exhausting.
鈥婽he challenge is architectural. While hearing students process audio and visuals in parallel, a D/HH student must process everything in serial 鈥 flicking their eyes between the lecturer, the interpreter, the slide and the captions. This 鈥渧isual ping-pong鈥 creates a split-attention effect that leads to massive cognitive fatigue. To make digital transformation meaningful, we must move beyond 鈥渁ccommodating鈥 disability to design for visual bandwidth.
鈥婤uild the visual landscape
鈥婦/HH students are almost entirely visual learners. If your slide layout is cluttered or your pacing is too fast, vital information is lost during the transition between looking at content and looking at translation.
- 鈥Apply the 鈥25 per cent rule鈥: always reserve one-quarter of your slide as a 鈥渟afe harbour鈥. This empty space ensures that an interpreter window or a 3D sign-language avatar can be overlaid without obscuring data.
- 鈥The 10-second rule: after displaying a complex diagram, pause for 10 seconds before speaking. This allows students to 鈥渞ead鈥 the visual landscape before they shift their attention back to the source of translation.
- 鈥Demonstrate visual agency: spend two minutes in your first session showing students how to pin and resize windows. Giving them control over their screen layout reduces frustration immediately.
Evidence of impact: at my university, we found that lectures supplemented with synchronised captions and 3D avatars improved comprehension by 85 per cent among deaf students once we applied these visual layout rules.
鈥婤uild on high-quality linguistic data
鈥婭苍 AI-enabled classrooms, inclusion is only as good as the data behind it. Generic translation tools often fail to capture the dialectal nuances or synonyms essential for academic clarity.
- 鈥Look for nuanced datasets: my published in ScienceDirect introduced the Arabic Yemeni sign language (ArYSL) version 2 dataset, featuring 35,900 labelled images. Crucially, it includes a dictionary of 357 words that account for synonyms and regional variations.
- 鈥Prioritise accuracy over speed: do not rely solely on any automated tool. Use platforms that respect linguistic nuances so a translation remains accurate even when a student uses a different regional sign for a technical concept.
鈥婨nsure platforms meet 鈥榠ndependent use鈥 standards
鈥婣ccessibility isn鈥檛 just about viewing; it鈥檚 about navigation. Students must be able to interact with your learning management system (LMS) independently.
- 鈥Check your navigation: verify colour contrast and keyboard shortcuts. Prioritise learning tools interoperability (LTI) standards so that accessibility tools stay 鈥渁lways on鈥 within the course dashboard rather than hidden behind external links.
- 鈥Edit automated captions: treat AI-generated captions as a draft. Review recordings to correct technical jargon; accurate punctuation provides the mental 鈥渂reathing room鈥 required to parse complex ideas.
Evidence of impact: platforms optimised for comprehensive accessibility at my university saw engagement rise to 95 per cent, compared with 70 per cent on non-optimised versions.
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- Effective support means meeting university students where they are
鈥婩oster 鈥榚xpressive symmetry鈥 in assessment
鈥婭苍clusion is a two-way street. If a student can receive information but cannot express themselves in their primary language, they face a 鈥渓inguistic ceiling鈥.
- 鈥Enable bidirectional communication: research I published in the Institute of Electrical and Electronics Engineers鈥 (IEEE) Access journal details a real-time bidirectional system. We used the YOLOv8n-cls model (a lightweight, high-speed image classification model optimised for real-time performance) to achieve 99.9 per cent accuracy in converting sign gestures to text. To facilitate the reverse text-to-sign process, we integrated a fuzzy string-matching tool to map written Arabic input against an extensive data dictionary. This technique identifies the closest linguistic match even when input is imprecise, ensuring the system reliably retrieves the correct sign images despite spelling typos or variations. By resolving the 鈥渓inguistic ceiling鈥 caused by input errors, this system allows students to contribute naturally and fluidly.
- 鈥婳ffer flexible assessment: offer alternatives to oral-only exams. Allow students to submit captioned video responses or sign-language explanations. Implementing these methods at our institution increased average exam scores from 68 per cent to 75 per cent.
鈥婽rain faculty for long-term sustainability
鈥 Educators must know how to implement these tools effectively.
- 鈥Provide workshops: supply faculty with simple checklists for preparing inclusive multimedia. After targeted training at my institution, 90 per cent of instructors successfully adapted their strategies.
- 鈥Avoid the 鈥渁fterthought鈥 trap: involve D/HH students in testing your digital tools early.
鈥婾se sign language technology with nuance
鈥婣I-powered avatars are developing quickly, but they require professional oversight. Sign language relies heavily on 鈥渘on-manual markers鈥 鈥 facial expressions and body shifts 鈥 that function as grammar.
- 鈥Use avatars for static content: these allow for 24/7 access to resources such as safety briefings or syllabus walkthroughs.
- 鈥Retain humans for complex debates: complex discussions require the expressive nuance that only a human interpreter can master. While AI can bridge the word-gap, maintaining grammatical context still requires human oversight.
鈥婦esign for diversity from the start
鈥婽rue inclusion begins with a design decision, not an 鈥渆nable captions鈥 button. By prioritising visual clarity, using high-quality datasets and fostering expressive symmetry, we move from mere accommodation to true empowerment. Digital transformation offers a clear roadmap for universities to foster equitable environments where deaf and hard-of-hearing students do not simply cope; they thrive.
AI disclosure: this article was developed by the author with AI assistance for structural editing and alignment with Times Higher Education Campus guidelines. All technical insights, metrics and strategies reflect the author鈥檚 professional expertise and peer-reviewed research.
Mogeeb A. A. Mosleh is a professor of artificial intelligence at Taiz University, Yemen.
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