Higher education and society at large are grappling with a curious new species: the “AI idiot (Arner, 2024).” Don’t worry—no need to call campus security! These are perfectly normal folks who know how to push all the right buttons on AI tools but seem to have left their critical thinking back at that coffee shop with free WiFi.
What’s happening? Well, many now treat AI like an all-knowing oracle: toss in a question, get an answer, call it a day—no need for pesky things like judgment, structure, or checking if what came out actually makes sense. In the most spectacular cases, some “AI experts” couldn’t tell you what their generated content means even if you bribed them with a semester’s supply of coffee. Yes! There is actual research showing that both AI users and experts frequently fail to understand or critically evaluate AI-generated outputs (Quantum Zeitgeist, 2025).
Let’s face it: we’ve always loved shortcuts. From calculators to spellcheck, our desire for “get it done quick” is as human as the desire to avoid Monday morning meetings especially the ones that start 8:00 am. Today’s AI makes it dangerously easy—just enough knowledge and logic to squeak by, but not enough to build real skills. For many, the temptation to skip the hard work and outsource thinking is real. But beware: those who take the shortcut path risk turning into walking, talking button-pushers—while education’s true mission (developing a sharp, questioning mind) gets left in the waste basket.
Next time you use an AI tool, take a moment to question its answers, think things through, and check how it works. Doing this will keep your mind sharp—and help you avoid becoming the subject of an “AI idiot” story.
In the meantime – because I am a sucker for “next steps” and “actionable items,” here are four key tips—with caveats and best practices—to help data, business, and other higher ed analysts maintain strong critical thinking and avoid becoming "AI idiots":
1. Question Assumptions and Evaluate Outputs
· Never accept AI-generated results at face value. Always ask: “What are the underlying assumptions? Are the data sources and logic sound?” Challenge the reliability, completeness, and possible biases of both data and model outputs.
· Caveat: Even widely-used AI models like Chat GPT or Gemini can contain errors or biased patterns. Automated results should be considered starting points, not final answers.
· Best Practice: Routinely cross-check AI outputs against alternative sources, domain expertise, and logic. Try to locate the right AI tool for the job. For instance, I prefer Julius AI over ChatGPT for anything data-related.
2. Prioritize Human Oversight and Expert Validation
· Integrate domain expertise throughout the analysis process. Validate findings with colleagues or subject matter experts, especially where context or nuance matters. Post a question to the discussion forums on the HEUG or email someone specific you know to be an expert before you turn to a generic AI tool.
· Caveat: Blindly trusting automation can lead to costly mistakes—AI lacks context and judgment in unfamiliar scenarios. I would email @Paula McDaniel or @Daniel Labrecque if I ever needed advice on how to tackle a particularly tricky query expression.
· Best Practice: Foster feedback loops and peer review for all key analytical decisions.
3. Understand AI’s Capabilities and Limitations
· Educate yourself on how your AI tools work—their strengths, weaknesses, and appropriate use cases. Know what AI can't do (such as interpret latent context, spot rare anomalies, or apply non-explicit business logic).
· Caveat: Overestimating AI’s abilities or misunderstanding technical boundaries can result in poor decision-making.
· Best Practice: Attend regular training sessions and seek out transparent documentation/explanations of your platform or model. Consider creating an AI tool inventory for yourself as a quick way to decide which tool makes sense for which task.
4. Stay Curious—Ask Questions, Spot Patterns, and Scrutinize Data
· Develop a habit of asking, “What does this data tell me? Are trends consistent, or are there anomalies?” Be skeptical—use statistical techniques and intuition when analyzing results.
· Caveat: Overreliance on surface-level insights without deeper investigation can mask important issues or opportunities.
· Best Practice: Explore data from multiple angles, test different hypotheses, and don’t ignore outliers. And don’t forget to use the tools you know and trust. Excel is your friend and hardly ever fails you!
And don’t’ forget what Michael once told Dwight in that fabulous episode from the hit series The Office, “Don’t be and Idiot.” It might change your life.
References
Arner, D. (2024, June 28). The rise of AI idiots: Losing critical judgment amid increasing cyber threats [Video]. Looking Back Looking Forward. https://www.youtube.com/watch?v=cPttP0ErSrk
Buu [Data Analytics Talks]. (2025, March 24). Power of critical thinking in data analytics [Video]. YouTube. https://www.youtube.com/watch?v=MLygBXUm_hE
LinkedIn. (2024, June 10). How do you develop critical thinking skills for data analysis? [Post]. LinkedIn. https://www.linkedin.com/advice/3/how-do-you-develop-critical-thinking-skills-data
Quantum Zeitgeist. (2025, August 12). Users frequently fail to detect errors in AI-generated marketing data analyses. Retrieved from https://quantumzeitgeist.com/users-frequently-fail-to-detect-errors-in-ai-generated-marketing-data-analyses/