AI in Education: From the Myth of Replacement to the Reality of Teacher Empowerment

Abstract

Will AI replace teachers or enhance them? This article provides a practical, evidence-based view of how AI can improve teaching and learning—while understanding its limits. The central hypothesis is that AI is a powerful tool, but it does not replace educators. If you teach, design courses, or lead academic teams, here you will find clear insights, real risks, and concrete guidelines to decide how to use it effectively.

Introduction

The rise of generative AI has reignited a key question: what tasks should technology perform in the classroom, and which remain non-delegable for teachers? Our hypothesis is clear: AI is a valuable tool for education, but it does not necessarily replace a teacher. This article concisely and practically reviews the available evidence—its advantages, risks, and the specific role of virtual tutors—and concludes by contrasting findings with the initial hypothesis.

Advantages of Using Artificial Intelligence in Education

Personalization and tutoring. Intelligent tutoring systems improve performance when they follow best practices, such as providing scaffolding and adaptive feedback (Ma et al., 2014; Luckin et al., 2016).

Agile and frequent feedback. Generative models help create feedback drafts and alternative explanations, shortening the practice–reflection cycles (Holmes et al., 2019; UNESCO, 2023).

Accessibility and more resources. Format conversion, text simplification, and the generation of examples and task variations promote inclusion (OECD, 2021).

Time optimization. Automating standardizable tasks (question banks, initial rubrics, summaries, templates) frees time for high-value pedagogical interactions (UNESCO, 2023; Zawacki-Richter et al., 2019).

Promotion of metacognition. Comparing one’s reasoning with AI output and justifying discrepancies enhances critical thinking—if the activity is well designed (Kasneci et al., 2023; Holmes et al., 2019).

Disadvantages and Risks of Using Artificial Intelligence in Education

Errors, bias, and low auditability. AI may produce plausible but incorrect answers and reproduce biases present in its data (Bender et al., 2021; UNESCO, 2023).

Digital divide and equity. Unequal access to devices, connectivity, and digital skills can deepen educational inequalities (UNESCO, 2023).

Academic integrity. Unregulated use facilitates task delegation and undermines authentic assessment (Cotton et al., 2023).

Dependence and skill atrophy. Outsourcing reasoning without proper pedagogical design reduces deliberate practice and student agency (Selwyn, 2019; Bender et al., 2021).

Privacy and data. Transferring student information to third parties without safeguards violates rights and regulations (UNESCO, 2023; OECD, 2021).

Virtual Tutors in Education

Evidence and practice converge: AI is most effective when handling operational and repetitive processes, while teachers focus on human-centered work—guiding, contextualizing, deliberating, and cultivating judgment. Specifically, AI-based teaching assistants can:

• Pre-develop rubrics, checklists, and question banks that teachers then validate and calibrate (UNESCO, 2023; Zawacki-Richter et al., 2019).

• Generate draft feedback or alternative explanations for teachers to review and personalize (Holmes et al., 2019).

• Produce accessible materials (summaries, reading levels, glossaries) and differentiated versions of the same task (OECD, 2021).

• Systematize logistics (schedules, reminders, basic participation analysis) without interfering in substantive evaluation (UNESCO, 2023).

  
  

However, as Chomsky, Roberts, and Watumull (2023) point out, generative models neither understand nor deliberate; they predict plausible sequences. By design, they cannot:

• Assume ethical responsibility or decide what is right or wrong.

• Guarantee truthfulness or maintain an independent epistemological criterion.

• Replace teachers’ professional judgment, pedagogical content knowledge, or capacity to build community and meaning.

  
  

Conclusion

AI is a powerful tool that, when integrated with pedagogical intent, enhances personalization, feedback, efficiency, and accessibility. Yet its structural limitations (truthfulness, bias, lack of understanding and accountability) prevent it from replacing teachers. Where AI accelerates processes, educators provide what is essential: experience design, judgment, ethics, context, and community building.

In practice, the winning combination is clear: AI as scaffolding, teachers as guides. When tasks involve analyzing, comparing, arguing, and creating, AI can catalyze learning without replacing human reasoning. When design becomes routine, it fosters outsourcing and erodes student agency.

A responsible roadmap includes transparent policies, AI literacy, authentic tasks, iterative assessment, and documented, guided use. Thus, AI does not replace teachers—it empowers them. That is precisely what the evidence supports.

In summary, when AI aligns with the formative goal—learning to think, deliberate, and create—it accelerates what truly matters. Transversal competencies (critical thinking, problem-solving, ethical judgment) develop primarily through human interaction. AI can shorten the path, but pedagogical guidance and educational meaning remain the domain of teachers.

References

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT). https://doi.org/10.1145/3442188.3445922

Chomsky, N., Roberts, I., & Watumull, J. (2023, March 8). The False Promise of ChatGPT. The New York Times.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., et al. (2023). ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education. arXiv:2304.04232

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson.

Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent Tutoring Systems and Learning Outcomes: A Meta-Analysis. Journal of Educational Psychology, 106(4), 901–918. https://doi.org/10.1037/a0037123

OECD (2021). Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. OECD Publishing.

Selwyn, N. (2019). Should Robots Replace Teachers? AI and the Future of Education. Polity Press.

UNESCO (2023). Guidance for Generative AI in Education and Research. UNESCO Publishing.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic Review of Research on Artificial Intelligence Applications in Higher Education—Where Are the Educators? International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and Cheating: Ensuring Academic Integrity in the Era of ChatGPT. Innovations in Education and Teaching International. https://doi.org/10.1080/14703297.2023.2190148