Electronic dictionaries have largely replaced paper dictionaries and become central tools for L2 learners seeking to expand their vocabulary. Users often assume these resources are reliable and rarely question the validity of the definitions provided. The accuracy of major E-dictionaries is seldom scrutinized, and little attention has been paid to how their corpora are constructed. Research on dictionary use, particularly the limitations of electronic dictionaries, remains scarce. This study adopts a combined method of experimentation, user survey, and dictionary critique to examine Youdao, one of the most widely used E-dictionaries in China. The experiment involved a translation task paired with retrospective reflection. Participants were asked to translate sentences containing words that are insufficiently or inaccurately defined in Youdao. Their consultation behavior was recorded to analyze how faulty definitions influenced comprehension. Results show that incomplete or misleading definitions can cause serious misunderstandings. Additionally, students exhibited problematic consultation habits. The study further explores how such flawed definitions originate, highlighting issues in data processing and the integration of AI and machine learning technologies in dictionary construction. The findings suggest a need for better training in dictionary literacy for users, as well as improvements in the underlying AI models used to build E-dictionaries.

翻译：电子词典已基本取代纸质词典，成为第二语言学习者扩充词汇的核心工具。用户通常认为这些资源可靠，极少质疑所提供释义的有效性。主流电子词典的准确性很少受到严格审查，其语料库的构建方式也鲜有关注。关于词典使用的研究，特别是电子词典的局限性，仍然十分匮乏。本研究采用实验、用户调查与词典评析相结合的方法，考察中国使用最广泛的电子词典之一——有道词典。实验包含一项翻译任务及回溯性反思。参与者被要求翻译包含有道词典中释义不充分或不准确的词汇的句子。通过记录其查询行为，分析了错误释义如何影响理解。结果表明，不完整或误导性释义会导致严重的误解。此外，学生表现出有问题的查询习惯。研究进一步探讨了此类缺陷释义的成因，突出了数据处理以及人工智能与机器学习技术在词典构建中的应用所存在的问题。研究结果提示，需要对用户进行更好的词典素养培训，并改进用于构建电子词典的底层人工智能模型。