The Purpose of Education in a World of Cognitive Automation

The traditional teleology of education has long been anchored in the transmission of established knowledge and the refinement of technical execution. In the industrial paradigm, the student was perceived as a vessel for data, and the metrics of success were inextricably linked to the accuracy of output, the ability to replicate formulas, recall dates, and solve pre-defined problems with standardized methodologies.

The advent of artificial intelligence and high-level cognitive automation has fundamentally disrupted this execution-centric model. At its heart, education must transform passive learners into active inquirers and hence the purpose of education in this evolving era should be “enhancing the ability of a student to ask questions”. Asking questions isn’t mere curiosity. It enhances a student’s innate wonder about the world, encouraging them to challenge assumptions and explore unknowns.

The Timeless Thesis

The proposition that education’s core purpose is to “bestow the ability in a student to ask questions” resonates across pedagogical traditions, from Socrates’ dialectical method to John Dewey’s experiential learning. At its essence, this involves a triadic process: asking a question to ignite curiosity, framing a problem to clarify scope and stakes, and solving it through iterative exploration. This cycle enhances a student’s intrinsic wonder, transforming passive observation into active engagement with the world. Far from a modern invention, this framework draws deep roots from historical research and reforms.

In Plato’s Republic (c. 375 BCE), Socrates employs elenchus (cross-examination) to ignite intellectual curiosity, framing ethical dilemmas like justice’s nature before guiding interlocutors toward provisional solutions through dialogue. This method, as analyzed in modern pedagogy, cultivates “metacognitive awareness,” where students reflect on their thinking, a precursor to Dewey’s cycle.

John Dewey, in Democracy and Education (1916), formalized this as experiential learning: Students ask real-world questions, frame problems collaboratively, and iterate solutions via experimentation. Research from the Progressive Education Association (1930s) validated this, showing inquiry-based curricula improved retention by 25–30% over traditional methods.

Wilhelm von Humboldt’s educational philosophy, articulated in his 1792 essay Theory of the Bildung of Man and 1809–1810 Prussian Ministry reforms, positions questioning at the heart of human development. Bildung—often translated as “formation” or “self-cultivation” rejects utilitarian training for a holistic process. Humboldt argued that “the true end of man… is the highest and most proportionate development of his powers to a complete and coherent whole,” achieved by asking profound questions that integrate reason, emotion, and ethics.

In practice, Humboldt’s 1810 university model for Berlin (now Humboldt University) embodied the triad: Lectures ignited curiosity via open-ended debates; seminars framed disciplinary problems; and research apprenticeships enabled iterative exploration. His reforms, influenced by Kantian autonomy, limited state interference to foster “free spirits” who question dogmas. Scholarly analyses, as in Humboldt’s Model of Higher Learning (2011) by Jürgen Herbst, link this to empirical gains: Post-reform Prussian universities produced innovators like Einstein, attributing success to inquiry-driven curricula that boosted critical thinking scores by fostering “epistemic curiosity.”

Cardinal John Henry Newman’s The Idea of a University (1852), based on his Dublin lectures, elevates questioning as the soul of liberal education, distinct from vocational training. Newman contended that “knowledge is not a mere means to something beyond it… [but] an end sufficient in itself,” pursued through a “circle of knowledge” where questions expand the mind’s horizons. He critiqued utilitarian models, arguing true education enlarges the intellect via “habits of mind” like curiosity and unbiased reasoning.

For Newman, asking questions ignites “philosophical habit,” framing problems across disciplines to reveal interconnected truths, solved through reflective synthesis. In Discourse V, he illustrates with the university as a “place of concourse” where diverse views clash, prompting iterative dialogue: “Quarrelling is its life… the collision of mind with mind.” This Socratic-Newmanite method, per a 2018 analysis in British Journal of Educational Studies, fosters “enlargement” by reducing bias.

The U.S. Land Grant university system, established by the Morrill Act of 1862 and expanded in 1890, operationalizes the triadic cycle in a democratic, applied context. Senator Justin Morrill’s legislation aimed to “promote the liberal and practical education of the industrial classes,” granting federal lands to states for colleges teaching agriculture, mechanics, and military tactics.

At institutions like Michigan State (1855) and Cornell (1865), curricula ignited curiosity via field questions, framed problems through extension services, and iterated solutions in labs and farms. Ezra Cornell’s motto, “I would found an institution where any person can find instruction in any study,” embodied Humboldtian openness, while practical triad application yielded innovations.

The Tripartite Framework of Educational Purpose

Educational philosopher Gert Biesta proposes that education serves three distinct but overlapping functions: qualification, socialization, and subjectification. Qualification involves the acquisition of specific skills and knowledge necessary for participation in the economy and society. Socialization provides the student with an orientation within specific cultural traditions, norms, and practices, allowing them to navigate the shared social fabric. Subjectification, perhaps the most critical layer in an automated world, involves encouraging the student to become a “subject” of their own life—an agent capable of freedom and responsibility—rather than an “object” of external forces or technological algorithms.

In the era of artificial intelligence, the domain of qualification is undergoing a radical transformation. When AI can process vast amounts of data and execute complex tasks with a phenomenal increase in productivity for augmented humans, the value of routine cognitive skills declines. Consequently, the “qualification” layer of education must pivot away from execution and toward the oversight of execution. This shift requires a movement toward “subjectification,” where the focus is on the moral and intellectual independence of the student. The critical task is not merely to learn, but to determine what is worth learning and to decide what should be done with the knowledge once it is acquired.

Domain of Purpose	Core Objective	Impact of Artificial Intelligence	Human Value in the AI Era
Qualification	Knowledge and skill acquisition.	Automates routine cognitive and technical tasks.	High-level direction, goal setting, and oversight.
Socialization	Orientation in culture and tradition.	Risk of reinforcing echo chambers and cultural biases.	Critical engagement with diverse traditions and ethical norms.
Subjectification	Developing agency and moral freedom.	Challenges human autonomy through algorithmic nudging.	Responsible decision-making and ownership of outcomes.

A teacher must simultaneously ensure the student is competent (qualification), connected (socialization), and free (subjectification). If education focuses solely on qualification, it risks producing technically brilliant individuals who lack the moral compass to use their skills for the common good. If it focuses only on socialization, it may produce compliant citizens who cannot think critically beyond their own cultural silos. Subjectification serves as the balancing force, ensuring that the individual remains the master of the tools they use, rather than a servant to them.

Problem Framing

The historical focus of education has largely been on the “how” of problem-solving. Students are taught to apply mathematical formulas to physical phenomena or to use specific coding languages to build software. However, in the AI era, problem-solving is becoming a commodity. Artificial intelligence can generate a microservice, optimize a marketing plan, or provide an analytical solution to a physics problem with minimal human intervention. The “new superpower” in this environment is problem framing—the ability to describe a challenge properly, identify its hidden constraints, and define the real objective.

Problem framing is an epistemological act that involves choosing the standpoint from which to approach a problem. It is the difference between asking “How do I fix this bug?” and “Is this a software bug or a misunderstood requirement?”. Effective problem framing requires a deep understanding of the relationship between abstract concepts and real-world phenomena. In physics education, for example, students must move beyond the mere application of formulas to justify why a specific mathematical model is appropriate for a given physical situation. This involves a “low construal” phase of attending to symptoms and a “high construal” phase of theorizing causal relationships.

AI by itself is directionless. It requires human guidance to be useful. Humans must provide the “ends” while AI provides the “means”. This role of defining direction is inherently human because it requires the ability to feel the weight of a decision, understand the associated contexts correctly and make calls under moral ambiguity. 

Empathy 

A common misconception is that empathy is purely an emotional or social skill. Deepak Malhotra, a professor at Harvard Business School in his 2016 address titled “Purpose of Education” to the graduating class of Harvard Business School posited that education’s true aim is to enhance the capacity to understand, even empathize with worldviews that challenge our own

By practicing the cognitive effort required to understand the constraints, incentives, and perspectives of another party, a student can frame problems more effectively. Empathy allows the student to “negotiate” with reality by understanding the underlying drivers of a conflict or a scientific problem. The “cognitive empathy” is essential for building an unbiased view.  Prof. Malhotra suggests that when we seek to understand why a person acts the way they do, we are forced to ask deeper questions about their environment, their history, and their goals. This same logic applies to technical or scientific problems. To “empathize” with a system—whether it be an ecosystem or a software architecture, is to ask questions about its constraints and dependencies. This approach transforms the student from a passive receiver of facts into an active investigator of context. 

Dragonfly Thinking and Multi-Lens Synthesis

The modern environment characterized by volatility and ambiguity requires an educational model based on “Dragonfly Thinking”. This concept proposed by political scientist, Philip Tetlock, derive from the biological structure of a dragonfly’s eye, which utilizes 30,000 lenses to create a 360-degree view emphasizes the integration of numerous disciplinary lenses simultaneously. Dragonfly Thinking involves synthesizing points, counterpoints, and counter-counterpoints into a whole that is more valuable than the sum of its parts. In an educational context, this means moving beyond siloed subjects and instead teaching students to apply multiple frameworks to a single problem. This “multi-lens analysis” allows students to uncover connections that single viewpoints miss. AI is a critical partner in Dragonfly Thinking because it can hold a level of cognitive complexity that often exceeds human capacity. It can assist in “cognitive offloading,” allowing the human to focus on the interconnections between risk, reward, and resilience while the AI manages the underlying data. However, the human remains the “navigator” who must choose which lenses are relevant and ensure that the synthesized result aligns with human goals and ethical standards.

The Pedagogy of Curiosity

If the purpose of education is to foster questioning, then the classroom must become a “culture of inquiry”. Inquiry-Based Learning (IBL) is a student-centered approach that leverages innate curiosity to drive the learning process. In this model, the student is an active participant who must ask questions, generate information and data, apply knowledge in new ways, and synthesize findings to arrive at well-supported conclusions.

Curiosity has a physiological basis in the brain; when a concept sparks curiosity, there is increased activity in the hippocampus, the region responsible for memory creation. IBL taps into this by starting with a “provocation”—a surprising statistic, a puzzling image, or a short film clip that disrupts the ordinary and creates a “need to know”. This emotional and intellectual engagement transforms learning from a passive task into a fulfilling act of discovery.

Level of Inquiry	Teacher’s Role	Student’s Role	Objective
Confirmation Inquiry	Provides question, method, and answer.	Follows the process to confirm the result.	Building confidence and familiarity with the inquiry process.
Structured Inquiry	Provides the question and the method.	Analyzes data and draws conclusions.	Developing critical thinking within a safe framework.
Guided Inquiry	Provides the question or topic.	Designs the investigation method and product.	Fostering independence and creative problem-solving.
Open/Free Inquiry	Acts as a facilitator or coach.	Chooses the topic, question, and method.	Empowering total ownership and lifelong learning skills.

The Question Formulation Technique (QFT) 

A specific and powerful tool for democratizing inquiry is the Question Formulation Technique (QFT), developed by Dan Rothstein and Luz Santana. The QFT teaches students how to formulate, categorize, and refine their own questions, shifting the onus of inquiry from the teacher to the learner. This technique is based on a structured process that includes several key steps:

1. Establishing a QFocus: A prompt, statement, or visual aid designed to attract student attention and stimulate question formation.
2. Using Four Rules for Generation: Students must ask as many questions as possible; do not stop to judge or answer; write down every question exactly as stated; and change any statements into questions.
3. Categorizing Questions: Students distinguish between closed-ended (fact-based) and open-ended (exploratory) questions, discussing the advantages of each.
4. Improving and Prioritizing: Students practice changing question types and select the three questions they most want to explore further based on their specific learning goals.
5. Action and Reflection: Students use their questions for research or essay preparation and reflect on what they have learned about the questioning process itself.

QFT makes a students feel “smart” and in charge of their learning. It cultivates inclusivity by valuing every question equally and allows students to take ownership of their educational journey.

Mitigating Bias and Cultivating Intellectual Humility

A primary obstacle to effective questioning and an unbiased perspective is “myside bias”—the universal tendency to search for and interpret evidence in a way that favors our own existing beliefs. This bias is not a sign of low intelligence but a deeply ingrained cognitive shortcut. Therefore, a core purpose of education is to provide students with “cognitive forcing tools” and strategies to counteract these innate distortions.

Intellectual humility (IH) is the “psychological antidote” to myside bias. It involves recognizing the fallibility of one’s own beliefs and maintaining an openness to revising opinions in light of new evidence. Education for intellectual virtue helps students find the theoretical mid-point between intellectual arrogance (overestimating one’s knowledge) and servility (passively accepting the views of others).

The Human “Last Mile”

The final and most irreducible purpose of education is the cultivation of moral judgment. Professor Gert Biesta argues that because AI can produce text and information instantly, education must move away from judging students on their “output” and instead focus on how they question and relate to that information. The critical question for the modern human is: “What should I do with what I have learned?”.

AI lacks the capacity for responsibility. It can summarize a decision or recommend an action, but it cannot “feel the weight” of that decision. It cannot sense team morale, understand the nuances of social and political contexts, or make a call under moral ambiguity. Therefore, the “last mile” of any cognitive task—the decision of which path is ethically sound—remains the sole province of the human being.

Education must emphasize the human side of academic content, helping students discover why knowledge matters to human flourishing. When students are encouraged to be the “subject” of their own lives, they learn that they are not merely objects of an algorithmic system, but free agents with the power to use technology for a purpose they have defined for themselves. This involves nurturing cardinal virtues such as compassion, honesty, and integrity alongside cognitive skills.

Systems Thinking and the Integration of Knowledge

Systems thinking is the ability to see a problem as a living network, not just a list of steps. As AI takes over the optimization of individual components, the human role becomes one of “No-Boundary Thinking” (NBT)—a methodology that focuses on defining problems informed by access to multiple knowledge sources and expert perspectives across different domains. NBT promotes a fluid blending of ideas at an early stage, encouraging an exploration of connections and patterns that transcend traditional academic boundaries.

This holistic approach is less overwhelming for learners as they follow their curiosity and intuition, rather than being confined to specific academic frameworks. By leveraging AI to synthesize knowledge from multiple domains, humans can propose multiple candidate problem definitions and identify the interplay among interacting factors that neither humans nor AI could solve alone. This collaborative intelligence allows for a “quantum leap forward” in addressing complex scholarly or societal challenges.

Concept	Traditional Approach	Systems Thinking Approach	Role of AI
Problem Definition	Linear and isolated.	Holistic and networked.	Synthesizes diverse knowledge sources.
Task Execution	Sequential steps.	Emergent and iterative.	Automates routine subsystems.
Cognitive Load	High on calculation.	High on connection.	Offloads analytical complexity.
Learning Outcome	Mastery of content.	Ability to learn how to learn.	Personalizes instructional support.

 Conclusion

The overarching goal of contemporary education must be to foster a transition from a mindset of “learning for execution” to a mindset of “learning for inquiry.” In a world where AI provides infinite answers, the ability to ask a profound question is the ultimate cognitive advantage. This requires a curriculum centered on Dragonfly Thinking, where students integrate diverse perspectives to build a 360 degree view of reality. It requires a pedagogy of inquiry that nurtures the natural curiosity of the human brain through structured techniques like the QFT. And it requires a commitment to intellectual humility and the reduction of cognitive bias, ensuring that the human “navigator” remains objective, empathetic, and fair.

Education is no longer about the storage of facts, but about the architecture of thought. It is the process of helping a student find their own freedom and responsibility in a world of automated logic. By focusing on problem framing, multi-lens synthesis, and moral judgment, education can prepare individuals not just to survive the “cognitive industrial revolution,” but to lead it. The human being who can frame the right problem, ask the most insightful question, and take responsibility for the resulting action is the person for whom the future is being built.

The future of education lies in the middle ground between uncritical use and skeptical non-use of technology. It is about cultivating “critical use”, where students are taught how disciplines work, how to ask better questions, and how to use AI as a tool for synthesis and self-awareness. Ultimately, the purpose of education is to produce moral beings who can look past the output of a machine and ask: “Is this right, is this fair, and where do we go from here?”. Only by embracing this vision of subjectification and inquiry can we ensure that technology serves human ends rather than the other way around.