|
[1]
|
倪晓玉, 袁敏, 孙师丹. 研究生教育赋能新质生产力: 核心要素、现实境遇与实践路径[J]. 研究生教育研究, 2024(4): 12-18.
|
|
[2]
|
林蕙青. 充分发挥科技创新的引领驱动作用实现高等教育高质量可持续发展[J]. 中国高教研究, 2024(11): 1-3.
|
|
[3]
|
项秋实, 王淼, 谢东辰, 等. 浅析光纤通信技术的原理及发展趋势[J]. 数字通信世界, 2024(3): 173-175.
|
|
[4]
|
戴岭, 赵晓伟, 祝智庭. 智慧问学: 基于ChatGPT的对话式学习新模式[J]. 开放教育研究, 2023, 29(6): 42-51+111.
|
|
[5]
|
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., et al. (2023) ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
|
|
[6]
|
Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö. and Mariman, R. (2025) Generative AI without Guardrails Can Harm Learning: Evidence from High School Mathematics. Proceedings of the National Academy of Sciences, 122, e2422633122. https://doi.org/10.1073/pnas.2422633122
|
|
[7]
|
梁俊斌, 谢贇, 唐振华. 生成式人工智能赋能教育数字化转型的机理、挑战与对策[J]. 现代教育管理, 2025(11): 15-23.
|
|
[8]
|
韩杰, 张伟刚. 基于P-MASE的拔尖人才培养模式改革与实践[J]. 高等理科教育, 2023(1): 63-69.
|
|
[9]
|
张伟刚, 严铁毅, 张严昕. 基于P-MASE模型的研究性教学与素质教育[C]//河南科技大学. 素质教育: 让未来更美好——2020年大学素质教育高层论坛论文集. 北京: 高等教育出版社, 2020: 166-173.
|
|
[10]
|
李钦, 钱泉, 胡坤. 人工智能赋能高等教育拔尖创新人才培养的模式探索[J]. 豫章师范学院学报, 2026, 41(3): 45-50.
|
|
[11]
|
Fan, Q., Zhou, G., Gui, T., Lu, C. and Lau, A.P.T. (2020) Advancing Theoretical Understanding and Practical Performance of Signal Processing for Nonlinear Optical Communications through Machine Learning. Nature Communications, 11, Article No. 3694. https://doi.org/10.1038/s41467-020-17516-7
|
|
[12]
|
Abdelli, K. (2026) Prompt Once, Manage All: A Unified LLM Framework for Multi-Task Optical Link Management. Journal of Lightwave Technology, 44, 2869-2879. https://doi.org/10.1109/jlt.2026.3660177
|
|
[13]
|
Rismanchian, S., Babar, E.T.R. and Doroudi, S. (2026) What Undergraduate Students Need to Know and Actually Know about Generative AI. Computers and Education: Artificial Intelligence, 10, Article 100554. https://doi.org/10.1016/j.caeai.2026.100554
|
|
[14]
|
Lewis, P., Perez, E., Piktus, A., et al. (2020) Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems, 33, 9459-9474.
|
|
[15]
|
Yao, S., Zhao, J., Yu, D., et al. (2023) ReAct: Synergizing Reasoning and Acting in Language Models. 2023 International Conference on Learning Representations, Kigali Convention Centre, 1-5 May 2023, 1-33.
|