Discover RDMLA’s new short course: Practical AI Skills for Librarians
February 26, 2026
The co-leaders behind the popular data management course talk about the new offering to help librarians use AI in library work.
As AI reshapes workflows at research institutions across the globe, academic librarians are exploring how to build AI literacy, both for themselves and the communities they support. A new, free Research Data Management Librarian Academy short course, “RDMLA: AI for Librarians,” leverages the experience behind the original RDMLA course to train librarians short on time in understanding and using AI in library services.
While AI literacy for librarians isn't a single skillset – it varies by role and institutional context – some foundational skills apply across roles: understanding how generative AI works, evaluating AI-generated outputs for accuracy and bias, and applying ethical frameworks to AI use in libraries.
Supporting this foundation with real-life use cases, RDMLA-AI is a globally available e-learning course hosted on the Canvas learning platform. Librarians can complete the course in just 3 hours of bite-sized segments.
RDMLA Co-leaders Elaine Martin, Director of Collaborative Initiatives, Countway Library at Harvard Medical School and Rong Tang, Professor Emerita, Simmons University School of Library and Information Science shared their experience developing the new course and how it enables academic librarians to extend their skills in the AI era.
Why a new AI course for librarians?
Rong’s current research into LIS education and professional development showed how generative AI was infiltrating library work, but often without hands-on training support for AI skills used within the library setting.
She explains, “We did an environmental scan of all the offerings that were available on the topic of AI. We found a good number of workshops and training on GenAI, but we observed that even though there are good discussions out there on using AI tools, there's also a lack of training that focuses on actual library applications and specific use cases.”
Elaine’s observations in Countway and the Harvard library system added a real-life practice perspective. “AI has been incorporated into multiple facets of library operations, including all stages of the research data lifecycle, and academic librarians play an important role in supporting the research community and providing data services to do that in their libraries.”
She offered an example: “because the NIH requires including a data management plan within its grant applications, we have faculty requesting help and librarians using ChatGPT or Copilot to help the researchers write those plans.”
Librarians’ training needs are broad. “They want to know how to create commands for ChatGPT, as well as how to search evidence-synthesis products such as Covidence,” referring to a widely-used software application for systematic reviews. She adds, “Both staff and librarians have a lot of questions about how publishers are incorporating AI tools into the resources that libraries purchase - and how to effectively use them.”
What sets this course apart from others?
A fresh approach to course design – targeted and timely
The team’s approach to creating the AI course is very different from the original RDMLA course. That 8-part data management course, launched in 2019, now encompasses 11 modules, and has been completed by over 9000 librarians from around the globe. Each module is extensive with slides, videos, readings, quizzes and more. Many do not complete all the modules.
Elaine explains that it reflects the time demands on librarians today. “We redesigned our approach in creating RDMLA-AI to make it more streamlined, video-based, and shorter. People are able to go through the videos, learn, and continue to the end.”
She noted the concern with AI’s rapid evolution as well. “We wanted it to be relevant and stand the test of at least some time. And online learning has changed – in the TikTok world, learners don't want to spend 2 hours on one module.”
Expert-led, real-world use cases
The curriculum aligns with the widely accepted ACRL AI Competencies in Libraries, which parallels frameworks available through other library associations around the globe. This course takes those principles further by showcasing working librarians who demonstrate real-world use cases and the practical application of AI skills in a library setting.
The RDMLA team found these collaborators from the community that has grown around the Research Data Management practice field. Rong explains, “we sent a Request for Proposal to recruit AI experts from working librarians.”
For instance, Unit 1, “AI tools for library research” is led by Nicole Hennig, a librarian from the University of Arizona who has offered AI training courses for librarians for a few years.
Each expert developed lessons and recorded video instruction on their own, then edited their segments after feedback from the team. Elaine adds “we also gave the instructors more autonomy in the presentation style and what they wanted to present. We've modified how we do things now and that's paid off, because the course was launched pretty quickly.”
Bite-sized units cover essential AI skills for academic librarians
The course is divided into three units containing several sections, each with a video of 10-18 minutes. They can be followed in any order, though the sequence is logical. The units cover a broad range of relevant topics:
Unit 1: AI tools for Library Research Using LLM chatbots for research tasks, using Perplexity and Elicit for literature search, and Claude and NotebookLM to study a topic
Unit 2: AI Ethics Reflects on concerns with AI tools such as bias, transparency, copyright, plagiarism, risk, and offers guidance on developing policy and cultivating a culture of integrity
Unit 3: AI Use Cases Integrating AI tools in day-to-day library work to enhance productivity in administrative tasks, reference services, research data management, and information literacy instruction
After taking the course, librarians will be able to:
Introduce core AI concepts tailored to library professionals, highlighting ethical and responsible practices.
Demonstrate AI applications through practical, real-world scenarios specific to library environments.
Showcase how AI can streamline library workflows and enhance user services.
Each unit includes transcripts, slide decks, and links to supplemental resources, so Librarians can continue their learning journey on topics most relevant to their roles and interests. Upon finishing the course, a certificate of completion is offered.
As for who can benefit from the course, Elaine pointed out that the course serves any librarian role in academic libraries. “Not every library has siloed specializations these days, often it’s a team-based approach. And some special libraries and hospital libraries are one-person do-it-all roles. The course offers something for everyone.”
What’s next for RDMLA?
Performance monitoring and expanding the curriculum
The AI course officially launched at the end of 2025, but in the first six weeks, nearly 1000 have already taken it.
It’s early to assess participant feedback overall, but they have received positive responses from a test run in another education initiative the RDMLA team developed called DSCPE (Data Services Continuing Professional Education), which is offered each fall. Elaine explains, “We incorporated one of the modules and invited the mentors of the DSCPE Capstone program to give the students AI projects.”
Rong described one of those Capstone projects: “A student helped to create an AI chatbot for automatically answering reference questions related to the library’s RDM area, which only had one librarian. If you had a question and she wasn't staffing that day, you could ask it online and the chatbot would answer the question.”
The team plans to expand the course later this year with the addition of a 4th unit: using AI for metadata and cataloging work.
Growing the community of AI experts in academic libraries
Leveraging the insights so far, the team is organizing a related webinar hosted by Friends of National Library of Medicine on the topic of “Practical AI Use Cases for Medical Librarians” in late April.
Through these integrated efforts, they hope to continue building the professional community that emerged after the launch of the RDMLA course. At that time, Elaine explains, “we felt that in addition to providing training, we could build a core group of RDM librarians and create a community of practice.”
That community has paid it forward ever since. “Because we kept involving people who have taken our classes in developing content, participating in DSCPE, or encouraging them to give poster presentations at conferences including RDAP (Research Data Access and Preservation) and MLA (Medical Library Association).”
She adds, “a trend in Libraries is having research data services integrated with AI. We want our learners to have a place where they can come together, share their experiences, learn from each other – and if we need instructors, this is where we're going.”
One broader goal of the course is overcoming what Rong observes as a thread of resistance to using AI among librarians. Their concerns - about bias in training data, transparency, deskilling, and environmental impact – are understandable. But Rong wants librarians to realize “even though you need to be mindful in using AI tools, they do increase efficiency and effectiveness of some work tasks. We would like this course to lead to wider adoption and responsible use of AI in the library.”
Meet the RDMLA Co-Leaders:
Elaine Martin joined the Countway Library as the Director and Chief Administrative Officer in 2016. Under her direction she managed a complex organization with a $11 million budget and one of the largest collections of both current medical research materials and historical and rare collections in the world, holding more than 630K vol. In January 2025, Martin retired from her administrative role and now focuses solely on growing RDMLA.
Elaine Martin
RDMLA/DSCPE Co-Leader Director of Collaborative Initiatives, Countway Library
Rong Tang is a Professor Emerita at the School of Library and Information Science, Simmons University. Her research interests center on AI competency for librarians, research data management services, open government data, paradigm shift in the field of information, usability and UX research, and information behavior. While at Simmons, Rong Tang taught primarily in areas of evaluation of information services, digital information services and providers, research methods and design, leadership and collaboration, leadership during crisis, theories of information science, and usability and user experience research. Since her retirement in July 2024, Rong Tang has been focusing on RDMLA related efforts and conducting research studies on AI competency/literacy for librarians.
Rong Tang
RDMLA/DSCPE Co-Leader | Professor Emerita, Simmons University