From Answer-Giving to Question-Asking: Inverting the Socratic Method in the Age of AI

By Anthea Roberts

When I first walked into a Harvard Law School classroom as a visiting professor in 2011, I experienced what generations of new law professors had before me: the quiet terror of the Socratic method. This legendary pedagogical approach—where professors pepper students with probing questions rather than delivering lectures—had been the cornerstone of American legal education since Dean Christopher Columbus Langdell pioneered it at Harvard in the 1870s.

I had already been teaching law at the London School of Economics. But the famed Socratic method at Harvard Law School was different to the traditional lecturing approach that I knew. What struck me then was how the Socratic method inverted traditional teaching. Instead of professors displaying their knowledge through giving answers, they revealed it through asking questions. The point wasn't to tell students what to think, but to teach them how to think—specifically, how to "think like a lawyer."

Now, fourteen years later, I've just returned from another stint at Harvard Law School as AI systems like ChatGPT and Claude are becoming ubiquitous in education (even if most students and many professors are still in denial about how widespread this use is). And I've been struck by what seems to me like an important realization: we may need to invert the Socratic method again. In the age of AI, where answers are abundant and instantaneous, perhaps the most valuable skill we need to teach isn't about how to answer questions, but instead how to ask them.

The Original Inversion: How Harvard Transformed Legal Education

To appreciate this new inversion, we should understand the first one. Before Langdell's revolution, legal education consisted primarily of rote memorization from treatises. Langdell upended this model by insisting students read original court cases and derive principles themselves. To guide this challenging process, he developed what we now call the Socratic method—"an interrogatory style in which instructors question students closely" about case facts, reasoning, and principles.

This approach was revolutionary precisely because it shifted class time from passive reception of answers to active inquiry. Instead of students memorizing settled rules, they learned to grapple with ambiguity and unknown scenarios. As Harvard professor Martha Minow observed, legal education embraced cases with conflicting interpretations to build students' "comfort with ambiguity." The result was training not just in legal doctrine but in critical thinking under uncertainty.

When I first taught at Harvard, I quickly realized this method wasn't just about intimidating first-year students (though it certainly did that). It was about cultivating a particular cognitive skill: the ability to probe beneath surface-level answers to reveal deeper questions and tensions. Good law professors don't just ask questions; they ask the right questions—the ones that expose assumptions, illuminate contradictions, and push thinking forward.

The AI-Driven Second Inversion

Fast forward to 2025, and we're witnessing the early stages of an intriguing reversal. With generative AI systems that can produce sophisticated answers to nearly any question, the role of question-asker has become increasingly valuable. In a world where answers are readily available, good questions—and the ability to engage in sustained, iterative questioning—have become precious.

This isn't simply about asking one good question and receiving a perfect answer. Just as the traditional Socratic method in a law classroom involves a back-and-forth dialogue—professor asks, student answers, professor probes deeper, student refines—effective engagement with AI requires an iterative exchange. The skilful AI user poses an initial question, evaluates the response, identifies gaps or weaknesses, asks follow-up questions, challenges assumptions, requests elaboration, and gradually refines both the questions and the resulting answers.

This pattern is emerging beyond education. In professional settings, those who can engage in this dance of iterative questioning extract more value from AI systems than those who simply seek one-off answers. The ability to probe, refine, and direct AI inquiry—what some call "prompt engineering"—has become an important skill and one on which people vary wildly.

Perhaps what we need to envisage is the inversion of the traditional Socratic classroom: instead of professors questioning students to develop their thinking, students need to learn to question AI to shape its outputs and develop their own understanding. The roles have started to flip, but the underlying principle—that sustained questioning drives deeper understanding—remains.

SocraSynth: The Machine Learning to Question Itself

One fascinating development in this evolving landscape is how AI systems themselves are being designed to incorporate Socratic principles. One experiment that captures this trend is SocraSynth, a multi-agent AI framework inspired by Socratic debates.

In SocraSynth, multiple AI agents engage in a structured debate on a topic—one taking one position, another the opposite—moderated by a human. The AI agents pose questions and counterarguments to each other in rounds, creating an AI "symposium." After the debate, a separate set of AI "judge" models evaluates the arguments for logical soundness using a "Critical Inquisitive Template."

What's remarkable about this system is how it inverts the traditional human-AI interaction. Instead of humans questioning the AI, the AI questions itself through an adversarial process. By forcing consideration of opposing viewpoints through internal debate, the system aims to produce more reasoned, less biased outputs.

We should be careful not to overstate current capabilities, however. Nevertheless, this approach recognizes that even advanced AI systems benefit from the disciplined application of questioning. Just as Socrates believed that "the highest form of human excellence is to question oneself and others," projects like SocraSynth suggest that more sophisticated artificial intelligence might eventually incorporate the ability to question its own assumptions and conclusions.

The Current Educational Landscape and Future Possibilities

The reality in 2025 is that most educational institutions, including elite ones like Harvard, are still primarily focused on defensive measures: How do we prevent students from using AI to cheat? How do we ensure AI doesn't become a crutch that atrophies critical thinking skills? How do we maintain academic integrity in an age of instant, AI-generated answers?

These concerns are legitimate. When students outsource their thinking to AI—having it write essays or solve problems without engaging their own analytical skills—they miss the developmental benefits that struggle and effort provide. Many institutions have responded with AI detection tools, redesigned assessments, or outright bans on AI use.

But focusing exclusively on preventing the floor from dropping out may cause us to miss the opportunity to raise the ceiling. The more interesting question from my perspective is: How might human-AI collaboration enable new heights of learning and achievement that neither humans nor AI could reach alone? And what cognitive skills and strategies do we need to develop in education in order to help students reach these new peaks?

If questioning is indeed becoming a premier cognitive skill in the AI age, how should education and professional development evolve? Here are some possibilities:

1. Assessment Through Iterative Questioning: Rather than evaluating students solely on their answers, we might assess their ability to engage in sustained, productive questioning—their skill at probing, following up, identifying inconsistencies, and refining inquiries over multiple rounds. Can they navigate a complex problem through a series of well-crafted questions? Can they identify when an AI response contains subtle errors or omissions that require further exploration?

2. Prompt Literacy as Core Curriculum: Just as reading and writing are foundational literacies, the ability to effectively prompt and question AI systems may become a basic skill taught from early education onward. This would include teaching students how to refine queries, test assumptions, and evaluate AI responses critically—recognizing that AI systems still hallucinate, contain biases from their training data, and have uneven performance across different domains.

3. Socratic AI Interfaces: Future AI interfaces might be designed explicitly to encourage Socratic dialogue rather than one-sided Q&A. Instead of simply answering queries, these systems might respond with clarifying questions of their own: "It sounds like you're asking about X—can you tell me more about your specific interest in this area?" This would model the kind of iterative exchange that characterizes productive human-human dialogue.

The Law Classroom Reimagined

Returning to where we started—the law school classroom—how might this evolution reshape legal education? Law was an early adopter of the Socratic method; could it once again be a proving ground for new pedagogies?

Legal education presents a particularly interesting case study because law is precisely the kind of field where AI systems both shine and struggle. They excel at summarizing case law and identifying patterns across large bodies of legal text, yet falter with the deeply contextual, value-laden questions that lie at the heart of legal practice—questions about "reasonable" behavior, balancing competing rights, or interpreting a statute's purpose in novel circumstances.

In this context, the value of human lawyers increasingly lies in their ability to engage in sophisticated questioning—of clients, witnesses, opposing counsel, and of AI tools themselves. Instead of only cold-calling students to recite case facts, professors might assess how well students can probe a case: What series of questions would you ask a client to uncover all relevant facts? How would you test the strength of a precedent by questioning its reasoning? How would you engage with an AI legal research tool to extract the most valuable insights while avoiding its limitations?

These skills—precision questioning, critical probing, and iterative refinement—represent the new frontier of legal education in the AI era.

The Socratic habit of mind—always interrogating and never taking assertions at face value—becomes even more crucial when working with AI systems that can present information with an air of authority even when their understanding is incomplete or flawed. The lawyers who thrive will be those who can engage in the kind of sustained, sophisticated questioning and review that illuminates paths forward in novel situations—the kind of questioning and probing that lies at the heart of both the traditional Socratic method and its AI-age inversion.

When I reflect on my own journey as a professor—from that first nervous day at Harvard Law School to today's yet-to-be-fully-acknowledged AI-influenced classroom—I'm struck by how the essence of good teaching has remained constant even as the methods evolve. The goal has always been to cultivate independent critical thinking in students. What has changed is not the aim, but the environment in which this thinking must operate—one increasingly mediated by AI systems that can provide answers but often lack deeper understanding.

In 2011, I did this by asking probing questions that forced students to articulate their reasoning. In 2025, I increasingly find myself thinking about how to help students formulate their own questions to the AI tools they're beginning to use. The direction of the questioning is starting to shift, but the underlying purpose remains.

What gives me hope is that the most human aspects of intellectual work—curiosity, judgment, and thoughtful inquiry—are becoming more valuable, not less. In a world populated with machine-generated answers of varying quality, the distinctly human art of asking good questions and evaluating responses critically may be our most enduring advantage.

Beyond the Classroom: The Broader Implications

This evolving relationship with the Socratic method also has implications far beyond education. It touches on fundamental questions about how humans and AI will collaborate in knowledge work.

In the traditional model, humans were knowledge providers and decision-makers. In the emerging model, humans and AI share these roles, with humans increasingly focusing on framing problems, directing inquiry, evaluating options, and engaging in iterative questioning rather than simply producing or consuming information. Humans still make the decisions, but the pathway to arriving at them becomes much more co-created.

This shift demands a recalibration of what we value in cognitive work. Speed and memory—long considered markers of intelligence—are becoming less valuable as AI excels at rapid recall and processing. In their place, judgment, curiosity, critical evaluation, and the ability to pose insightful questions and sustain productive inquiry are emerging as premium skills. These distinctly human capacities for nuanced questioning and contextual understanding become our comparative advantage in human-AI collaboration.

We must recognize AI's current limitations. These systems still hallucinate facts and carry biases from their training data, and while impressive generalists, they can be outperformed by human specialists in domains requiring deep expertise or contextual understanding. These limitations heighten the importance of questioning effectively. The human ability to detect inconsistencies, probe hidden assumptions, and engage in iterative questioning becomes even more valuable when working with systems that have these shortcomings.

The Question Mark Ascendant

Two and a half millennia ago, Socrates wandered the Athenian agora, pestering citizens with probing questions to test their wisdom. In 1870, Harvard's Langdell shocked his students by refusing to spoon-feed them information, opting instead to pepper them with queries in a law classroom. And now, in 2025, we find ourselves surrounded by artificial minds that, for all their computational power, still depend on our questions to show their greatest worth.

The common thread through these eras is the enduring power of the question mark—that tiny hook that opens minds. As we integrate AI into learning and work, we are all becoming participants in a grand Socratic dialogue with our machines and with each other. This dialogue, when done well, elevates both human and machine thinking beyond what either could achieve alone.

In an era when advanced AI can answer almost anything, the true art is knowing what to ask. It is not the time to abandon the Socratic method, but rather the moment to invert it—transforming ourselves from answer-seekers into question-crafters, from information consumers into inquiry designers.

As I tell my students today: In a world of instant answers, the power of good questions becomes everything.

References:

Harvard Magazine – “Making the Case” (September 2003) (Making the Case | Harvard Magazine) – Historical background on Langdell’s introduction of the Socratic/case method at Harvard Law School and its evolution and critiques in legal education.

Diplomacy.edu – “What can Socrates teach us about AI and prompting?” (2023) (What can Socrates teach us about AI and prompting? - Diplo) – Explores the analogy between Socratic inquiry and AI prompt engineering, suggesting that incorporating Socratic questioning elements can improve our interactions with AI and deepen critical thinking.

Edward Y. Chang, Multi-LLM Agent Collaborative Intelligence: The Path to AGI (2025) – Introduction to the SocraSynth platform which blends “Socratic Synthesis” and “Socratic Symposium” by having multiple AI agents debate a topic under a human moderator, using a Socratic reasoning framework (CRIT algorithm) to evaluate and refine arguments. Illustrates an AI experiment employing dialogic learning and multi-agent inquiry.