AI Literacy at CMU Libraries

Generative AI is the newest information environment your students and colleagues are working in, and helping the campus navigate it is central to what the Libraries do.

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AI Literacy is the Next Chapter of Information Literacy

For decades, librarians have helped the CMU community find information, judge its credibility, recognize bias, use it ethically, and cite it honestly. Generative AI raises the same questions, often with higher stakes. A chatbot that fabricates a citation, reproduces a bias from within its training data, or quietly ingests your unpublished work, poses a familiar information-literacy challenge in a new and faster-moving form.

That's why we see AI literacy as core to our mission. We bring the same critical, evidence-based habits of mind we've always applied to information to tools that can write, summarize, code, and synthesize on demand. Our stance is practical and even-handed: we help you and your students use these tools well, with a clear sense of what they do reliably, where they fall short, and what's at stake when you rely on them.

What AI Literacy Means

AI literacy is a set of competencies that let people evaluate AI technologies critically, communicate and collaborate with them, and use them effectively and ethically. In practice, it's what lets you and your students intentionally put these tools to work (or choose not to).

How the Libraries Can Help

Our librarians and specialists offer support across the range of academic AI use:

Choosing and assessing AI tools. Matching the right tool to the task and evaluating its accuracy, reliability, and limits before you trust it.
AI for evidence synthesis. Using AI responsibly within literature reviews and systematic reviews, where rigor and reproducibility matter most.
AI for research communication. Using AI to build audience personas; summarizing and refining scholarly writing for maximum impact while keeping your voice and standards intact.
AI for coding. Using AI assistants to write, debug, and translate code in languages such as Python and R.
Understanding publisher AI policies. Navigating what journals and presses now permit, require, or prohibit in submitted work.
Generative AI, open access, and copyright. Making sense of authorship, ownership, licensing, and reuse while the law is still catching up.
Recognizing bias, hallucinations, and misinformation. Spotting fabricated sources and skewed outputs, and teaching your students to do the same.
Prompt and context engineering. Designing clear, structured prompts that produce relevant and useful results, using techniques like chain-of-thought reasoning and few-shot prompts.
Data privacy and ethics. Protecting confidential, proprietary, and personal data when AI tools are involved.

The People Behind It

AI literacy at CMU is led by librarians who are also researchers, with advanced degrees in fields from physics to archaeology to engineering. Whatever your discipline, there's someone here who understands it. A few of the people you might work with:

Alfredo González-Espinoza, Research Data Librarian. A physicist by training, with a PhD in physics and complex systems, who supports data management and analysis across the disciplines. He wrote the Libraries' guides to working effectively with large language models and making research data AI-ready.

Haoyong Lan, STEM Librarian. With an EdD in Engineering Education, Haoyong’s work spans prompt engineering, conversational AI, and information retrieval. He designed and co-built ScottAI, the Libraries' AI reference assistant. He also teaches credit-bearing generative AI literacy courses in evidence synthesis.

Jennifer McKee, Business & Entrepreneurship Librarian. Merging a humanities background with an MBA, her interdisciplinary work in this space focuses around ethical implementation and usage of AI tools, alongside how AI can enhance design thinking and research processes - whether for a startup or a research project.

Kristen Scotti, STEM Librarian. A materials science PhD. She created the Libraries' "for All" coding workshops, which use AI chatbots as an on-demand tutor to lower the barrier to learning Python and R while teaching participants to catch and correct the errors AI introduces.

Emma Slayton, Data Education Librarian. Brings a social scientist's perspective, with a PhD in archaeology, to data and AI literacies. She leads workshops on using AI responsibly, weighing where it adds value, how data shapes its outputs, and where the real risks lie, and she helps researchers turn their data into work that's ready to publish and share.

Sarah Young, Social Sciences Librarian. Directs the Libraries' Evidence Synthesis Program and focuses on using AI in systematic reviews in ways that hold to the rigor and transparency the method depends on. She provides methodological expertise for evidence syntheses across disciplines and co-convenes the Campbell Collaboration's Information Retrieval Methods Group.

Huajin Wang, STEM Librarian. With a PhD in cell biology, Huajin works at the intersection of AI and open science, helping research communities make their work more open, reproducible, and reusable as AI changes how science is done.

Nicky Agate, Associate Dean for Academic Engagement. A humanities PhD, she works on AI in research communication, helping scholars build audience personas to help translate their work and maximize its impact. She is the PI on the IMLS-funded POEM project and a co-author of CMU's Gen AI Canvas for Learners modules.

Training Opportunities

The Libraries run hands-on workshops on AI throughout the year, open to faculty, staff, and students. These workshops cover a wide range of topics - some examples include: publishing and copyright, prompt engineering, and coding with AI. For current dates and registration, see the CMU Libraries Workshops calendar. We're also glad to design a curated session for your class, lab, or department upon request.

Examples of past training opportunities include:

Resources and Events

Published Resources

Artificial Intelligence Research guide. Definitions of key AI concepts, with links to CMU research tools, datasets, and scholarship.
Best Practices for Large Language Models. A curated set of examples for getting reliable results from AI chatbots.
Generative AI guide. Prompt engineering techniques, ethical considerations, citation practices, and CMU-specific guidance for teaching, learning, and research.
Making Your Research Data AI-Ready. Curated materials and guidance on how to ensure your data is optimized for machine learning environments.
Text and Data Mining guide. Resources and support for text and data mining projects.
POEM, Project on Open and Evolving Metaliteracies (launching late 2026). An open educational resource that extends information literacy into AI, media, and data literacy.

Events

Workshops. The Libraries run hands-on workshops on AI throughout the year, open to faculty, staff, and students. These workshops cover a wide range of topics - some examples include: publishing and copyright, prompt engineering, and coding with AI. For current dates and registration, see the CMU Libraries Workshops calendar. We're also glad to design a curated session for your class, lab, or department upon request.
AI Literacy Teaching Resource Hackathon. In Fall 2026, the Libraries are running two editions of our AI Literacy Teaching Resource Hackathon, during which participants co-design and build open educational resources that teach AI literacy. Teams work across three dimensions: technical literacy (how AI works), practical literacy (how to use it well), and ethical literacy (its societal implications and potential for misuse). The resources they create are released openly for anyone to use and adapt.
- For CMU faculty. A campus edition centered on creating materials that help our students get to where they need to be in understanding AI and how to use it. October 13, 2026, in the IDeATe studios at Hunt Library, with space for 30 participants.
- For librarians everywhere. A two-day edition on October 21 and 22, 2026, open to library professionals from any institution, reflecting the Libraries' role in shaping AI literacy practice across the field.

Tools and Frameworks

Tool Assessment Framework. A librarian-developed framework for deciding whether an AI tool fits your research, weighing functionality, data sources, output quality, transparency, and ethics. The Libraries also maintain a regularly updated list of vetted, AI-enhanced tools they subscribe to.
Open-source tools on GitHub. Including an AI sustainability dashboard that estimates the environmental impact of AI research tools, and an assessment-framework tool for AI-powered academic search engines.
ScottAI. The Libraries’ conversational AI reference agent. Users can query library collections, research profiles, instructional videos, and open-access publications at CMU—all from ScottAI’s interface.

Programs

AI in Research (AIR) Program. The Libraries' hub for using AI in research responsibly, offering tool assessment, training, one-on-one consultations, and a campus community of practice.

Work with Us

Request a workshop for your course, lab, or department.
Book a consultation with a librarian to talk through AI in your research, teaching, or publishing.
Using AI in your own research? Faculty can also work with the AI in Research (AIR) Program, which offers tool assessment, training, consultations, and a community of practice focused on the research process.
Bring us a hard question, whether it's about a publisher's new AI policy, a tool you're not sure you can trust, or how to teach students to use AI with integrity.

Contact your Librarian | Request an Instruction Session | Browse All Workshops