Artificial grammar learning (AGL) is an experimental paradigm frequently adopted to investigate the unconscious and conscious learning and application of linguistic knowledge. This paper will introduce ENIGMA (https://enigma-lang.org) as a free, flexible, and lightweight Web-based tool for running online AGL experiments. The application is optimized for desktop and mobile devices with a user-friendly interface, which can present visual and aural stimuli and elicit judgment responses with RT measures. Without limits in time and space, ENIGMA could help collect more data from participants with diverse personal and language backgrounds and variable cognitive skills. Such data are essential to explain complex factors influencing learners’ performance in AGL experiments and answer various research questions regarding L1/L2 acquisition. The introduction of the core features in ENIGMA is followed by an example study that partially replicated Chen (Lang Acquis 27(3):331–361, 2020) to illustrate possible experimental designs and examine the quality of the collected data.

人工语法学习（AGL）是一种经常采用的实验范式，用于研究语言知识的无意识和有意识的学习和应用。本文将介绍 ENIGMA (https://enigma-lang.org)，它是一个免费、灵活且轻量级的基于 Web 的工具，用于运行在线 AGL 实验。该应用程序针对桌面和移动设备进行了优化，具有用户友好的界面，可以呈现视觉和听觉刺激，并通过 RT 测量引发判断反应。 ENIGMA 不受时间和空间的限制，可以帮助从具有不同个人和语言背景以及不同认知技能的参与者收集更多数据。这些数据对于解释影响学习者在 AGL 实验中表现的复杂因素以及回答有关 L1/L2 习得的各种研究问题至关重要。在介绍 ENIGMA 的核心功能之后，接下来是部分复制 Chen 的示例研究（Lang Acquis 27(3):331–361, 2020），以说明可能的实验设计并检查所收集数据的质量。