| 摘要(英) |
Since the launch of ChatGPT, its real-time response capabilities have showcased the potential of artificial intelligence in the education sector, especially in answering questions, providing academic advice, and assisting with data retrieval. However, its application in education still faces many challenges, such as passive responses, generic generated content, lack of specificity, and inability to integrate into teachers′ teaching experiences. Therefore, we created an educational agent crafting system called the Educational Agent Crafting Tool (EduACT) to allow teachers to incorporate their teaching experiences into their own educational agents. This study focuses on improving and expanding the EduACT system, particularly the process of creating chatbots and supplementing teaching tasks.
Firstly, this study designed a new agent creation method (Agent Builder) that guides teachers through real-time chat and interaction, enabling them to create their conversational agents step-by-step. Additionally, the system supports automated dialogue testing, significantly improving the creation process′s efficiency and helping creators identify design issues. Secondly, according to experiments, 11.3% of dialogue scenarios in the system lacked suitable tasks, so we designed dynamic tasks to reduce the scenarios with no available tasks to 6.7%. The prompts for dynamic tasks vary based on each agent′s task goals, generating more precise and personalized responses for users.
To evaluate whether the responses after selecting dynamic tasks were better, we assessed the differences between responses generated by unsuitable tasks and those generated by dynamically designed tasks using two methods. The results indicated that manually designed dynamic tasks were insufficient to handle different agent dialogue scenarios. In contrast, dynamically generated tasks produced responses that were superior in 64% of cases compared to unsuitable tasks. In 16% of dialogue data, the response quality far exceeded the aforementioned tasks, demonstrating the flexibility and effectiveness of dynamic tasks in supplementing dialogues.
In conclusion, this study contributes a new methodological framework for designing and applying educational chatbots. This framework emphasizes the importance of user-friendliness and system flexibility, providing valuable experiences and references for future research and practice in related fields. |
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