Digital Education Reform for First-Class Course under the Empowerment of AIGC

With the rapid development of Artificial Intelligence Generated Content (AIGC), its application in education is becoming widespread, especially showing great potential in digital reform of First-Class courses. This paper aims to take AIGC technology to promote the digital reform of courses, including the adjustment of teaching objectives, the construction of Knowledge Graph-based teaching resources, the innovation of teaching methods, and a student-centered "AI Assistant + Teacher" model for cultivating abilities through "Knowledge- Application- thinking -innovation" is proposed. Based on this, the paper presents a personalized course resource recommendation algorithm based on deep learning S-CNN, which synthesizes learners' implicit and explicit feature labels and course resource labels, and is trained in CNN model, which is used to give personalized learning recommendation path for students by combining implicit ratings with explicit user ratings. Finally, Experimental comparisons with existing methods show that the algorithm proposed in the paper can provide learners with more accurate recommendation, and the pedagogical effectiveness of this model was demonstrated through questionnaires and student satisfaction survey.