创新创业理论研究与实践 ›› 2025, Vol. 8 ›› Issue (19): 12-14.

• 理论研究 • 上一篇    下一篇

数智赋能高等学校外语口语考试创新研究:理论、实践与挑战

李桃桃1, 王环宇2, 张健3, 丁建华1   

  1. 1.唐山师范学院 国际交流中心,河北唐山 063009;
    2.唐山师范学院 外国语学院,河北唐山 063009;
    3.唐山师范学院 教育学院,河北唐山 063009
  • 出版日期:2025-10-10 发布日期:2025-11-17
  • 作者简介:李桃桃(1981—),女,河北唐山人,硕士研究生,讲师,研究方向:英语语言文学。
  • 基金资助:
    2024年度河北省教育考试招生研究立项课题“数智赋能高等学校外语口语考试研究”(HBJK2024126)

Digital Intelligence Empowering Innovation in Foreign Language Oral Proficiency Testing in Higher Education: Theory, Practice, and Challenges

LI Taotao1, WANG Huanyu2, ZHANG Jian3, DING Jianhua1   

  1. 1. International Exchange Centre, Tangshan Normal University, Tangshan Hebei, 063009, China;
    2. School of Foreign Languages, Tangshan Normal University, Tangshan Hebei, 063009, China;
    3. School of Education, Tangshan Normal University, Tangshan Hebei, 063009, China
  • Online:2025-10-10 Published:2025-11-17

摘要: 该文聚焦数智技术对高等学校外语口语考试的赋能作用,针对传统高校外语口语考试存在的评分主观性强、反馈延迟、考核维度单一、缺乏个性化反馈等问题,结合多学科理论基础,基于自然语言处理、语音识别和生成式人工智能技术的智能评价工具,构建了数字化背景下外语口语考试的新模式;通过对比实验,证明数智技术在优化外语口语考试流程、提升考试质量与公平性以及增强学生口语能力培养效果方面成效显著,验证了“人机协同”评估模式的有效性,为高等学校外语口语考试改革提供了重要的理论与实践参考,为教育数字化转型提供了可复制的实践范式。

关键词: 数智技术, 外语口语考试, 智能评价工具, 人机协同, 教育数字化转型, 考试公平性

Abstract: This study focuses on the empowering role of digital intelligence technologies in foreign language oral proficiency testing in higher education. Addressing the limitations of traditional testing methods—such as subjective scoring, delayed feedback, narrow assessment dimensions, and lack of personalized feedback—it integrates multidisciplinary theoretical foundations and leverages intelligent assessment tools based on natural language processing, speech recognition, and generative artificial intelligence. The research constructs a novel model for foreign language oral testing in the digital era. Comparative experiments demonstrate that digital intelligence technologies significantly optimize testing procedures, enhance test quality and fairness, and improve the effectiveness of students'oral proficiency development. The study validates the efficacy of the“human-computer collaboration”evaluation model, providing critical theoretical and practical insights for reforming oral language testing in higher education and offering a replicable paradigm for digital transformation in education.

Key words: Digital intelligence technologies, Foreign language oral proficiency testing, Intelligent assessment tools, Human-computer collaboration, Digital transformation in education, Test fairness

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