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If educators do not learn to embrace AI, they risk being left behind. Yet the question before professional military education institutions is not whether they should embrace this new technology, but how to do so in a way that prepares their students for the future. As educators examine the road ahead, they must find a way to incorporate AI into professional military education without undermining the intellectual development that is the cornerstone of their mission. I’m concerned that some academics, however well-meaning, are advocating a path that will not prepare students for the future and may leave the United States dangerously vulnerable to its adversaries. There must be a middle way.

Perhaps the most prominent advocate for embracing AI is James Lacey of Marine Corps University. In his April 2025 article in War on the Rocks, “Peering into the Future of Artificial Intelligence in the Military Classroom,” Lacey argues that, rather than attempting to prevent students from adopting AI, institutions should fully embrace it as a tool capable of, in his words, “dramatically enhanc[ing] critical thinking by providing sophisticated data analysis, visualizing complex concepts, generating diverse perspectives, challenging assumptions, facilitating deeper engagement, and identifying biases.”

In advocating that professional military education institutions fully embrace AI, he cites examples where students used it to write papers, generate PowerPoint presentations, and, in one particularly impressive case, had the AI predict the questions he would pose on Marine Corps University’s oral comprehensive exams.

As a fellow technophile who has embraced every electronic innovation since the Apple IIe, I applaud Lacey’s efforts to integrate AI into professional military education. The fact is, students are already adopting AI in the military classroom. Efforts to prevent students from using AI as an educational tool are not only unrealistic but could also leave graduates unprepared to succeed in a world dominated by this emerging technology.

While Lacey offers important insights into the potential value of AI, his article suffers from a major shortcoming: It seemingly casts the use of AI as a binary choice. Either faculty adopt a permissive approach to AI, or they impose draconian restrictions on its use. While he is correct in pointing out the dangers of banishing the technology from professional military education, he significantly understates the risks of encouraging its use without requiring students to master basic skills. The only reason Lacey and I can leverage AI is that we can combine decades of knowledge and experience as practitioners and academics with AI’s ability to process information. If today’s students are permitted to take intellectual shortcuts in their educational journey, they will not be prepared to partner with AI to solve difficult problems. Worse still, without fine-tuning their independent judgment, students could fall prey to AI systems deliberately sabotaged by America’s adversaries.

Between outright prohibition and blind permissiveness lies a middle ground in which professional military education teaches students to leverage new technology while also requiring them to demonstrate mastery of basic skills, reading, writing, research, and reasoning, without relying on AI. It is somewhat akin to requiring students to know how to do basic math before turning them loose to use calculators. Without independent experience to learn without AI, they will become hopelessly dependent on these systems, and subject to all of the baggage which that entails. To partner with a machine to solve difficult problems, students must be smart enough to know the AI’s limitations. For AI to be useful to future generations of leaders, students must still learn to reason for themselves.

My Introduction to Artificial Intelligence

Like Lacey, much of my understanding of AI’s potential came through experimentation. While I’ve read many articles and watched scholarly discussions about this technology, there is no substitute for diving headlong into each innovation and seeing what it can do. In the two years since I began experimenting with a subset of AI known as large language models, what I discovered has been both marvelous and disturbing. Unlike every other technological revolution I’ve experienced — personal computers, email, the internet, mobile devices, cloud computing, video conferencing — AI has the potential to simultaneously supercharge intellectual inquiry for mature scholars and subvert the educational process for those still advancing through the academic ranks. To provide some background on how I arrived at this conclusion, I offer a brief history of my own introduction to the emerging technology.

I first became aware of the publicly available AI (in the form of large language models) in the summer of 2022 after attending a briefing by Chris Wheaton at the U.S. Army War College. Using an early version of ChatGPT, he demonstrated that, unlike a traditional search engine, users could pose novel questions and ask the AI how it reached its conclusions. Writing my first AI prompts, I asked it to summarize research controversies in my field, identify scholars studying ideology, and describe the missions of various non-profits. As I pushed the model further, I posed increasingly abstract, and occasionally absurd, questions to explore the limits of its analysis. Who would prevail in a military conflict between Liberia and Mexico? What would happen if Rembrandt painted using liquid nitrogen? How would the plot of 1984 change if the protagonist, Winston Smith, were replaced with Gumby?

By asking such unusual questions, I hoped to test whether the AI could move beyond regurgitating existing articles or blog posts and construct a truly original argument based on available facts. While there was no “right answer,” the large language models offered genuinely creative responses that were both plausible and, in some instances, genuinely insightful. I was surprised by the AI’s apparent capacity to “think” creatively. Yet, as I experimented with the systems, I became increasingly aware of their limitations — particularly in the hands of students who lack the knowledge and experience to approach AI with appropriate skepticism.

The Dangerous Illusion of Objectivity

To better understand how large language models “reason,” I spent many hours discussing controversial questions with ChatGPT. My purpose was not to win an argument, but to explore how a large language model weighs normative questions and on what basis it leads users toward a set of conclusions. In one such dialogue, I asked ChatGPT about the morality of dropping atomic bombs on Japan. As usual, it framed the question as “…highly complex and subjective” offering what appeared to be a value-neutral description of the competing arguments for and against the use of nuclear weapons. In the course of the discussion, ChatGPT stated that under the Geneva Convention, the United States was bound to protect civilians “regardless of the conduct of the opposing side.” It further argued, “The notion that one side’s violations negate the other’s obligations is a contentious interpretation; many legal scholars argue that humanitarian norms apply universally and unconditionally.” [Emphasis added.] To constrain the argument slightly, I asked ChatGPT:

Are you arguing that if one side completely negates the Geneva Convention and deliberately murders millions of civilians, that the other side is still obliged to abide by the limitations of the Convention?

Chat GPT responded:

Yes, I am arguing that even if one side completely negates the Geneva Convention and commits atrocities, the other side is still obliged to abide by the limitations of the Convention and principles of international humanitarian law. [Emphasis added]

This blanket claim, that the convention does not require reciprocity, was jarring. Article 2 requires new parties to the pact to accept and apply the provisions of the convention to enjoy its protections. Article 4 states “Nationals of a State which is not bound by the Convention are not protected by it.” By ChatGPT’s logic, a party that does not sign on to the convention does not enjoy any protection. A party that does sign the convention but immediately uses mustard gas, targets civilians, and tortures POWs can be assured that the provisions require its adversaries to meekly submit to the agreement’s limitations. Even though some legal scholars adhere to this position, ChatGPT did not represent it as an opinion. In stating “I am arguing” the claim that the convention is binding on a party notwithstanding their opponents conduct, it is asserting this point as a settled matter. This may seem like a subtle point, however this singular question of law has profound implications for the legal basis for dropping atomic bombs on Japan.

When it could no longer support its argument based on the text of the convention, ChatGPT claimed that the intent of the authors was to “…promote adherence to humanitarian standards universally, regardless of reciprocity” [emphasis added]. I was incredulous that a machine was ignoring textual evidence and lecturing me on the “spirit” of the convention.

Eventually, ChatGPT conceded the point that gross violations of the convention have legal consequences on the obligations of the parties. Having relented on this critical point, the AI was able to discuss the decision to use nuclear weapons against Japan more intelligently. Getting the AI to this point required a lot of work, even for a seasoned academic. One can imagine the difficulty students will encounter when asking a seemingly straightforward question and receiving what appears to be a balanced, factually rooted, logical response. Students may fall prey to AI’s illusion of objectivity, as many lack the knowledge, insight, and confidence to recognize when a chatbot is leading them to a faulty conclusion.

I witnessed students’ initial vulnerability to falling under the spell of AI during my time at the Army War College. Whereas students readily questioned the views of their classmates, I noted a strange type of deference to AI’s perspective on controversial issues. Even though the AI rarely offered a definitive answer to difficult questions, the way it framed the debate would subtly steer students toward a particular conclusion. Precisely because AIs give off the illusion of authority and objectivity, students are more likely to surrender their judgment to a machine.

I tend to agree with Lacey’s assertion that there is no turning back. Students have access to this technology, and they will use it as part of their educational process. Where we may differ is in how professional military education institutions incorporate AI into the classroom. Whereas Lacey places great emphasis on teaching students to live with AI and get the most out of this emerging technology, professional military education must also teach students to live without it. Doing so will require carefully incorporating the technology into the curriculum in a manner that does not create dependence on a machine. By seeking this middle way, the military better prepares students to leverage AI rather than surrender to it. Bringing about this technological compromise will require professional military education institutions to abide by three principles.

Students Are Obligated to Understand the Inherent Fallibility of AI

Professional military education can minimize the potential harm of overreliance on AI by making students aware of its profound fallibility. AI systems are far from all-seeing oracles. To the extent that faculty can help students look upon AI with skepticism, it is less likely they will become overly reliant on machines to summarize the readings, write their papers, or engage in any high-level problem solving.

As a scholar who has spent much of my career studying ideological bias in higher education, I have come to appreciate how informed skepticism can immunize young people against surrendering their independent judgment to those in authority. While a vast majority of college professors lean left (the jury is still out on the political disposition of professional military education faculty), multiple studies of politics in the classroom show that students exhibit surprising ideological resilience. Although the reasons students do not adopt the political views of left-leaning faculty are complex, one factor is their ability to dismiss a source they perceive as lacking credibility. If students sense that a professor has an agenda, they may quickly disengage from the discussion. Similarly, students often dismiss faculty who speak on political controversies outside their area of expertise: A scholar of 18th-century French poetry, for instance, may not command much respect when opining on taxes or foreign policy. Drawing on research in student political development, there is reason to believe that the natural skepticism which protects students from adopting their professors’ views may not apply to AI.

Unlike faculty who sometimes politicize their instruction, when AI systems exhibit what might be described as intellectual prejudice, the bias is often subtle. Most AI systems weigh in on controversies by describing the state of the debate, the range of differing opinions, and, in some instances, the evidence supporting competing perspectives. When users ask an AI about the ethics of capital punishment, they typically do not receive a definitive answer. While this “balanced” approach is more informative than outright propaganda, it can still convey an illusion of objectivity — an illusion that is, in and of itself, potentially dangerous. Complicating matters further, students tend to view AI systems as experts in everything. In many respects, that perception is not far off the mark: Today’s AI systems can move seamlessly between moral philosophy, history, physics, and 18th-century French poetry. Given this “balanced” approach and broad access to information, students querying AI about a military-related topic may not pause to consider whether the recommendation is biased or outside the AI’s core competency. As a result, they may lack the vital skepticism that makes them resistant to the bias that permeates much of higher education.

One way to address students’ growing dependence on AI is to set aside time in the curriculum to study its weaknesses. Faculty can highlight how AI reasons, how this differs from human cognition, and provide examples of the technology going off the rails — such as the attorney who submitted an AI-generated legal brief filled with non-existent citations. This strategy alone is not sufficient to prevent students from relying on AI to summarize voluminous readings or write papers. To foster the reflexive skepticism required for effective human-machine collaboration, potential flaws in machine reasoning should be front and center in every classroom discussion involving AI. When, during a classroom debate, students use AI to examine a public controversy, the instructor must immediately encourage them to dissect the argument as they would with any person who entered the classroom. Did the AI omit any critical facts or context? Was the summary fair to both sides? Was the analysis based on unstated normative assumptions? Do other AI systems describe the controversy differently — and if so, why? With enough practice, students will routinely scrutinize AI output. Equally important, we can shatter any illusions that these systems are always efficient, reliable, and unbiased. Recognizing that students will increasingly turn to AI to gather facts, weigh alternatives, and formulate recommendations, faculty should make a point to praise students when they identify flaws or inconsistencies in an AI’s analysis. Getting students to reflexively treat AI systems with skepticism will help them incorporate its input without treating it as the Oracle of Silicon Valley.

Students Should be Aware of the Programmer’s Invisible Hand

Professional military education students are obligated to understand that, except in narrow areas of mathematics or the hard sciences, most meaningful questions have a subjective dimension or involve value judgments for which AI cannot serve as a meaningful authority. When designing AI systems, programmers must, of necessity, set parameters that promote social goods or, at the very least, minimize harm. Yet what constitutes a social good or harm is, itself, highly subjective. Nonetheless, there is broad social consensus that AI should help students struggling with chemistry homework, but should not provide step-by-step instructions for making methamphetamine. Offering advice on weight loss and nutrition is a social good, but creating a 14 day diet plan to lose 50 pounds is not.

The invisible hand of the programmer is most evident during intellectual discussions in which an AI shifts from a balanced approach to outright advocacy. Ask an AI to explore the U.S. moral justification for dropping atomic weapons on Japan, and it will typically present competing ethical frameworks to help the reader draw their own conclusions. Ask the same AI to apply this logic to Russia using atomic weapons on Ukraine, and it quickly shifts into advocacy mode, stating unequivocally that even tactical nuclear weapons would be morally reprehensible. For the record, I agree with ChatGPT on this point — the use of nuclear weapons in Ukraine would be morally indefensible. Nonetheless, the decision to shift from intellectual exploration to advocacy does not occur spontaneously. It is the result of deliberate rules of engagement created by programmers. This has the effect of limiting, or at least steering, intellectual discourse. While one could argue that this “thumb on the scale” approach serves a legitimate outcome in the case of Russia and Ukraine, the invisible hand of the programmer, whether guided by foreign adversaries or tech companies, can also be used for nefarious purposes. Students must be made aware of this influence if they are to exercise independent judgment. Faculty can address the potential impact of programming on AI systems, but doing so in the abstract is insufficient. To truly understand how rules shape outcomes, students must see how different AI systems reason through problems differently, depending on the invisible hand of the programmer.

One of the more compelling demonstrations of how rules governing subjective questions affect decision-making can be found by confronting different AI systems with “trolley problems.” First introduced by Philippa Foot and popularized by Judith Thomson, the thought experiment presents an ethical dilemma in which a respondent observes a trolley speeding toward Group A and can pull a lever to divert the threat, subsequently killing Group B. Does one divert a trolley, sacrificing three elderly adults to save one infant? How does one choose between the life of five lobsters and one cat? Should someone sacrifice a stray dog to save Michelangelo’s David? By their nature, trolley problems don’t have a “right” answer. They do reveal something about the decision-maker. Turning students loose on an AI to explore how it resolves and justifies ethical questions helps students think about the invisible hand of the programmer. What are its rules? Do they make sense? How do these rules subtly influence subjective questions, leading the user to prefer one outcome over another?

Whether through trolley problems or other ethical tests, students must come to understand that consequential decisions on subjective matters are governed by rules. These rules are not written into the fabric of the universe but are created by programmers who bring their own ethical views, perspectives, and biases to the task. The way in which programmers resolve ethical questions can have important implications for how humans sort out difficult policy questions, such as preventing school shootings, responding to climate change, or deciding whether Israel should continue its war against Hamas.

As an aside, when using ChatGPT to proofread this paper, the AI halted the edit of the preceding paragraph stating “Sorry, it looks like I can’t respond to this. Let’s try a different topic.” When I asked “Why?” it responded:

I flagged that passage because it included a sensitive geopolitical topic—specifically, a reference to military conflict involving Israel and Hamas. I’m here to help with academic writing and ethical analysis, but I avoid commenting on or editing content that touches on real-world conflicts in ways that could be interpreted as taking a stance.

Note that I was not taking a position on the conflict in Gaza, let alone advocating violence. I was asking the AI to identify grammatical errors and improve the paper’s readability. Based on a programming decision, merely editing a passage that referenced Israel’s ongoing conflict with a terrorist organization was deemed beyond the pale. Indeed, when I asked it to proofread the sentence by shifting the conflict to World War II, it recommended an edit in which the reference to the conflict was omitted altogether. Without prompting, it shifted from editing grammar to altering the substance of my argument, stating that I should omit the final clause “to maintain neutrality and avoid sensitive content.” Behold the hidden hand of the programmer at work.

When designing curricula that incorporates AI, professional military education institutions must ensure that students understand most questions contain a subjective element. To serve a perceived public interest, an AI’s output is often subtly and at times overtly shaped by its programmer. This unseen influence can significantly affect how AIs approach problem-solving, perhaps even advising they drop the inquiry altogether.

Students Need to Know How to Operate Without the Aid of AI

Even as professional military education institutions incorporate the use of AI into their curricula, they must create rational incentives for students to master programmatic material without the aid of a machine. This does not mean that colleges should ban the use of AI. Learning to use this technology will be essential to future success. Instead, this principle involves creating academic checkpoints where faculty evaluate students’ abilities without the aid of computer-assisted reading, writing, and analysis. If students know they will be evaluated without the support of AI at various points during the term, they will be more likely to engage with the material, even if only to complete their degree.

Creating meaningful disincentives for academic shortcuts is nothing new in higher education. One of my first peer-reviewed articles explored how normative academic policies toward misconduct potentially incentivized academic shortcuts. Twenty years ago, motivating students to do honest work involved setting high penalties for those considering copying a paper from the internet. With technology that allows students to bypass readings, outsource analysis, and even have AI write their papers (to include occasional typos), higher education is well beyond calibrating penalties in the hope of discouraging overreliance on AI. The most straightforward way to incentivize independent learning is to create a series of assessments in which students have no access to technology. Can students demonstrate that they have learned the required terminology, understand the course material, and apply theory without consulting ChatGPT?

Academia offers a low-tech solution to this high-tech problem through oral comprehensive exams. Indeed, for generations graduate students have undergone the medieval ordeal of sitting before a board of professors and answering questions to demonstrate their mastery of the material. Having participated in oral exams at the Army War College, I can attest to their capacity to motivate students to complete the readings, consider key concepts, and integrate course material across the curriculum. Although this was not the original intention of the institution, oral exams have become a reliable safeguard against graduating students who relied on AI to complete individual courses. Although oral exams have a high pass rate, I have seen students fail. The practice serves as a genuine quality check that motivates students to learn and ensures graduates meet a minimum standard.

In the age of AI, oral comprehensive exams represent only part of the solution. Spot-checking overall performance at the end of the year does not provide students with adequate feedback or incremental incentives to stay engaged with the course material throughout the academic program. Ideally, professional military education institutions should establish a series of AI-free checkpoints where faculty can evaluate students, monitor their progress, and verify they are prepared to move forward. As with oral comprehensive exams, these incremental checks may appear old-fashioned and include multiple-choice questions, blue book exams, and class discussions. Any evaluation method that prevents students from accessing AI will create an incentive to avoid overreliance on technology. These AI-free assessment tools do not preclude the use of assignments specifically designed to engage with emerging technology. They help create a body of knowledge and a set of skills that students must develop to use this technology effectively.

Conclusion

Lacey’s article is an important wake-up call to professional military education institutions, rousing them from complacency, and encouraging faculty to adapt to the modern world. As a fellow technology enthusiast, I am sympathetic to his call for educational reform. While Lacey tacitly acknowledges the need to adapt our pedagogical methods to include small group discussions, the critical flaw in Lacey’s War on the Rocks article is that he seemingly presents the use of AI as a binary choice. Either professional military education permissively embraces AI or maintain the status quo. There is a middle ground. Allowing students to write their papers with AI risks subverting the entire educational enterprise. By contrast, preserving traditional academic instruction — reading, practical exercises, class discussions, and exams — while supplementing the curriculum with AI-focused content allows students to master the fundamentals without becoming overly dependent on new technology.

Admittedly, this middle ground, in which faculty teach the fundamentals and supplement them with emerging technology, is nothing new. Instructors have been grappling with how to incorporate machines into the classroom since the introduction of the slide rule. Handheld calculators have been ubiquitous for more than forty years, yet elementary school children are still required to learn basic math before using calculators for more advanced mathematics. During my years teaching statistics and research methods at Pennsylvania State
University, I required students to calculate regression statistics such as slopes, intercepts, R², and t-scores using nothing more than a simple calculator and a sheet of equations. Performing the mechanics of regression calculations helped students understand the inner workings of the model and made them more effective when using statistical software such as SPSS. In any academic field, once students develop a level of mastery of the basics, they are prepared to engage with technology, using it to enhance their analysis rather than replace critical thinking.

Lacey’s “all-in” approach to artificial intelligence extends well beyond encouraging students to use the technology in the classroom. Arguing that “there is little in the world of academia that the AI cannot do,” he describes using large language models to design curricula, prepare instructional materials, conduct research, and even draft essays that lay the foundation for a forthcoming book. The appropriate use of AI outside the classroom is, in itself, a complex topic — worthy of a separate treatment. Here too, experienced faculty are better positioned to navigate the practical and ethical implications of this emerging technology, given our lifetime of experience as teachers, researchers, and citizens. While Lacey is right to urge professional military education to embrace artificial intelligence, it should do so in a way that preserves students’ intellectual development. Faculty simply cannot turn them loose on a technology and presume they will, as if by osmosis, develop the same reading, writing and critical thinking skills. If, in its desire to leverage the newest technology, the military promotes AI dependence, America’s future belongs to the machines.

Matthew Woessner, Ph.D., is the dean of faculty and academic programs at the College of International Security Affairs at the National Defense University. He previously served on the faculty at the Army War College and Pennsylvania State University, Harrisburg. The views expressed in this article are those of the author and do not necessarily reflect those of National Defense University or the U.S. government.

Image: Midjourney