1.2 Limitations of Traditional AI in Education
Despite technical advances, current AI solutions have several limitations in addressing global educational challenges:
Limitations of Centralized Structures
Scalability Challenges: Centralized control of AI models by a single entity introduces bias in data and decision-making, limiting adaptability to diverse user needs and posing significant barriers to global scalability.
Data Sovereignty Issues: Centralized AI systems raise data sovereignty and privacy concerns, especially as educational data reveals sensitive insights into personal growth and achievement.
Difficulty Localizing: A single AI model struggles to accommodate diverse educational systems, cultural contexts, and linguistic characteristics needs, as centralized decision-making restricts the creation of region-specific solutions.
Constraints on Innovation and Value Distribution
Innovation Barriers: Closed systems limit participation from diverse educational experts, small startups, and individuals, slowing the advancement of educational AI development.
Unequal Value Distribution: In the educational ecosystem, contributors such as data providers, creators, and teachers often lack fair compensation, while benefits remain concentrated among a few entities.
Commercialization Bias: Development efforts prioritize commercially attractive education sectors, sidelining socially important but less commercial domains.
In response to these challenges, XIIID proposes a blockchain-based decentralized AI platform to achieve global scalability, foster innovation, and ensure equitable value distribution.
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