The integration of artificial intelligence (AI) in education is reshaping English language teaching (ELT), redefining the 'assistant' function of technology and the 'interaction' roles of language instructors. This study specifically investigates ins...
When the solution domain, internal parameters, and initial and boundary conditions of partial differential equation (PDE) are changed, many potential characteristics of the equation's solutions are still similar. This provides the possibility to redu...
Prior research on student achievement has typically examined isolated factors or bivariate correlations, failing to capture the complex interplay between learning behaviors, pedagogical environments, and instructional design. This study addresses the...
This research explores the potential of combining Meta Reinforcement Learning (MRL) with Spike-Timing-Dependent Plasticity (STDP) to enhance the performance and adaptability of AI agents in Atari game settings. Our methodology leverages MRL to swiftl...
As generative artificial intelligence (AI) rapidly transforms educational landscapes, understanding its impact on students' core competencies has become increasingly critical for educators and policymakers. Despite growing integration of AI technolog...
In human error-based learning, the size and direction of a scalar error (i.e., the "directed error") are used to update future actions. Modern deep reinforcement learning (RL) methods perform a similar operation but in terms of scalar rewards. Despit...
In this issue of Neuron, Chiappa et al. describe how neural networks can be trained to perform complex hand motor skills. A key to their approach is curriculum learning, breaking learning into stages, leading to good control.
Journal of the American Medical Informatics Association : JAMIA
Aug 1, 2024
The integration of large language models (LLMs) like ChatGPT into medical education presents potential benefits and challenges. These technologies, aligned with constructivist learning theories, could potentially enhance critical thinking and problem...
In computational neuroscience, recurrent neural networks are widely used to model neural activity and learning. In many studies, fixed points of recurrent neural networks are used to model neural responses to static or slowly changing stimuli, such a...
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