Teacher-Free AI School to Open in Chicago

by Iryna Pavlova

The recent announcement of a teacher-light, AI-driven school opening in Chicago signals a bold new direction in educational innovation; one that promises personalized, efficient, and scalable learning through artificial intelligence. An article in Government Technology highlights the launch of what is being described as a “teacher-free” AI school, where students will learn primarily through adaptive technologies rather than traditional classroom instruction (Armanini, 2026). As enthusiasm grows around the potential of AI to reshape classrooms, it is worth pausing to consider what may be lost in the process. Education is not simply the delivery of content or the optimization of learning pathways. It is a deeply human endeavor grounded in relationships, dialogue, and the development of judgment. While AI may transform how students access information, it cannot replace the human dimensions of teaching that make education meaningful. At the same time, the emergence of AI-led schools highlights important innovations. These models offer the potential for personalized learning, real-time feedback, and expanded access. These benefits are difficult to achieve consistently at scale in traditional classrooms. AI systems can tailor instruction to individual learners, identify gaps in understanding, and provide immediate support. For some students, particularly those who benefit from flexible pacing or additional language support, these tools may enhance engagement and accessibility. Research on AI-enabled adaptive learning systems supports these claims, showing that such platforms can improve learning outcomes while offering customized learning pathways, targeted scaffolding, and flexible presentation formats that respond to individual learner needs (Wang et al., 2024).

However, these advantages address only one dimension of the educational experience. Education is not solely about the efficient delivery of content as it also involves the cultivation of critical thinking, ethical reasoning, and meaningful relationships. These elements are inherently human and difficult to replicate through technology alone. Teaching is relational, and mentorship, empathy, and professional judgment play a central role in student development. Importantly, even proponents of AI-enabled systems acknowledge that educators remain essential: they must understand how these systems function, interpret the data they generate, and use their professional expertise to guide learning and inform instructional decisions (Wang et al., 2024). This raises an important question: can AI technologies support an educational model that minimizes the central involvement of human teachers? A balanced approach, therefore, requires acknowledging the strengths of AI while remaining attentive to the aspects of teaching and learning that depend on human connection and judgment.

Scholarly work also suggests that evaluating AI in education solely through learning outcomes provides an incomplete picture. Broader considerations such as equity, learner agency, transparency, and the evolving dynamics between teachers and students are equally important in understanding the full impact of AI in educational contexts (Favero et al., 2026). These perspectives highlight the need to situate AI within a wider educational and ethical framework rather than viewing it purely as a tool for efficiency.

Equity is another important consideration. With reported tuition costs of approximately US$55,000 per year, access to such schools may be limited to students with significant financial means. This raises concerns about the emergence of a two-tier education system, where AI-led models become a premium offering rather than a solution for underserved communities. Rather than addressing existing inequities, such models may risk reinforcing them. Research indicates that students with stronger academic backgrounds and higher levels of digital literacy are better equipped to critically evaluate and effectively use AI tools, while others may rely on them uncritically. Without intentional efforts to build inclusive AI literacy, these disparities may deepen existing educational inequalities (Favero et al., 2026).

The development of critical thinking skills, widely recognized as a core goal of education, also warrants closer examination. Learning is not a frictionless process; it involves uncertainty, questioning, trial and error, and dialogue. While AI can provide rapid answers and continuous feedback, it may also limit opportunities for deeper cognitive engagement. Immediate responses can reduce the productive struggle that often leads to meaningful learning. Research in this area highlights the importance of cognitive effort, often experienced as difficulty or discomfort, in fostering deep understanding and long-term retention. AI systems that prioritize speed and fluency risk minimizing this effort, potentially weakening critical thinking unless they are deliberately designed to preserve and scaffold productive struggle (Favero et al., 2026).

AI-led models also rely heavily on students’ capacity for self-directed learning. This assumes a level of independence, motivation, and metacognitive skill that many learners are still developing. Without consistent human guidance and accountability, some students may disengage, potentially widening the gap between those who thrive in independent environments and those who require more structured support. From this perspective, learner agency becomes a critical concern. While agency involves the ability to make informed and autonomous choices, the convenience and persuasive nature of AI-generated outputs may encourage passivity, positioning students as consumers of information rather than active participants in their own learning (Favero et al., 2026).

Proponents of AI-led schools suggest that educators are not being replaced but rather repositioned as guides or facilitators. MacKenzie Price, the founder of Alpha School, has argued that teachers do not need to be subject-matter experts in this model (Armanini, 2026). This shift raises important questions about the role of professional expertise in education. If educators are distanced from content knowledge, their ability to identify errors, challenge misconceptions, or respond to inaccuracies, particularly given the known limitations of AI systems, may be diminished. More broadly, research emphasizes that education plays a crucial role in shaping not only knowledge, but also emotional resilience, social responsibility, and civic engagement. Replacing human interaction with automated systems risks weakening these foundations. Addressing these risks requires prioritizing emotional and social well-being as central considerations in the integration of AI (Favero et al., 2026).

Finally, it is important to note that AI-led schools are emerging in the absence of robust, long-term research on the effectiveness of fully AI-driven curricula. The educational outcomes of teacher-light or teacher-free models remain largely unknown. As a result, the rapid adoption of such approaches may be better characterized as innovation outpacing evidence. While AI-led models promise efficiency, personalization, and scalability, each of these advantages carries corresponding limitations. What is gained in consistency and speed may come at the expense of depth, relational engagement, and responsiveness to the full complexity of learners. Existing research also cautions that discussions of AI in education often remain fragmented, focusing on isolated benefits or risks without fully addressing their broader societal implications (Favero et al., 2026).

As we explore the future of education, it is essential to ensure that technological advancement does not come at the cost of the human elements that make learning meaningful. Ultimately, education should not aim to optimize learners for compliance with intelligent systems, but rather to equip them with the capacity to question, understand, and use these systems responsibly (Favero et al., 2026). When aligned with goals such as critical thinking, agency, and ethical awareness, AI has the potential to enhance education. When it is not, it risks narrowing it.

From a postplagiarism perspective, as articulated by Sarah Elaine Eaton (Eaton, 2023), AI-led education reflects a broader shift in how knowledge is produced, used, and evaluated. As hybrid human-AI work becomes normalized, the boundaries between student effort and technological assistance become increasingly blurred. At the same time, relinquishing greater control to AI systems does not eliminate human responsibility. Educators remain accountable for ensuring the accuracy, ethical use, and pedagogical value of these tools, raising important questions about what is lost when subject-matter expertise is de-emphasized. Moreover, as traditional definitions of plagiarism and authorship evolve, AI-led models challenge long-standing assumptions about originality and independent work. In this context, the integration of AI into education is not simply a technical shift, but a conceptual one that requires careful consideration of responsibility, agency, and the purpose of learning itself.

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Author bio: Iryna Pavlova is a member of the Postplagiarism Research Lab, University of Calgary

*ChatGPT was used to support the refinement of language and structure in this document. All content and perspectives are those of the author.

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References

Armanini, K. (2026, April 3). Teacher-free AI school to open in Chicago. Government Technology. https://www.govtech.com/education/k-12/teacher-free-ai-school-to-open-in-chicago

Eaton, S.E. (2023). Postplagiarism: Transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology. International Journal for Educational Integrity, 19(23). https://doi.org/10.1007/s40979-023-00144-1

Favero, L., Pérez-Ortiz, J. A., Käser, T., & Oliver, N. (2026). AI in Education beyond learning outcomes: Cognition, agency, emotion, and ethics. arXiv preprint arXiv:2602.04598. https://doi.org/10.48550/arXiv.2602.04598

Wang, X., Huang, R. T., Sommer, M., Pei, B., Shidfar, P., Rehman, M. S., Ritzhaupt, A. D., & Martin, F. (2024). The efficacy of artificial intelligence-enabled adaptive learning systems from 2010 to 2022 on learner outcomes: A meta-analysis. Journal of Educational Computing Research, 62(6), 1348–1383. https://doi.org/10.1177/07356331241240459