arXiv:2606.30662v1 Announce Type: cross Abstract: The advent of Generative Artificial Intelligence (GenAI), and in particular Large Language Models (LLMs), is reshaping educational practice, while intensifying ethical debate about its adoption. To date, the dominant paradigm remains cloud-based and text-only chatbot: a centralized service that offers limited pedagogical control, weak transparency over knowledge sources, and non-trivial risks for privacy and regulatory compliance. This model also presumes continuous connectivity and recurring API costs, creating structural barriers for many ins
The rapid advancement of GenAI, particularly LLMs, is pushing the educational sector to confront both the opportunities and significant ethical, privacy, and accessibility challenges of current cloud-based models.
This paper highlights a critical design flaw in current GenAI educational tools, specifically their centralized, text-only nature, and proposes an alternative human-centered approach that could democratize access and control.
The focus shifts from general-purpose, remote GenAI tutors to human-centered, potentially decentralized, and pedagogically controlled virtual tutors that address privacy, transparency, and accessibility concerns.
- · Educational institutions
- · Students in underserved areas
- · Open-source AI developers
- · Local AI infrastructure providers
- · Large centralized GenAI platforms
- · Cloud service providers (for education-specific applications)
- · Traditional textbook publishers
- · Subscription-based online learning platforms
The adoption of human-centered, privacy-preserving GenAI tutors could lead to a significant increase in personalized, scalable educational opportunities.
This decentralization and focus on local control could spur the development of edge AI solutions and specialized, domain-specific AI models for education.
Reduced reliance on large, external AI infrastructures for education could enhance national digital sovereignty in learning, potentially leading to varied pedagogical approaches globally.
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Read at arXiv cs.AI