Artificial Intelligence (AI) offers innovative solutions to address the teaching challenges of linear algebra courses caused by their high level of abstraction, such as students’ difficulties in comprehension, lack of interest, and weak self-directed learning abilities. This study proposes empowering educators to reconstruct teaching models through three key strategies: building intelligent teaching platforms (enabling personalized resource recommendations, learning path planning, and real-time feedback), leveraging data-driven instructional decisions (to dynamically optimize teaching content), and deploying intelligent tutoring systems (with virtual assistants to explain abstract concepts). Meanwhile, to tackle challenges such as data privacy, teachers’ adaptability to technology, and integration of educational resources, it is necessary to enhance data security mechanisms, strengthen teacher training, and promote inter-institutional collaboration. Future development will move toward multimodal interaction (e.g., VR/AR to enhance visual intuition) and interdisciplinary integration, advancing AI from a supporting tool to a core driving force of educational innovation, ultimately serving the goal of cultivating high-quality, versatile talents.
Qingrong Zou (Tue,) studied this question.
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