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<p>As artificial intelligence technology is increasingly applied in the field of education, issues such as low efficiency in answering questions, insufficient personalized support, and weak interactive feedback mechanisms in traditional middle school chemistry teaching urgently need to be addressed. This paper describes a robot-assisted online Q&amp;A and interaction system for chemistry, designed and implemented on the Chaoxing platform. The system integrates intelligent Q&amp;A, personalized recommendations, and human-computer interaction. It uses a front-end and back-end separation architecture, incorporating natural language processing models like BERT and T5 to accurately understand student questions and generate responses. Personalized recommendation strategies are built based on user behavior data, dynamically adjusting learning paths. Interactive mechanisms such as comments, likes, and peer collaboration enhance communication efficiency between teachers and students, as well as among students. A total of 488 student questionnaires were distributed, with 453 valid responses collected from 8 middle schools in the eastern and central regions. Additionally, 9 chemistry teachers participated in interviews and system trials, and 50 students underwent a one-week functional test of the system. The test results showed that the platform operated stably, with a core module function pass rate of 94.3%, an average response time of less than 2&#xa0;s, and a recommendation module click-through rate of 71.6%. Empirical results indicate that the system significantly increased students’ initiative to ask questions and their participation in learning, reduced the repetitive burden of answering questions for teachers, and enhanced the relevance and intelligence of teaching. The study demonstrates that the system not only has significant auxiliary value in teaching but also provides a replicable practical path for integrating artificial intelligence into middle school chemistry teaching.</p>

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Design and implementation of robot assisted chemistry online Q&A and interactive system supported by Chaoxing platform

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月5日

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  • Linghua Chen

摘要

As artificial intelligence technology is increasingly applied in the field of education, issues such as low efficiency in answering questions, insufficient personalized support, and weak interactive feedback mechanisms in traditional middle school chemistry teaching urgently need to be addressed. This paper describes a robot-assisted online Q&A and interaction system for chemistry, designed and implemented on the Chaoxing platform. The system integrates intelligent Q&A, personalized recommendations, and human-computer interaction. It uses a front-end and back-end separation architecture, incorporating natural language processing models like BERT and T5 to accurately understand student questions and generate responses. Personalized recommendation strategies are built based on user behavior data, dynamically adjusting learning paths. Interactive mechanisms such as comments, likes, and peer collaboration enhance communication efficiency between teachers and students, as well as among students. A total of 488 student questionnaires were distributed, with 453 valid responses collected from 8 middle schools in the eastern and central regions. Additionally, 9 chemistry teachers participated in interviews and system trials, and 50 students underwent a one-week functional test of the system. The test results showed that the platform operated stably, with a core module function pass rate of 94.3%, an average response time of less than 2 s, and a recommendation module click-through rate of 71.6%. Empirical results indicate that the system significantly increased students’ initiative to ask questions and their participation in learning, reduced the repetitive burden of answering questions for teachers, and enhanced the relevance and intelligence of teaching. The study demonstrates that the system not only has significant auxiliary value in teaching but also provides a replicable practical path for integrating artificial intelligence into middle school chemistry teaching.