Volume 3,Issue 8
Risks and Governance Paths of the Internal Circulation of College Students’ Knowledge Structures Under the Intervention of Generative Intelligence
In the current digitalization process of higher education oriented towards independent and self-reliant science and technology, generative intelligence has become an important tool for college students’ daily learning. Compared with traditional information acquisition methods, generative intelligence has significant advantages in speed, presentation form, and structured expression. However, the corpus circulation and content regeneration mechanisms behind its operation are quietly changing students’ knowledge source structures, learning path logic, and cognitive formation methods. When the text supply enters a contraction cycle, it becomes normal for models to rely on internally regenerated content for training. Students gradually form a dependence on regenerated knowledge in the learning process, making the knowledge structure shift from external expansion to internal circulation. Phenomena such as the convergence of knowledge content, the single-path nature of expression structures, and the blurring of conceptual boundaries continue to accumulate, leading to a stable deviation in the cognitive foundation. This structural change not only affects learning quality but may also weaken students’ academic judgment, value recognition ability, and theoretical innovation ability. To address this trend, higher education needs to construct a systematic governance path from three dimensions: knowledge supply, learning structure, and value guidance. By expanding the boundaries of knowledge sources, reconstructing in-depth learning structures, and strengthening value-oriented intervention, students can maintain the openness, hierarchy, and value stability of their knowledge structures while using generative intelligence, and promote the formation of real learning abilities.
[1] Guo J, Content Security Risks of Generative Artificial Intelligence: Origin, Types and Regulatory Paths. Science and Management, 1–10.
[2] Xu L, Yu J, Wang D, Value Implication, Multi-Dimensional Risks and Mitigation Strategies of Generative Artificial Intelligence Empowering Public Administration Case Teaching. Journal of China West Normal University (Philosophy and Social Sciences Edition), 1–12.
[3] Liu B, Cen Y, 2025, “Metamorphosis” and “Distortion” of Global Higher Education Under the Digital Wave. Journal of Higher Education Management, 2025(6): 27–36.
[4] Su J, Ye H, 2025, From Embodied Intelligence to Educational Innovation: Multi-Disciplinary Construction of Cognitive Growth Paths. Open Education Research, 2025(5): 79–88.
[5] Yang D, He J, 2025, Philosophical Reflection on the Reform of School Science Education from the Perspective of Artificial Intelligence. Journal of Dialectics of Nature, 2025(11): 100–107.
[6] Gao S, 2025, Progress and Prospect of Artificial Intelligence Technology in International Chinese Language Education from the Perspective of Large Language Models. China Computer & Communication, 2025(22): 251–253.
[7] Li Y, Dai Y, Artificial Intelligence as a Method: Systematic Generation and Governance Reconstruction of the Integration of Educational Technology Talents. Journal of Higher Education Evaluation, 1–12.
[8] Sun F, Hu Z, 2025 Generative Logic of Teaching and Learning Driven by Intelligent Technology. Journal of Higher Education, 2025(02): 74–81.
[9] Liang J, 2025, Research on Artificial Intelligence Empowering the High-Quality Development of Continuing Education. Continue Education Research, 2025(12): 18–24.
[10] Chen L, Dai J, 2025, Logic, Value and Path of Artificial Intelligence Driven Innovation and Development of School Moral Education. Journal of Teaching and Management, 2025(33): 33–39.
[11] Leng J, Lu H, Dai L, 2024, Generative Artificial Intelligence Empowering Critical Thinking Assessment—An Application Experiment Based on ChatGPT. Modern Distance Education Research, 2024(6): 102–111.
[12] Tian F, Lu C, 2025, Positions and Strategies of Top American Research Universities in Responding to Generative Artificial Intelligence. E-education Research, 2025(11): 108–113.
[13] Cornell University, 2025, Guidelines for Artificial Intelligence. 2025–08–20.