| University Libraries
This page provides strategies and tools for conducting AI research through George Mason University Libraries, emphasizing interdisciplinary approaches, effective search techniques, and vetted resources.
If you are looking for information regarding generative artificial intelligence (GenAI) such as ChatGPT or Copilot, see our Generative AI Research Guide.
If you are looking for support for using AI tools in research such as data mining, see the Data and Digital Scholarship Research Guide.
There are a variety of George Mason Libraries experts you can assist you with AI research.
Mason Libraries subscribes to IT and computer science databases like IEEE Xplore and ACM Digital Library which focus on AI by providing technical research papers, conference proceedings, and industry standards covering machine learning, robotics, and algorithms. They also include topics like AI ethics, human-computer interaction, and real-world applications.
Contact Dr. Heidi Blackburn or your subject librarian for assistance locating additional resources.
Mason Libraries subscribes to interdisciplinary database such as JSTOR and ABI/INFORM that place AI at the intersection of such topics as government, policy, education, healthcare, sciences, social justice, and many others.
Contact Dr. Heidi Blackburn or your subject librarian for assistance locating additional resources.
JSTOR is a collection of core scholarly journals in the humanities, social sciences, and sciences digitized in most cases back to the first date of issue. Includes selected books, primary sources and other materials for academic work.
Start by identifying the broad area of AI that interests you, such as technical methods, practical applications, or ethical and policy issues. Then develop focused, actionable questions to guide your AI research.
Brainstorming Frameworks:
PICOT Model: For experimental AI studies (e.g., "In radiology workflows (Population), how does AI-assisted image analysis (Intervention) compare to human radiologists (Comparison) in diagnostic accuracy (Outcome) over 6-month trials (Time)?").
SPICE Framework: For theoretical AI ethics (e.g., "In university admissions (Setting), how do admissions officers (Perspective) perceive algorithmic bias (Intervention) compared to traditional methods (Comparison) in fairness evaluations (Evaluation)?").
AI-Specific Pitfalls:
Scope Narrowing: Convert "How does AI impact healthcare?" to "How does AI help dentists to spot irregularities in patient digital imaging faster?"
Measurability: Require quantifiable metrics (accuracy, processing speed, cost reduction) rather than subjective claims.
Use George Mason’s specialized databases to find scholarly articles, conference papers, and reports on AI. Computer science databases will focus on technical aspects. For interdisciplinary research such as AI and government, AI and education, or AI and healthcare, we recommend checking out subject-specific databases.
Tailor your search terms to your academic field to get the most relevant results. For healthcare, try keywords like “predictive diagnostics” or “AI patient triage.” For education, use “adaptive learning systems” or “AI tutoring ethics.” Business students might search for “AI supply chain optimization” or “fraud detection algorithms.” A psychology student could search “AI mental health chatbots” AND “user trust” to find focused research.
Build your research on authoritative sources by locating key journals, books, and datasets. If you need additional help, make an appointment with a librarian for more guidance.
Some AI research topics may require looking beyond traditional articles because of how fast the technology and subsequent policies develop. Computer science researchers often rely on conference proceedings, white papers, and grey literature to produce their findings faster than traditional scholarly journals. Newspapers and magazines will have the latest information but are not considered scholarly sources. You may need to use several different kinds of sources depending on your research topic.
If you are conducting long-term research, stay current by setting up Google Scholar Alerts for emerging topics like “quantum machine learning.”
Always check the credibility of your sources before using them. Confirm that articles are peer-reviewed by using filters in databases, and/or verify author credentials to ensure they are experts in the field. Look for clear methodological details such as data and code availability, which indicate thorough research when reviewing journal articles. If you need help, ask a librarian to help you confirm the quality of the source.
Remember, you don’t have to research alone — Mason’s subject librarians are here to support you.
You may be interested in how AI intersects with different areas outside of computer science or how policies regarding AI affect different groups of people.
Use keywords to investigate cross-disciplinary impacts:
Be specific in order to narrow down what you mean by "AI" in your research:
Boolean operators save you time by helping you find exactly what you need without wading through irrelevant results. For example, using AND narrows your search to include only sources with all of your keywords, while OR broadens it to include any of your terms, so you get better, more focused results faster.
| Operator | Example | Purpose |
|---|---|---|
| AND | "machine learning" AND "climate change" | Narrows results to include both terms |
| OR | "AI" OR "artificial intelligence" | Broadens results to include either term |
| NOT | "chatbots" NOT "customer service" | Excludes irrelevant subtopics (topics you don’t want) |
| Quotes | "neural networks" | Searches exact phrases |
("generative AI" OR "large language models") AND ("academic integrity" OR "plagiarism")
Nexus: A Brief History of Information Networks from the Stone Age to AI
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Co-Intelligence: Living and working with AI
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The AI Con
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