We all remember the warning from math class: <\/p>\n\n\n\n
\n\u201cYou won\u2019t always have a calculator in your pocket!\u201d<\/p>\n<\/blockquote>\n\n\n\n
How we laugh now, with calculators first arriving in our pockets and, eventually, smartphones putting one in our hands at all times.<\/p>\n\n\n\n
I have seen a lot of comparisons 1<\/a><\/sup> 2<\/a><\/sup> 3<\/a><\/sup> across the Internet to artificial intelligence (AI) and these mathematics classes of yesteryear. The idea being that AI is but the newest embodiment of this same concern, which ended up being overblown.<\/p>\n\n\n\n
But is this an apt comparison to make? After all, we did not replace math lessons and teachers with pocket calculators, nor even with smart phones. The kindergarten student is not simply given a Casio and told to figure it out. The quote we all remember has a deeper meaning, hidden among the exacerbated response to the question so often asked by students: “Why are we learning this?”<\/p>\n\n\n\n
The response<\/h3>\n\n\n\n
It was never about the calculator itself, but about knowing how<\/strong>, when<\/strong>, and why<\/strong> to use it. A calculator speeds up the arithmetic, but the core cognitive process remains the same. The key distinction is between pressing the = button<\/strong> and understanding the result of the = button<\/strong>. A student who can set up the equation, interpret the answer, and explain the steps behind the screen will retain the mathematical insight long after the device is switched off.<\/p>\n\n\n\n
The new situation – Enter AI<\/h2>\n\n\n\n
Scenario<\/h3>\n\n\n\n
Pressed for time and juggling multiple commitments, a student turns to an AI tool to help finish an essay they might otherwise have written on their own. The result is a polished, well-structured piece that earns them a strong grade. On the surface, it looks like a success, but because the heavy lifting was outsourced, the student misses out on the deeper process of grappling with ideas, making connections, and building understanding.<\/em><\/p>\n\n\n\n
This kind of situation highlights a broader concern: while AI can provide short-term relief for students under pressure, it also risks creating long-term gaps in learning. The issue is not simply that these tools exist, but that uncritical use of them can still produce passing grades without the student engaging in meaningful reflection gained by prior cohorts. Additionally, when AI-generated content contains inaccuracies or outright hallucinations, a student\u2019s grade can suffer, revealing the importance of reviewing and verifying the material themselves. This rapid, widespread uptake stresses the need to move beyond use alone and toward cultivating the critical habits that ensure AI supports, rather than supplants, genuine learning. <\/p>\n\n\n\n
Some background studies<\/h2>\n\n\n\n
In a 2024 study on Generative AI Usage and Exam Performance<\/a>, Wecks et al. (2024) describe that:<\/p>\n\n\n\n
\nEmploying multivariate regression analysis, we find that students using GenAI tools score on average 6.71 (out of 100) points lower than non-users. While GenAI may offer benefits for learning and engagement, the way students actually use it correlates with diminished exam outcomes<\/p>\n<\/blockquote>\n\n\n\n
Another study (Ju, 2023) found that:<\/p>\n\n\n\n
\nAfter adjusting for background knowledge and demographic factors, complete reliance on AI for writing tasks led to a 25.1% reduction in accuracy. In contrast, AI-assisted reading resulted in a 12% decline. Ju (2023).<\/p>\n<\/blockquote>\n\n\n\n
In this same study, Ju (2023) noted that while using AI to summarize texts improved both quality and output of comprehension, those who had a ‘robust background in the reading topic and superior reading\/writing skills’ benefited the most.<\/p>\n\n\n\n
Ironically, the students who would benefit most from critical reflection on AI use are often the ones using it most heavily, demonstrating the importance of embedding AI literacy into the curriculum. For example: A recent article by Heidi Mitchell from the Wall Street Journal (Mitchell, 2025) cites a study showing that the “less you know about AI, the more you are likely to use it”, and describing AI as seemingly “magical to those with low AI literacy”. <\/p>\n\n\n\n
Finally, Kosmyna et al. (2025), testing how LLM usage affects cognitive processes and neural engagement in essay writing, assembled groups of LLM users, search engine users, and those without these tools (dubbed “brain-only” users). The authors recorded weaker performance in students with AI assistance over time, a lower sense of ownership of work with inability to recall work, and even seemingly reduced neural connectivity in LLM users compared to the brain-only group, which scored better in all of the above.<\/p>\n\n\n\n
The takeaways from these studies are that unstructured AI use acts as a shortcut that erodes retention. While AI-assistance can be beneficial, outright replacement of thinking with it is harmful. In other words, AI amplifies existing competence but rarely builds it from scratch.<\/p>\n\n\n\n
Undetected<\/h2>\n\n\n\n
Many people believe themselves to be fully capable of detecting AI-usage:<\/p>\n\n\n\n
\nMost of the writing professors I spoke to told me that it\u2019s abundantly clear when their students use AI. Sometimes there\u2019s a smoothness to the language, a flattened syntax; other times, it\u2019s clumsy and mechanical. The arguments are too evenhanded \u2014 counterpoints tend to be presented just as rigorously as the paper\u2019s central thesis. Words like multifaceted and context pop up more than they might normally. On occasion, the evidence is more obvious, as when last year a teacher reported reading a paper that opened with \u201cAs an AI, I have been programmed \u2026\u201d Usually, though, the evidence is more subtle, which makes nailing an AI plagiarist harder than identifying the deed. (Walsh, 2025).<\/p>\n<\/blockquote>\n\n\n\n
In the same NY Mag article, however, Walsh (2025) cites another study, showing that it might not be as clear who is using AI and who is not (emphasis added):<\/p>\n\n\n\n
\n[…] while professors may think they are good at detecting AI-generated writing, studies have found they\u2019re actually not. One, published in June 2024, used fake student profiles to slip 100 percent AI-generated work into professors\u2019 grading piles at a U.K. university. The professors failed to flag 97 percent<\/strong>.<\/p>\n<\/blockquote>\n\n\n\n
The two quotes are not contradictory; they describe different layers of the same phenomenon. Teachers feel they can spot AI because memorable extremes stick in their minds, yet systematic testing proves that intuition alone misses the overwhelming majority of AI\u2011generated work. This should not be surprising though, as most faculty have never been taught systematic ways to audit AI\u2011generated text (e.g., checking provenance metadata, probing for factual inconsistencies, or using stylometric analysis). Nor do most people, let alone faculty grading hundreds of papers per week, have the time to audit every student. Without a shared, college-wide rubric of sorts, detection remains an ad\u2011hoc, intuition\u2011driven activity. Faulty detection risks causing undue stress to students, and can foster a climate of mistrust by assuming that AI use is constant or inherently dishonest rather than an occasional tool in the learning process. Even with a rubric, instructors must weigh practical caveats: large-enrollment courses cannot sustain intensive auditing, some students may resist AI-required tasks, and disparities in access to tools raise equity concerns. For such approaches to work, they must be lightweight, flexible, and clearly framed as supporting learning rather than policing it.<\/p>\n\n\n\n
This nuance is especially important when considering how widespread AI adoption has been. Walsh (2025) observed that \u201cjust two months after OpenAI launched ChatGPT, a survey of 1,000 college students found that nearly 90 percent of them had used the chatbot to help with homework assignments.\u201d While this figure might seem to justify the use of AI detectors, it could simply reflect the novelty of the tool at the time rather than widespread intent to circumvent learning. In other words, high usage does not automatically equal cheating, showing the importance of measured, thoughtful approaches to AI in education rather than reactionary ones.<\/p>\n\n\n\n
What to do…?<\/h2>\n\n\n\n
The main issue here is not that AI is magically writing better essays than humans can muster, it is that students are slipping past the very moments where they would normally grapple with concepts, evaluate evidence, and argue a position. Many institutions are now taking a proactive role rather than a reactive one, and I want to offer such a suggestion going forward.<\/p>\n\n\n\n
Embracing the situation: The reflective AI honor log<\/h3>\n\n\n\n
It is a fact that large language models have become ubiquitous. They are embedded in web browsers<\/a>, word processors<\/a>, and even mobile keyboards<\/a>. Trying to ban them outright creates a cat\u2011and\u2011mouse game; it also sends the message that the classroom is out of sync with the outside world.<\/p>\n\n\n\n
Instead of fighting against a technology that is already embedded in our lives, invite students to declare<\/strong> when they use it and to reflect<\/strong> on what they learned from that interaction.<\/p>\n\n\n\n
For this post, I am recommending using an “AI Honor-Log Document”, and deeply embedding it into courses, with the goal of increasing AI literacy. <\/p>\n\n\n\n
What is it?<\/h3>\n\n\n\n
As assignments vary across departments and even within courses, a one-size-fits-all approach is unlikely to be effective. To support thoughtful AI use without creating extra work for students, faculty could select an approach that best aligns with their course design:<\/p>\n\n\n\n
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- Built-in reflection:<\/strong> Students note when and how they used AI, paired with brief reflections integrated into their normal workflow.<\/li>\n\n\n\n
- Optional, just-in-time logging:<\/strong> Students quickly log AI use and jot a short note only when it feels helpful, requiring minimal time.<\/li>\n\n\n\n
- Embedded in assignments:<\/strong> Reflection is incorporated directly into the work, so students engage with it as part of the regular writing or research process.<\/li>\n\n\n\n
- Low-effort annotations:<\/strong> Students add brief notes alongside tasks they are already completing, making reflection simple and natural.<\/li>\n<\/ol>\n\n\n\n
These options aim to cultivate critical thinking around AI without imposing additional burdens or creating the perception of punishment, particularly for students who may not be using AI at all.<\/p>\n\n\n\n
AI literacy is a massive topic, so let’s only address a few things here: <\/p>\n\n\n\n
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- Mechanics Awareness<\/strong>: Ability to explain the model architecture, training data, limits, and known biases.<\/li>\n\n\n\n
- Critical Evaluation<\/strong>: Requiring fact-checking, citation retrieval, and bias spotting.<\/li>\n\n\n\n
- Orchestration Skills<\/strong>: Understanding how to craft precise prompts, edit outputs, and add original analysis.<\/li>\n<\/ul>\n\n\n\n
Note<\/strong>: you might want to go further and incorporate these into an assignment level learning outcome. Something like: “Identifies at least two potential biases in AI-generated text” could be enough on a rubric to gather interesting student responses.<\/p>\n\n\n\n
Log layout example<\/h3>\n\n\n\n