Written by Betsy Griffen, Charlotte Skolasky, Chrissy Abbott, Rebecca Rubenstein
The Sheridan Libraries’ Reserves team recently participated in the American Library Association’s Annual Reference and User Services Association (RUSA) Virtual Forum “Reference Revolution: AI + More.” During the two-day event, we were particularly intrigued by the “AI and Disability: Benefits and Potential Bias” session, which focused on the current and potential future intersections of artificial intelligence and accessibility. This session inspired us to consider how AI could enhance the accessibility of the services we provide, including the PDFs we produce and upload to the library’s eReserves system, Ares.
Ares seamlessly integrates with JHU’s course management system, Canvas, ensuring that students have easy access to their instructor-assigned readings. The Sheridan Reserves team of five dedicated staff members annually delivers over 98,000 eReserve items across more than 2,600 courses spanning six academic divisions. We collaborate with the library’s Digitization team to produce high-quality PDFs from resources. These PDFs are not only user-friendly but also undergo Optical Character Recognition (OCR) processing, making them accessible to students with disabilities. If a PDF lacks OCR processing, visually impaired students who rely on screen readers may be unable to access essential course readings.
Many of these assigned electronic readings contain images, which the Office of Student Disability Services can supply alt-text for upon request. Alt-text offers meaningful descriptions of images that visually impaired users can access through assistive technology. Traditionally, alt-text must be manually entered through software such as Adobe Acrobat. AI can now automatically generate alt-text, but the current technology has limited capabilities; Poor accuracy, hallucinations, the inability to supply context, and potential biases make it unsuitable for eReserves.
Despite the current limitations of alt-text generation, AI technology is rapidly advancing, offering many other innovations poised to benefit students with disabilities. Kamran Rasul, the Assistive Technology/Alternate Format Specialist for the Office of Student Disability Services, emphasized: “I genuinely believe that AI has the potential to improve the quality of life for individuals with disabilities through Assistive Technology.” Rasul highlighted some of the AI technology that is already providing such benefits, such as new adaptive hearing aids that can “automatically adjust audio cues settings based on the student’s current location, all with the assistance of AI.” These hearing aids utilize AI algorithms to dynamically adjust to various settings, such as quiet reading areas, group study rooms, interactive workshops, and lively events offered by the library.
AI is frequently met with apprehension in academia, but we are excited to discover what other new and unexpected technologies emerge to enhance library services. We anticipate that progress in AI technology, such as advanced alt-text generators, will foster a more equitable learning experience for all students.
Additional Resources
Al-Shamayleh, Ahmad Sami, et al. “A Comprehensive Literature Review on Image Captioning Methods and Metrics Based on Deep Learning Technique.” Multimedia Tools and Applications, 20 Feb. 2024, http://proxy.library.jhu.edu/login?url=https://doi.org/10.1007/s11042-024-18307-8.
“Assistive Technology.” Reference & User Services Association (RUSA), 20 Dec. 2021, www.ala.org/rusa/assistive-technology.
“Blindness and Low Vision.” Reference & User Services Association (RUSA), 20 Dec. 2021, www.ala.org/rusa/blindness-and-low-vision.
Cimons, Marlene. “Kathy Cahill Spreads the Word about Digital Accessibility.” The Hub, 25 Jan. 2024, https://hub.jhu.edu/at-work/2024/01/25/who-does-that-kathy-cahill/
“Documents by Topic: Standards, Guidelines, and Recommendations.” Reference & User Services Association (RUSA), 23 Feb. 2024, www.ala.org/rusa/documents-topic-standards-guidelines-and-recommendations#AccessibilityAssembly.
Miller, Meg, and Ilaria Parogni. “The Hidden Image Descriptions Making the Internet Accessible.” The New York Times, The New York Times, 18 Feb. 2022, www.nytimes.com/interactive/2022/02/18/arts/alt-text-images-descriptions.html. [JHU affiliates can sign up for a free NYT membership here]
Rentz, Aleyna. “Nathan R. Stenberg Works to Make JHU More Equitable for Disabled People.” The Hub, 25 Jan. 2024, hub.jhu.edu/2024/01/25/johns-hopkins-disability-culture-inclusion-director-nathan-stenberg/
Sarhan, Habiba, and Simon Hegelich. “Understanding and Evaluating Harms of AI-Generated Image Captions in Political Images.” Frontiers in Political Science, vol. 5, 20 Sept. 2023, http://proxy.library.jhu.edu/login?url=https://doi.org/10.3389/fpos.2023.1245684.
“Virtual Accessibility.” Reference & User Services Association (RUSA), 20 Dec. 2021, www.ala.org/rusa/virtual-accessibility.