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We propose a novel architecture for educational AI systems based purely on attention mechanisms, dispensing with recurrence and convolutions entirely. Our model achieves superior performance on personalized learning tasks while being more parallelizable and requiring significantly less time to train.
This paper presents the first large-scale evaluation of LLMs as educational tutors across multiple subjects and grade levels. We analyze performance, bias, and learning outcomes in real educational settings.
We introduce a federated learning framework that enables educational institutions to collaboratively train AI models while preserving student privacy and data sovereignty.
This work explores the integration of computer vision, speech recognition, and natural language processing to create adaptive learning experiences that respond to multiple input modalities.
We examine the ethical implications of using AI for student assessment, including bias, fairness, transparency, and the impact on student well-being and learning outcomes.