Education Society
Researchers Examine Challenges Towards a General AI Tutor
AI is already envisioned as a personal home tutor, but a recent review article reminds us that the journey is still long. Despite decades of attempts, a general AI tutor suitable for everyone has not materialized, and new questions are now emerging with the advent of large language models.
Estonian AI researchers Jaan Aru and Kristjan-Julius Laak examine the problems that arise if such a tutor is to be built as a nationwide system. The underlying idea is of a 'general personal tutor' that would follow a student throughout their educational path and adapt teaching to individual needs.
The researchers do not present a ready-made system but compile practical questions raised by the development of a national AI tutor. Through these questions, they demonstrate that scientific understanding of the learning process itself is still lacking. For example, it is unclear how AI could best support motivation, deep understanding, or long-term practice in different learner groups.
The article emphasizes that large language models — current AI systems capable of conversation — could be groundbreaking, but their use in education requires more than just technical fine-tuning. First, it must be determined what good learning is, how it is measured, and how AI can support those aspects of learning that we do not yet fully understand.
Thus, the research sets the framework for discussion: a general AI tutor may be an attractive vision, but its implementation quickly encounters fundamental questions of learning that science has not yet answered.
Source: Developing a General Personal Tutor for Education, ArXiv (AI).
Estonian AI researchers Jaan Aru and Kristjan-Julius Laak examine the problems that arise if such a tutor is to be built as a nationwide system. The underlying idea is of a 'general personal tutor' that would follow a student throughout their educational path and adapt teaching to individual needs.
The researchers do not present a ready-made system but compile practical questions raised by the development of a national AI tutor. Through these questions, they demonstrate that scientific understanding of the learning process itself is still lacking. For example, it is unclear how AI could best support motivation, deep understanding, or long-term practice in different learner groups.
The article emphasizes that large language models — current AI systems capable of conversation — could be groundbreaking, but their use in education requires more than just technical fine-tuning. First, it must be determined what good learning is, how it is measured, and how AI can support those aspects of learning that we do not yet fully understand.
Thus, the research sets the framework for discussion: a general AI tutor may be an attractive vision, but its implementation quickly encounters fundamental questions of learning that science has not yet answered.
Source: Developing a General Personal Tutor for Education, ArXiv (AI).
This text was generated with AI assistance and may contain errors. Please verify details from the original source.
Original research: Developing a General Personal Tutor for Education
Publisher: ArXiv (AI)
Authors: Jaan Aru, Kristjan-Julius Laak
December 23, 2025
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