Abstract
Interdisciplinary knowledge sharing is a crucial component of higher education. The use of asynchronous online discussion forums as a medium for fostering interdisciplinary knowledge sharing is effective, as it allows for the sharing, posting, and reflecting of information among learners outside of traditional classroom settings. However, the sheer volume of posts in such forums can threaten the quality of discussion and the ability of instructors to provide timely evaluations. To address this issue, an automated post-rating system has been developed utilizing a BERT-based AI model. This system evaluates learners' posts and provides prompt categorization outcomes into three categories: non-informative, informative, and neutral, within 10 seconds. Our model demonstrated appropriate accuracy in assessing the information density in forum posts, indicating potential benefits for both learners and instructors. Specifically, it achieved 67%, 68%, and 75% accuracy rates for posts categorized as discussion, comment, and reply, respectively. To assess the effectiveness of the system, it was tested and evaluated using two courses, 'Python Programming' and 'Introduction to AI,' through the use of a questionnaire. Results revealed that learners held positive evaluations of the system, noting improvements in post quality, reduced plagiarism, and enhanced comprehension. Additionally, feedback from open-ended questions also indicated the benefits of automatic feedback on post quality.