AI in Education: Can Machines Replace Teachers?
In the rapidly evolving landscape of education, artificial intelligence (AI) has emerged as a transformative force, reshaping how knowledge is delivered, absorbed, and assessed. From intelligent tutoring systems to adaptive learning platforms, AI tools are increasingly embedded in classrooms around the world. As these technologies grow more sophisticated, a fundamental question looms large: Can artificial intelligence truly replace human teachers?
This inquiry is no longer confined to science fiction or speculative discourse. It has become a pressing philosophical and practical concern for educators, policymakers, and technologists alike. A recent in-depth study published in Teacher Education Research by Zhang Wunong of Henan University, Jia Baoxian from Liaocheng University, and Zeng Qiang and Chang Sheng from Tsinghua University, offers a rigorous philosophical examination of this issue, challenging the assumption that technological advancement inevitably leads to human obsolescence in teaching.
The paper, titled “‘Proxy’ or ‘Substitution’ —— Is Artificial Intelligence Really Possible Replacing Human Teachers,” does not dismiss the capabilities of AI. On the contrary, it acknowledges the remarkable progress in machine learning, neural networks, and data analytics that have enabled AI to outperform humans in specific cognitive tasks—such as pattern recognition, information retrieval, and even complex game strategies like Go. The authors cite AlphaGo’s historic victory over world champion Lee Sedol as emblematic of AI’s growing prowess. In educational settings, AI systems can process vast datasets, personalize learning trajectories, and provide instant feedback, often with greater speed and consistency than human instructors.
Yet, the central argument of the study is that superiority in function does not equate to equivalence in role. Drawing on insights from philosophy of technology, ethics, and cognitive science, the authors assert that while AI may serve as a powerful proxy—a tool that extends and enhances human capability—it cannot act as a true substitute for the human teacher in any holistic, ethical, or existential sense.
One of the paper’s key contributions lies in its distinction between two types of questions surrounding AI in education: those of technical possibility and those of value rationality. The former asks whether AI can simulate human intelligence; the latter asks whether it should, and under what conditions. While the technical trajectory suggests that AI may one day replicate many aspects of human cognition—even emotional recognition through affective computing—the authors argue that this does not resolve the deeper philosophical dilemmas.
At the heart of their analysis is the concept of irreducible human subjectivity. Human teachers are not merely information processors; they are moral agents, cultural transmitters, and relational beings. Teaching, the authors emphasize, is not simply the transmission of data but the nurturing of growth—intellectual, emotional, and ethical. This growth occurs within a dynamic, intersubjective relationship between teacher and student, one that is grounded in shared humanity, empathy, and lived experience.
AI, by contrast, lacks the biological and phenomenological foundation necessary for genuine subjectivity. It has no body, no emotions, no mortality, and no intrinsic motivation. As the authors note, human consciousness evolved through embodied interaction with the environment, shaped by evolutionary pressures, social dynamics, and cultural narratives. AI, being a product of design rather than evolution, operates within predefined parameters and cannot transcend its programming to achieve true autonomy or self-awareness.
The paper further explores the limitations of AI in understanding context, language, and creativity. While AI can parse syntax and generate grammatically correct sentences, it struggles with semantics that depend on cultural nuance, historical background, and emotional subtext. Human language is not a closed system of rules but an open, evolving practice embedded in social life. As philosopher Ludwig Wittgenstein argued, meaning arises from use, not from formal structure alone. AI may mimic conversation, but it cannot participate in dialogue in the full human sense.
Similarly, while AI can engage in combinatorial and exploratory forms of creativity—rearranging existing ideas or optimizing solutions within known frameworks—it falls short in transformational creativity, the kind that redefines the rules themselves. The authors reference Immanuel Kant’s notion of genius as the ability to create new rules, a capacity that emerges from lived experience, intuition, and imagination—qualities that cannot be algorithmically encoded.
Perhaps most compellingly, the study addresses the ethical dimensions of replacing teachers with machines. The authors argue that teaching is inherently a moral practice. Teachers model values, guide character development, and respond to students’ needs with care and judgment. These are not merely procedural tasks but acts of ethical responsibility that require empathy, discretion, and a sense of duty.
AI, even when programmed with ethical guidelines, operates through heteronomy—obedience to externally imposed rules—rather than autonomy, the capacity for self-legislation. A machine can follow a rule, but it cannot reflect on its validity, question its application in novel situations, or feel remorse when it causes harm. The Confucian ideal of shen du, or moral integrity in solitude, is inaccessible to any entity without self-consciousness.
Moreover, the presence of a human teacher conveys something intangible yet essential: the recognition of the student as a fellow human being. This recognition fosters trust, belonging, and identity formation—elements critical to education but impossible to quantify or automate. When students interact with AI, they may receive accurate answers, but they do not experience the warmth of encouragement, the subtle correction of a raised eyebrow, or the spontaneous moment of shared laughter that often marks the most memorable lessons.
The authors also warn against the technological reductionism that underlies much of the discourse on AI in education. Reducing teaching to a set of measurable outcomes and automating its delivery risks turning education into a mechanistic process devoid of meaning. They caution that the real danger is not that AI will become too human-like, but that humans may begin to think and act like machines—prioritizing efficiency over depth, standardization over individuality, and data over wisdom.
This concern is particularly relevant in the context of personalized learning algorithms, which, while promising, can create “filter bubbles” that limit exposure to diverse perspectives. The paper highlights the risk of cognitive cocooning, where students are fed content tailored to their existing preferences, reinforcing biases and narrowing intellectual horizons. Human teachers, by contrast, can deliberately introduce dissonance, challenge assumptions, and broaden worldviews—functions that cannot be outsourced to algorithms.
The study also examines the impact of AI on teacher identity and professional development. If educators increasingly rely on AI for lesson planning, assessment, and even student engagement, there is a risk of deskilling—the erosion of pedagogical expertise and reflective practice. The authors argue that AI should be designed not to replace teachers, but to augment their agency, freeing them from routine tasks so they can focus on higher-order aspects of teaching: mentoring, facilitating discussion, and inspiring curiosity.
In this vision, AI becomes a co-teacher rather than a displacer. For example, an AI system might analyze student writing for grammatical errors, allowing the teacher to concentrate on rhetorical structure, argumentation, and voice. Or it could track learning analytics to identify struggling students, enabling the teacher to intervene with targeted support. The goal is not automation, but amplification—using technology to enhance the uniquely human dimensions of education.
The paper also considers disciplinary differences in AI’s applicability. In STEM fields, where knowledge is often procedural and rule-based, AI may assume a larger role in delivering content and assessing performance. However, even here, the authors caution against over-reliance. If students outsource all calculation and problem-solving to machines, they may lose foundational skills and conceptual understanding. There is also the risk of creating a technological elite—those who control the algorithms—while the majority become passive users.
In the humanities and social sciences, where interpretation, critique, and value judgment are central, the role of AI is likely to remain limited. These disciplines deal with ambiguity, contested meanings, and normative questions that resist algorithmic resolution. In fact, the authors suggest that as AI permeates technical domains, the importance of humanistic education may increase, providing the ethical and philosophical grounding necessary to navigate a world shaped by intelligent machines.
The aesthetic and moral dimensions of education are equally resistant to automation. While AI can compose music, generate poetry, or produce visual art, these creations lack the lived experience and emotional depth that give human art its resonance. The authors point to the poetry of AI-generated “Xiaobing,” noting that while readers may feel moved by the verses, the machine itself experiences nothing. Art, in its deepest sense, is an expression of the human condition—an act of meaning-making that cannot be replicated by a system without consciousness.
Similarly, moral education cannot be reduced to rule-following. True virtue involves internalization, reflection, and the cultivation of character—processes that unfold through relationships, not programming. A robot may be programmed to avoid harming humans, but it cannot understand the weight of that prohibition or feel the moral anguish of a difficult decision.
Ultimately, the authors conclude that the teacher-student relationship is irreplaceable because it is fundamentally a relational rather than a transactional bond. Education is not a service to be optimized but a journey of becoming. It is through sustained, embodied interaction with a caring adult that students learn not just facts, but how to be in the world—to think critically, act ethically, and connect meaningfully with others.
The paper calls for a reorientation in how we approach educational technology. Rather than asking how AI can replace teachers, we should ask how it can serve the human purposes of education. This requires interdisciplinary collaboration between technologists, educators, and philosophers to ensure that AI is developed and deployed in ways that uphold human dignity, promote equity, and enrich the learning experience.
It also demands a critical literacy around AI—one that enables students and teachers alike to understand the limitations and biases of algorithmic systems. As AI becomes more pervasive, the ability to question, interpret, and resist technological determinism becomes a crucial educational objective.
In sum, the study offers a powerful counter-narrative to the techno-utopian vision of fully automated classrooms. It reminds us that education is not just about efficiency or outcomes, but about meaning, growth, and the transmission of culture across generations. While AI can be a valuable tool, it cannot assume the role of the teacher, for teaching is not merely a function to be performed, but a vocation rooted in human care, wisdom, and hope.
As the authors write, “The real crisis is not that AI is becoming too human, but that humans are becoming too machine-like.” In resisting this drift, educators have a vital role to play—not as relics of a pre-digital age, but as guardians of what makes us fundamentally human.
Zhang Wunong, Jia Baoxian, Zeng Qiang, Chang Sheng. “‘Proxy’ or ‘Substitution’ —— Is Artificial Intelligence Really Possible Replacing Human Teachers.” Teacher Education Research, Vol. 33, No. 1, January 2021.