以多模态数智技术助力高等教育改革

Group 1 - New quality productivity is driven by technological innovation, characterized by high-tech content, high operational efficiency, and high-quality development, aligning with advanced productivity forms in the new development concept [1] - Higher education plays a crucial role in cultivating new quality productivity, serving as a key arena for nurturing innovative talent and supporting national strategies [1] - The "Education Strong Nation Construction Plan Outline (2024-2035)" emphasizes digital education as a breakthrough, advocating for a comprehensive transformation in educational concepts, teaching models, and governance [1] Group 2 - Constructivist learning theory underpins the creation of multimodal learning environments, which are essential for nurturing new quality productivity talent [2] - Multimodal learning environments enhance knowledge construction through multisensory interactions, supported by digital learning spaces [2] - The integration of multimodal large language models is reshaping learning resources and cognitive interaction patterns [2] Group 3 - Generative AI provides a technical paradigm for constructing multimodal environments, enabling intelligent generation of teaching resources [3] - Teachers can transform abstract concepts into concrete multimodal materials, enhancing interdisciplinary teaching and learning experiences [3] - This multimodal conversion aligns with constructivist theories, supporting the cultivation of innovative talent suited for new quality productivity [3] Group 4 - Educational neuroscience technology empowers multimodal learning analysis, creating opportunities for data value extraction in educational digital transformation [4] - Traditional analysis frameworks are limited, but advancements in non-invasive physiological measurement technologies extend analysis dimensions to physiological mechanisms [4] - Educational neuroscience integrates cognitive neuroscience, psychology, and education, forming a technical system for multimodal data collection [4] Group 5 - Educational neuroscience-driven multimodal learning analysis overcomes limitations of subjective reporting by objectively recording learning responses [5] - It enables millisecond-level dynamic monitoring of neural activities, constructing high-precision learning state profiles [5] - The technology reveals implicit cognitive dimensions, providing scientific cognitive diagnostic tools for nurturing innovative talent [5] Group 6 - Generative AI innovates multimodal learning evaluation, offering a comprehensive solution from feedback diagnosis to predictive intervention [6] - Traditional evaluation systems face challenges of lag and one-dimensionality, but AI can provide more accurate assessments [6] - Research indicates that AI technologies can outperform humans in tasks like paper grading and code diagnostics [6] Group 7 - Generative AI's predictive evaluation capabilities enhance the effectiveness, precision, and reliability of multimodal learning assessments [7] - Multi-agent systems can autonomously generate personalized learning paths and conduct pre-evaluations of learning tasks [7] - This innovative evaluation paradigm creates a closed-loop system of "evaluation-feedback-optimization," providing solutions for talent evaluation in new quality productivity development [7] Group 8 - New quality productivity and higher education form a mutually empowering closed loop, with the former providing strategic support for educational digital transformation [8] - Higher education integrates data elements and intelligent technologies, contributing to talent cultivation, scientific research innovation, and industrialization [8] - This creates a value chain of "education nurturing talent - talent driving innovation - innovation empowering industry," promoting high-quality digital transformation [8]