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Titlebook: Generative Intelligence and Intelligent Tutoring Systems; 20th International C Angelo Sifaleras,Fuhua Lin Conference proceedings 2024 The E

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樓主: 恐怖
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發(fā)表于 2025-3-23 09:41:49 | 只看該作者
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發(fā)表于 2025-3-23 15:50:30 | 只看該作者
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發(fā)表于 2025-3-23 21:20:40 | 只看該作者
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發(fā)表于 2025-3-23 23:17:03 | 只看該作者
Alex M. Greenberg,Joachim Preinsment in real-world learning scenarios. Through our detailed analysis of the QuizMaster architecture, we demonstrate how to leverage reinforcement learning and generative intelligence in the development of systems for formative assessment.
15#
發(fā)表于 2025-3-24 04:18:12 | 只看該作者
16#
發(fā)表于 2025-3-24 08:18:53 | 只看該作者
Using Large Language Models to?Support Teaching and?Learning of?Word Problem Solving in?Tutoring Systerms of their ability to provide the correct solution for specific conceptual schemes. Beyond their potential as a problem-solving tool, the research presented opens the door to using LLMs for the implementation of virtual agent-based students.
17#
發(fā)表于 2025-3-24 14:35:12 | 只看該作者
18#
發(fā)表于 2025-3-24 17:53:45 | 只看該作者
SAMI: An AI Actor for?Fostering Social Interactions in?Online Classrooms” felt by the students in the community of online students. SAMI has been deployed at Georgia Institute of Technology in several online classes with over 11000 students in the past two years. We describe our findings from student surveys to gauge SAMI’s effectiveness.
19#
發(fā)表于 2025-3-24 19:51:53 | 只看該作者
Using Large Language Models to?Support Teaching and?Learning of?Word Problem Solving in?Tutoring Sys-solving. In this paper, we examine the potential of a large variety of open models for solving different types of arithmetical problems and discuss the potential implications for the development of Intelligent Tutoring Systems (ITSs). The results reported show that relatively small LLMs are able to
20#
發(fā)表于 2025-3-25 02:29:54 | 只看該作者
A Generative Approach for?Proactive Assistance Forecasting in?Intelligent Tutoring Environmentsng effectiveness. However, numerous studies on student behavior have revealed that they may not consistently utilize help-seeking functions. Deciding when a system should assist students during the dynamic learning process poses a challenge. We propose a new approach called Transformer4HELP, which e
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