From ‘One Teacher’ to ‘Personal Tutor for Every Student’

Education inherently requires personalization. However, the current education system has been designed with a limited number of teachers, a fixed curriculum, and a uniform pace. As a result, learning gaps have structurally developed, and supplementary learning has always been reactive. The advent of AI-based tutoring is changing this very structure. Education is no longer a system focused on achieving averages, but rather one that tracks individual learning in real time.

The market need: Not "more content," but "instructions tailored to me."

The reason the education industry is focusing on AI isn't because of a lack of content. There's already an abundance of lectures, problems, and materials. The problem is this:

• It is difficult for a teacher to immediately know where a student is stuck.
• Even if the same explanation is repeated, the speed of understanding varies from student to student.
• It is difficult to detect in advance the moment when learning motivation declines.

This is where AI tutoring comes into play. The key isn't "teaching," but the ability to continuously assess understanding.

Solution: Real-time tutoring based on learning behavior data.

AI tutors aren't tools that provide the right answers. They're systems that interpret the numerous signals generated during the learning process and determine what interventions are needed for each student. Some representative examples of their implementation include:

1. Automatically designing personalized learning paths: AI analyzes problem-solving time, error patterns, and the number of retries to adjust learning paths. Some students are presented with conceptual explanations, while others are presented with application problems first.
2. Real-time feedback and hints: Provide immediate hints or explain alternative approaches when students get stuck. This system differs from simple grading systems in that it tracks the student's "thought process."
3. Providing Learning Insights for Teachers: AI tutors won't replace teachers. Rather, they will enhance the quality of instruction by providing teachers with summarized data on individual student understanding, risk signals, and intervention points.

Challenges from a technical, operational, and ethical perspective

The introduction of AI tutoring is both a technology project and a question of educational philosophy.

• Technical challenges: Accuracy of training data, unbiased recommendation logic, and explainability are important.
• Operational Challenge: The key is how to connect the existing curriculum with the AI system.
• Ethics and Security Issues: Minor learning data is among the most sensitive personal information. The purpose of data collection and the scope of its use must be clear.

Iropke's perspective: AI tutoring is about "scaling," not "automation."

Iropke doesn't view AI tutoring as a technology that replaces teachers. Rather, he interprets it as a structural device that expands teachers' influence. AI handles repetitive explanations and diagnoses, allowing teachers to focus on critical thinking, discussion, and emotional care. Ultimately, the quality of education depends not on the technology itself, but on how it is designed and controlled. When properly designed, AI tutoring becomes a tool that approaches the essence of education as closely as possible.