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Attention Is All You Need: Transforming Educational AI with Self-Attention Mechanisms
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.
Large Language Models as Educational Tutors: A Comprehensive Evaluation
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.
Federated Learning for Privacy-Preserving Educational Analytics
We introduce a federated learning framework that enables educational institutions to collaboratively train AI models while preserving student privacy and data sovereignty.
Multimodal AI for Adaptive Learning: Combining Vision, Audio, and Text
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.
Ethical Considerations in AI-Powered Educational Assessment
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.