| dc.description.abstract | In response to the surge of artificial intelligence in the era of big data, programming and data analysis skills have become some of the most lacking practical abilities in the industry. During the teaching process, it was found that teachers face numerous challenges in developing students′ practical programming skills for the industry, such as being unable to determine whether students effectively absorb the knowledge taught in class, the time-consuming task of designing test questions for students of different levels, and the inability to provide tailored learning recommendations for students′ knowledge deficiencies.
To address these issues in the teaching field, this research intends to introduce a developed adaptive spaced learning system. This system uses an adaptive question-pushing mechanism supplemented by knowledge tracking technology to provide students with suitable learning content, aiming to improve students′ learning outcomes and help teachers understand students′ learning statuses. The system will integrate generative AI technologies, such as automatic summarization, automatic question generation, and role-playing functions. In particular, this research will have the generative AI model ChatGPT attempt to act as a Python programming course teacher, generating questions automatically (Automatic Question Generation, AQG) to reduce the burden on teachers in designing test questions. Through knowledge tracking technology, each question will be marked with knowledge points, and based on students′ answers, the difficulty and content of subsequent questions will be automatically adjusted to achieve adaptive learning.
The results of this study demonstrate that the adaptive learning platform effectively reduces the burden on teachers, enhances student learning outcomes, and fosters interaction between teachers and students. The questions automatically generated by ChatGPT are of similar quality to those created by teachers, confirming the feasibility of using ChatGPT in a teaching role. By integrating a knowledge-tracking adaptive question recommendation mechanism, the system not only makes student learning more efficient but also allows teachers to accurately understand students′ learning progress. This enables targeted teaching adjustments, which is significant for the integration of generative artificial intelligence and modern education. | en_US |