Framework Design of a Multi-Educational-Agent System for University Lecturers
Kai Yang, Lei Niu · 2024
Lecturers play a crucial role in students' learning activities. Their teaching planning, understanding of students' situations, and teaching methods all impact student success. The use of artificial intelligence in education is growing, but there is limited research on AI solutions to assist lecturers' daily work. Current research lacks a comprehensive intelligent system to assist lecturers in dynamically tracking student learning, supporting them in research and teaching, and assisting them in applying for resources from the university. This paper proposes a framework design for a virtual teaching and research intelligence system for university lecturers. The system offers personalized guidance to students, real-time feedback on student learning to lecturers, assists with teaching planning, optimizes working time, and aids in scientific research. Meanwhile, the proposed system design can also optimize the allocation of campus educational resources to alleviate conflicts caused by traditional manual allocated methods.