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<p>The limited in-person availability of administrative services at higher education institutions can delay the resolution of student queries and reduce satisfaction levels. To address this issue, we developed a conversational agent capable of understanding and responding to student questions in Portuguese using natural language processing and machine learning techniques. To enable non-technical management of the agent’s knowledge base, a web-based service was implemented, allowing staff to update content and trigger model retraining. The system was evaluated by comparing multiple learning models, with the best performance achieved using Google’s BERT language model combined with the DIET classifier, yielding an <i>F</i>1-score of 0.965. In a real-world deployment involving 256 questions, the chatbot achieved approximately 70% accuracy and received an average user satisfaction rating of 4.20 on a 0–5 scale. These results demonstrate the effectiveness of the proposed solution for improving accessibility and efficiency in academic student services.</p>

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ChatBot for student service based on RASA framework

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Fátima Rodrigues,
  • João Fonseca

摘要

The limited in-person availability of administrative services at higher education institutions can delay the resolution of student queries and reduce satisfaction levels. To address this issue, we developed a conversational agent capable of understanding and responding to student questions in Portuguese using natural language processing and machine learning techniques. To enable non-technical management of the agent’s knowledge base, a web-based service was implemented, allowing staff to update content and trigger model retraining. The system was evaluated by comparing multiple learning models, with the best performance achieved using Google’s BERT language model combined with the DIET classifier, yielding an F1-score of 0.965. In a real-world deployment involving 256 questions, the chatbot achieved approximately 70% accuracy and received an average user satisfaction rating of 4.20 on a 0–5 scale. These results demonstrate the effectiveness of the proposed solution for improving accessibility and efficiency in academic student services.