移动优化的人工智能驱动的个性化学习:穆罕默德六世理工大学案例研究

Khalid Baba, N. E. Faddouli, Nicolas Cheimanoff
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引用次数: 0

摘要

随着移动学习平台的兴起,人们越来越清楚地认识到,个人需要针对移动设备的优势和局限性量身定制的个性化体验。本研究探讨了由人工智能(AI)驱动的个性化移动学习环境可能产生的重大影响。本研究专门评估了为移动设备设计的人工智能驱动的个性化教育平台对穆罕默德六世理工大学学生的学业成绩和教育进展的影响。该平台专为移动设备设计,允许教师轻松上传信息。学员可以通过聊天界面与人工智能导师进行互动,该界面已无缝集成到他们的移动课程材料中。该系统采用了 Langchain、Pinecone 和 LLM 模型等尖端技术,能够为经常移动的学习者提供个性化的实时反馈和支持。这项研究对两组学生进行了比较。其中一组可以使用由人工智能驱动的移动个性化学习平台,而对照组则无法使用。我们对这两组学生的移动教育体验、参与程度和学习成绩进行了比较分析。此外,我们还收集了教育工作者和学生的定性反馈,以评估该系统的移动可用性和有效性。我们的研究结果表明,人工智能驱动的移动定制学习系统显著改善了移动学习者的体验。参与程度的提高、理解能力的增强以及优异的学习成绩都支持了我们的观点。这项研究不仅证明了人工智能驱动的个性化移动学习在高等教育中的潜力,还强调了不断创新以提高其实用性和有效性的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mobile-Optimized AI-Driven Personalized Learning: A Case Study at Mohammed VI Polytechnic University
With the rise of mobile learning platforms, it has become increasingly evident that individuals require personalized experiences that are tailored to the strengths and limitations of mobile devices. The present study explores the significant impact that personalized mobile learning environments, powered by artificial intelligence (AI), could have. This study specifically evaluates the impact of an AI-driven personalized educational platform, designed for mobile devices, on the academic achievement and educational progress of students at Mohammed VI Polytechnic University. The platform, designed for mobile devices, allows instructors to easily upload information. Learners can interact with an AI mentor through a chat interface that is seamlessly integrated into their mobile course materials. The system, constructed using cutting-edge technologies such as Langchain, Pinecone, and the LLM Model, excels at providing personalized, real-time feedback and support for learners who are frequently mobile. This study compared two groups of students. One group had access to a mobile personalized learning platform powered by AI, whereas the control group did not have access to it. We conducted a comparative analysis of mobile educational experiences, levels of engagement, and academic outcomes across these groups. In addition, qualitative feedback was gathered from educators and students to evaluate the mobile usability and effectiveness of the system. The results of our study demonstrate that the AI-driven mobile-tailored learning system significantly improves the experience of mobile learners. The increased levels of engagement, improved understanding, and superior academic achievements support our claim. This study not only supports the potential of AI-driven personalized mobile learning in higher education but also emphasizes the importance of continuous innovation to improve its usefulness and effectiveness.
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