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This paper introduces echoQuiz, an open-source, AI-supported Audience Response System (ARS) designed for synchronous university (online) teaching with open-ended questions. The system follows a two-phase interaction model: In the quiz phase, students/learners submit their responses and then rate their peers’ responses. In the echo phase, the instructor highlights one response for group reflection, with all responses remaining anonymous. To ease the interpretation of open responses, the lecturer can be assisted by an AI system during live sessions. Developed with an Educational Design Research (EDR) approach, echoQuiz was piloted in synchronous university courses with a total of 62 participants. Survey results show high motivation and moderate perceived learning gains. The findings suggest that free-text interaction, supported by AI, can enhance engagement and adaptability in digital classrooms.

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Enhancing Synchronous Collaborative Learning with AI-Supported Audience Response Systems: The EchoQuiz Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Benedikt Brünner,
  • Martin Ebner

摘要

This paper introduces echoQuiz, an open-source, AI-supported Audience Response System (ARS) designed for synchronous university (online) teaching with open-ended questions. The system follows a two-phase interaction model: In the quiz phase, students/learners submit their responses and then rate their peers’ responses. In the echo phase, the instructor highlights one response for group reflection, with all responses remaining anonymous. To ease the interpretation of open responses, the lecturer can be assisted by an AI system during live sessions. Developed with an Educational Design Research (EDR) approach, echoQuiz was piloted in synchronous university courses with a total of 62 participants. Survey results show high motivation and moderate perceived learning gains. The findings suggest that free-text interaction, supported by AI, can enhance engagement and adaptability in digital classrooms.