Intern Software Engineer

ITHAKA fall 2026 community letter: AI at JSTOR. September 23, 2026 Research is changing. Its mission isn't. Dear friends and colleagues, Artificial intelligence is changing how people discover, evaluate, and engage with scholarly resources. Ithaka see evolution in research and teaching behaviors, not just the tools people use. Researchers and learners increasingly begin with questions rather than keyword searches. They expect conversational exploration, faster orientation to unfamiliar topics, and support as they move between research environments. These changes affect not only where research begins but how people learn to ask questions, evaluate sources, build understanding, and develop original ideas. Ithaka is studying these changes through its work at Ithaka S+R, and Ithaka is actively exploring how best to support shifting behaviors with AI at JSTOR. For decades, JSTOR has helped people discover trusted scholarship, understand scholarly context, benefit from library-supported access, and connect with original sources. As using AI becomes part of the research, teaching, and learning process, grounding the work in those principles becomes even more important. Ithaka is working to help scholarship remain discoverable, attributable, and connected to original sources while supporting the foundational habits of inquiry and source evaluation wherever students and scholars do their work. This means exploring whether carefully governed AI-supported experiences can strengthen the research process while preserving the practices it depends on. Ithaka is taking a two-pronged approach. First, Ithaka continue to invest in responsible and thoughtful use of AI to support users' work on JSTOR. Already, more than 15% of users engage with its AI-enabled research capabilities. This fall, Ithaka is releasing a dynamic new AI-assisted search experience. Ithaka will roll it out slowly across institutions to support feedback and learning. As always, on-platform AI experiences are optional and can be turned on or off by library administrators. Second, Ithaka is looking beyond its platform to other AI-enabled environments that students and scholars are using in their work. People use tools that make their work easier, more convenient, and more insightful. Strengthening research and learning in this context means ensuring that the research experience includes access to trusted, high-quality content with clear provenance that respects copyright holder rights and access entitlements. Ithaka is actively engaged with a small number of companies so Ithaka can begin to explore how best to work together, starting with a focus on academic journals. Ithaka will continue to share lessons and directional insights as its AI experiments evolve. On this point, I invite you to dive deeper into its thinking and work by reviewing two recent publications: AI is changing research behaviors: How JSTOR preserves thinking and learning by JSTOR product leaders Ashank Rai and Beth LaPensee and Postsecondary Value in the Age of AI by Ithaka S+R managing director Martin Kurzweil. Please be in touch - whether you'd like to ask a question, share an idea, or explore opportunities to collaborate. Feel free to reach out to me or your day-to-day ITHAKA contacts. Sincerely, Kevin Guthrie

AWS Public Sector Blog highlights PDFix's role in ITHAKA's on-demand PDF accessibility pipeline. Sep 01, 2026, 10:00 ET AWS Public Sector Blog highlights how PDFix structural tagging helped ITHAKA achieve a reported 98% accessibility check pass rate while reducing remediation costs by more than 97% FREDERICK, Md., Sept. 1, 2026 /PRNewswire/ - PDFix-US today highlighted a new AWS Public Sector Blog article detailing how ITHAKA, the nonprofit organization behind JSTOR, built an on-demand PDF accessibility remediation pipeline on Amazon Web Services (AWS) using PDFix for automated structural tagging. ITHAKA provides researchers, students, and educators access to an extensive collection of scholarly content through platforms that include JSTOR. Its digital corpus encompasses approximately 20 million PDFs and 156 million pages, including materials dating back to 1550. Remediating a collection of this size through conventional manual methods would present a significant financial and operational challenge. According to the AWS article, manual remediation of ITHAKA's full collection could cost between $156 million and $624 million. Rather than attempting to remediate its entire archive in advance, ITHAKA worked with AWS to create an on-demand pipeline that processes documents as users request accessible versions. Each remediated PDF is then stored and made available for subsequent requests, allowing ITHAKA's accessible collection to grow organically based on actual user demand. Within the pipeline, PDFix applies structural tagging - including headings, paragraphs, lists, and tables - to individual PDF pages. Amazon Bedrock generates alternative-text descriptions for images and charts, while AWS Step Functions orchestrates the processing workflow. The completed document is reassembled and evaluated against PDF/UA accessibility requirements using validation tools that include veraPDF. Early Results Reported by AWS * A 98% accessibility check pass rate * An estimated processing cost of $0.026 per page * A cost reduction of more than 97% compared with manual remediation * Production deployment approximately two months after the initial AWS and ITHAKA workshop | "This implementation demonstrates what becomes possible when PDF accessibility is treated as a scalable technology and workflow challenge." - David Herr, President, PDFix-US | ITHAKA combined PDFix with AWS services, artificial intelligence, validation tools, and appropriate human oversight to create a practical solution for an extraordinarily large and diverse document collection. It is an excellent example of how organizations can move beyond an exclusively manual approach and begin addressing accessibility at scale. The architecture also incorporates a human-review fallback for documents that do not satisfy the required accessibility checks through automated processing. This combination of automation, validation, and human intervention allows ITHAKA to direct specialized remediation resources to documents that require them while processing many other documents efficiently. Because the pipeline is modular, ITHAKA was able to adopt PDFix and veraPDF in place of the structural-tagging components used in the original open-source solution without rebuilding the complete architecture. The same modular approach will allow ITHAKA to evaluate and integrate new technologies as PDF accessibility and generative AI capabilities continue to evolve. The AWS article presents the ITHAKA implementation as a replicable model for universities, libraries, government agencies, nonprofits, and other organizations managing large collections of inaccessible or untagged PDFs. It also demonstrates how PDFix can serve as the structural-tagging engine within cloud-based, enterprise, and on-premises document workflows. "Organizations do not have to choose between an unsustainable manual-remediation expense and leaving large document collections inaccessible," Herr added. "PDFix gives developers and organizations the technology to build accessibility directly into scalable document-processing workflows while retaining validation and human review where they are needed." To learn how PDFix can support automated PDF tagging, batch processing, and integrated accessibility workflows, visit PDFix.net or contact PDFix-US to request a demonstration. About PDFix-US PDFix-US LLC is the exclusive U.S. distributor of PDFix software solutions for automated PDF accessibility, remediation, validation, and document-processing workflows. PDFix technology includes desktop applications for accessibility professionals and enterprise users, as well as developer tools that enable organizations to integrate PDF accessibility into cloud, server, desktop, and automated production environments. PDFix solutions help government agencies, educational institutions, corporations, nonprofits, and technology providers address large document collections through scalable automation combined with human verification. David Herr, President PDFix-US LLC Trademark notice: Amazon Web Services, AWS, Amazon Bedrock, AWS Lambda, AWS Step Functions, AWS Fargate, Amazon EventBridge, and Amazon S3 are trademarks of Amazon.com, Inc. or its affiliates. JSTOR is a digital platform of ITHAKA. PDFix-US is not representing that AWS or ITHAKA endorses PDFix-US or this announcement. Media Contact David Herr, President PDFix-US LLC SOURCE PDFix-US LLC