It is likely that methods for searching databases for information will change rapidly as we increasingly rely on artificial intelligence (AI) to conduct research.
While these changes may feel dramatic, it is worth remembering that information literacy skills have always evolved alongside new technologies. Just as we transitioned from card catalogs to electronic databases, the way we find and evaluate information will continue to evolve—but the underlying principles of information literacy will remain essential.
We will still use much of the same knowledge and skills—just expressed in new ways when interacting with AI tools.
In this post, I want to share how we can update the information literacy knowledge and skills we have been using for decades to excel in the age of AI by focusing on one familiar example: Boolean operators.
Why Boolean Operators Still Matter in AI Environments
Boolean operators—AND, OR, and NOT—have long been foundational to effective database searching. They help users expand, narrow, or refine search results with precision. Understanding when to ask for more (AND), when to consider alternatives (OR), and when to exclude irrelevant information (NOT) has always been central to effective research.
Even though AI tools may not require users to type these words explicitly, the logic behind them still matters deeply. When working with AI, we can apply Boolean thinking as a mental framework for constructing better prompts. Think of this approach as setting parameters for your queries and giving the AI more context to generate focused, meaningful results or outputs.
Using AND Logic in AI Prompts
For example, using AND logic in AI prompting helps combine concepts to narrow the focus of a response:
“Explain how artificial intelligence can enhance student engagement in K–12 classrooms.”
This is the equivalent of a Boolean search connecting all three ideas (AI, student engagement, AND K-12 classrooms), helping the AI deliver information that is relevant to the intersection of those topics.
Using OR Logic to Explore Options or Comparisons
The concept of OR can guide prompts when we want to explore options or comparisons. A prompt such as:
“What are the advantages of teachers OR tutors in supporting student writing?”
This asks the AI tool to consider multiple perspectives, broadening the range of ideas provided. This can help with a query about human instructors vs. personalized learning from a computer.
Using NOT Logic to Exclude Irrelevant Directions
Finally, NOT logic can be especially helpful in keeping the AI on track and excluding distractions. For example:
“Provide information about instruction but not corporate training.”
This helps the system focus on the desired topic.
Teaching Boolean-Style Prompting to Learners
To help learners internalize these ideas, instructors can build simple, hands-on activities into workshops or classroom sessions. Ask learners to practice layering concepts using “and” to see how it deepens the focus and context of AI outputs. Have them test prompts with and without “or” phrasing to observe how the range of answers changes. Encourage them to include clear exclusions when the AI begins to overgeneralize.
Boolean logic helps users of AI keep control and intentionality in an AI environment that can feel opaque and unpredictable. By consciously applying Boolean operators conceptually, prompts become clearer and more structured, similar to database search results.
In other words, Boolean logic has not disappeared in the age of AI—it has simply evolved into a more natural, flexible way of expressing precision and purpose for researching with AI.
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