The exponential growth of question answering (QA) has made it an indispensable topic in any Natural Language Processing (NLP) course. Additionally, the breadth of QA derived from this exponential growth makes it an ideal scenario for teaching related NLP topics such as information retrieval, explainability, and adversarial attacks among others. In this paper, we introduce UKP-SQuARE as a platform for QA education. This platform provides an interactive environment where students can run, compare, and analyze various QA models from different perspectives, such as general behavior, explainability, and robustness. Therefore, students can get a first-hand experience in different QA techniques during the class. Thanks to this, we propose a learner-centered approach for QA education in which students proactively learn theoretical concepts and acquire problem-solving skills through interactive exploration, experimentation, and practical assignments, rather than solely relying on traditional lectures. To evaluate the effectiveness of UKP-SQuARE in teaching scenarios, we adopted it in a postgraduate NLP course and surveyed the students after the course. Their positive feedback shows the platform's effectiveness in their course and invites a wider adoption.

翻译：问答（QA）领域的指数级增长使其成为任何自然语言处理（NLP）课程中不可或缺的主题。此外，由这一增长带来的问答广度，使其成为教授信息检索、可解释性、对抗性攻击等相关NLP主题的理想场景。本文介绍了UKP-SQuARE这一用于问答教育的平台。该平台提供交互式环境，学生可从通用行为、可解释性和鲁棒性等不同视角运行、比较并分析各类问答模型。因此，学生能够在课堂上亲身体验不同的问答技术。基于此，我们提出了一种以学习者为中心的问答教育方法：学生通过交互式探索、实验和实践任务主动学习理论知识并获得问题解决能力，而非仅依赖传统授课。为评估UKP-SQuARE在教学场景中的有效性，我们在研究生NLP课程中采用了该平台，并在课程结束后对学生进行了调查。学生的积极反馈验证了该平台在课程中的有效性，并为其更广泛的应用提供了支持。