If you’ve ever asked ChatGPT to draft a lesson plan or summarize an article, congratulations—you’re officially part of the growing population of educators interacting with AI. But beyond those flashy tools lies a deeper, rapidly evolving body of research that’s reshaping how we think about learning, teaching, and even cognition itself. The question for us, as faculty, is this: How do we keep up—and what should we be paying attention to?

You don’t need a PhD in computer science to engage meaningfully with AI research. But we do need to understand enough to guide our students, influence policy, and design effective learning experiences in a tech-saturated world.

The Research Is Moving Fast—but So Are the Questions

AI in education isn’t just about automation or efficiency. The research is increasingly focused on big-picture questions:

  • How does AI change the way students learn?
  • What biases do AI systems reinforce or challenge?
  • Can AI support more personalized, equitable education—or will it widen existing gaps?
  • What does academic integrity look like when generative AI can do the writing?

Studies from organizations like UNESCO, the Brookings Institution, and major academic publishers are beginning to offer answers. One key theme: AI’s impact depends less on the technology itself and more on how we use it.

What Faculty Should Be Watching

Here are a few areas of AI research that I’ve found most relevant to my own teaching (and worth exploring across disciplines):

  1. AI and Learning PersonalizationAI tools can adapt to individual student needs in real time—at least in theory. Research on intelligent tutoring systems shows promise, especially for foundational skills in math and reading. But studies also caution that “adaptive” doesn’t always mean “effective.” Are students learning more—or just clicking through faster?

    Try this: Experiment with AI-driven feedback tools in low-stakes assignments. Then gather student input. Did it help? Did it confuse them? Invite students into the evaluation process.

  2. Bias in AI Systems
    One major thread in the research: AI systems replicate the biases in their training data. That includes racial, gender, linguistic, and socioeconomic biases—issues with real consequences in education. As educators, we need to understand these risks, especially if we’re using AI tools in grading, admissions, or student support.

    Try this: Have students use an AI tool to generate responses to prompts involving different perspectives or social issues. Then lead a class discussion on what the AI got right—and what it missed.

  3. AI and Assessment
    Can AI fairly evaluate student work? Can it help students reflect on their own thinking? Some research suggests AI might eventually support more formative, process-based assessment (like giving feedback on drafts). But other studies warn about over-reliance and potential harm to student motivation and creativity.

    Try this: Use AI to model peer feedback—but make the real assignment about critiquing that feedback. Students get exposed to multiple perspectives and sharpen their evaluation skills.

  4.  Ethical Use and Digital Literacy
    Studies consistently show that students need more support in understanding how AI works and when (or whether) to use it. That means building AI literacy into the curriculum—not just as a side topic, but integrated into how we teach research, writing, and problem-solving.

    Try this: Assign a short reading on the ethics of AI in your field and connect it to a current event. Let students discuss not only the tech—but the social and ethical implications.

Bringing Research into the Classroom—Without Overload

You don’t need to become an AI researcher yourself. But skimming relevant articles, following key education tech journals, or attending a workshop can go a long way. Even better: Talk with your librarian, instructional designer, or computer science colleagues. Ask what they’re seeing. Collaboration is key in this space.

Some helpful places to start:

  • Educause Review: AI and Higher Ed: er.educause.edu
  • Brookings Report on AI and Education: brookings.edu/
  • AI Literacy Framework (MIT Teaching Systems Lab): tsl.mit.edu

Final Thought: Don’t Just Use AI—Understand It

AI research isn’t just shaping the future of education—it’s happening because of education. As faculty, we play a vital role in ensuring that AI enhances rather than diminishes the learning process. That means asking questions, staying curious, and helping our students do the same.

And maybe, just maybe, leaning into the messy, fascinating middle ground between the machine and the mind.