利用5G和人工智能技术增强实时英语学习

IF 0.5 Q4 TELECOMMUNICATIONS
Xueqin Wang
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引用次数: 0

摘要

5G和人工智能(AI)技术的融合为实现更快、更灵敏、更个性化的教育体验,彻底改变实时英语学习提供了一个强大的机会。这些限制阻碍了学习者的参与,降低了语言习得的整体有效性,特别是在实时交流场景中。为了克服这些挑战,本文提出了一种称为智能实时语言增强系统(SRLES)的新框架。该框架将5g连接与人工智能驱动的工具(如语音识别、自然语言处理(NLP)和实时错误检测)集成在一起。SRLES框架采用深度学习模型,特别是循环和基于变压器的架构,用于语音识别和自适应反馈。它们有时与基于规则的组件集成,以进行上下文微调,形成混合方法。SRLES的实验实施显示了学习者成果的显著改善,包括实时沟通准确性提高了30%,学习者保留率提高了40%。此外,用户对口语技能的满意度和信心也有所提高。这些结果突出了5G和人工智能相结合在创造适应性强、高效、吸引人的英语学习环境方面的有效性。在12周的时间里,研究人员使用了120名不同年龄段和教育背景的参与者的数据集,测量了93.43%的学习者保留率和96.25%的沟通准确率。度量来源于使用日志、交互成功率和后续评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leveraging 5G and AI Technologies to Enhance Real-Time English Language Learning

The integration of 5G and Artificial Intelligence (AI) technologies offers a powerful opportunity to revolutionize real-time English language learning by enabling faster, more responsive, and personalized educational experiences. These limitations hinder learner engagement and reduce the overall effectiveness of language acquisition, particularly in real-time communication scenarios. To overcome these challenges, this paper proposes a novel framework called the Smart Real-Time Language Enhancement System (SRLES). This framework integrates 5G-enabled connectivity with AI-driven tools such as speech recognition, natural language processing (NLP), and real-time error detection. The SRLES framework employs deep learning models, particularly recurrent and transformer-based architectures, for speech recognition and adaptive feedback. These are sometimes integrated with rule-based components for contextual fine-tuning, forming a hybrid approach. Experimental implementation of SRLES showed a significant improvement in learner outcomes, including a 30% increase in real-time communication accuracy and a 40% boost in learner retention rates. Additionally, users reported greater satisfaction and confidence in speaking skills. These results highlight the effectiveness of combining 5G and AI in creating an adaptive, efficient, and engaging English language learning environment. The 93.43% learner retention rate and 96.25% communication accuracy were measured over a 12-week period using a dataset of 120 participants across diverse age groups and educational backgrounds. Metrics were derived from usage logs, interaction success rates, and follow-up assessments.

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