This study traces the evolution of computer-assisted language learning (CALL) by investigating published research articles (RAs) in four major CALL journals: ReCALL, Computer Assisted Language Learning, Language Learning & Technology, and CALICO Journal. All 2,397 RAs published over four decades (1983–2019) were included in the pool of data, and the Google Scholar citation metric was adopted to assess the impact of the papers. By selecting the top 15% of widely cited papers from each individual year, we minimized the time bias between years, enabling a balanced narration of the history of CALL through a representative dataset of 426 high-impact RAs. To identify the evolution of research trends, the contexts, methodologies, theoretical underpinnings and research foci of all 426 RAs were investigated using NVivo 12 and AntConc. The analysis of the data yielded encouraging results such as the upward trend in the number of publications and the international reach of CALL in the last two decades, the physical or virtual presence of language learners with diverse language profiles, and the growing tendency to triangulate methodology for increased complexity. However, long-standing issues such as the heavy reliance on traditional research contexts, poor reporting practices of basic demographic information, the large number of atheoretical papers and the concentration on a limited number of research foci continue to pose challenges in CALL research. Based on the findings, the paper suggests solutions for the controversies and addresses key issues for future research in CALL.

本研究通过调查四种主要 CALL 期刊（ 《ReCALL》、《计算机辅助语言学习》、《语言学习与技术》和《CALICO Journal》）上发表的研究文章 (RA) 来追踪计算机辅助语言学习 (CALL) 的演变。四十年来（1983-2019）发表的所有 2,397 篇 RA 均包含在数据池中，并采用 Google Scholar 引用指标来评估论文的影响力。通过从每一年中选择前 15% 的被广泛引用的论文，我们最大限度地减少了年份之间的时间偏差，从而通过 426 个高影响力 RA 的代表性数据集平衡地叙述了 CALL 的历史。为了确定研究趋势的演变，使用 NVivo 12 和 AntConc 对所有 426 个 RA 的背景、方法、理论基础和研究重点进行了调查。对数据的分析产生了令人鼓舞的结果，例如过去二十年中出版物数量和 CALL 的国际影响力呈上升趋势，具有不同语言特征的语言学习者的实际或虚拟存在，以及三角测量方法的日益增长的趋势以增加复杂性。然而，长期存在的问题，如对传统研究背景的严重依赖、基本人口信息报告实践不佳、大量非理论论文以及集中于有限数量的研究焦点等问题继续对 CALL 研究构成挑战。根据研究结果，本文提出了争议的解决方案，并解决了 CALL 未来研究的关键问题。