平行教学实验室：赋能K-12教育的新AI基础设施

高欣; 林飞; 傅君; 孙健君

doi:10.11959/j.issn.2096-6652.202610

Chinese

您当前的位置：

首页 >

文章列表页 >

平行教学实验室：赋能K-12教育的新AI基础设施

学术论文 | 更新时间：2026-04-27

- 平行教学实验室：赋能K-12教育的新AI基础设施
- Parallel teaching laboratories: a new AI infrastructure empowering K-12 education
- 智能科学与技术学报 2026年8卷第1期页码：94-104
- 作者机构：
  
  1.天津市第二中学，天津 300140
  2.澳门科技大学创新工程学院工程科学系，澳门 999078
- 作者简介：
  
  [ "高欣（1974- ），女，天津市第二中学党委副书记、校长，主要研究方向为智慧校园管理及建设。" ]
  [ "林飞（1994- ），男，澳门科技大学博士生，主要研究方向为平行智能、AI for Science、大语言模型、多模态感知和生成式AI。" ]
  [ "傅君（1982- ），女，天津市第二中学纪委书记，主要研究方向为现代教育技术、人工智能教育、中学科学教育和智慧校园建设。" ]
  [ "孙健君（1989- ），男，天津市第二中学德育主任，主要研究方向为高中语文教学、高中德育工作和团工作。" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202610
  中图分类号： G482
- 收稿：2026-01-10，
  
  修回：2026-03-25，
  
  录用：2026-03-26，
  
  纸质出版：2026-03-15
- 稿件说明：
移动端阅览
高欣,林飞,傅君等.平行教学实验室：赋能K-12教育的新AI基础设施[J].智能科学与技术学报,2026,08(01):94-104.

Gao Xin,Lin Fei,Fu Jun,et al.Parallel teaching laboratories: a new AI infrastructure empowering K-12 education[J].Chinese Journal of Intelligent Science and Technology,2026,08(01):94-104.
高欣,林飞,傅君等.平行教学实验室：赋能K-12教育的新AI基础设施[J].智能科学与技术学报,2026,08(01):94-104. DOI： 10.11959/j.issn.2096-6652.202610.

Gao Xin,Lin Fei,Fu Jun,et al.Parallel teaching laboratories: a new AI infrastructure empowering K-12 education[J].Chinese Journal of Intelligent Science and Technology,2026,08(01):94-104. DOI： 10.11959/j.issn.2096-6652.202610.

摘要

为应对中小学（K-12）教学实验室在运行效率、安全保障与智能支持等方面的挑战，引入平行智能，提出了一种以人工系统-计算实验-平行执行（ACP）方法为核心、融合三类人系统的平行教学实验室框架。该框架通过构建人工系统、开展计算实验并实施平行执行，将传统教学实验转化为可计算、可分析、可验证的虚实融合过程。通过系统集成大语言模型、视觉-语言-行动（VLA）等新一代人工智能（AI）技术，支撑实验教学的自主运行与安全可控。围绕平行教学实验室的体系架构、关键技术与运行机制展开分析，并结合天津市第二中学智慧出行AI实验室的建设实践，验证框架在真实教学场景中的可落地性与应用价值。研究表明，平行教学实验室为推动K-12实验教学由静态设施向智能化系统演进提供了一种可行路径。

Abstract

To address the challenges faced by kindergarten through twelfth grade (K-12) teaching laboratories in operational efficiency

safety assurance

and intelligent support

parallel intelligence was introduced

and a parallel teaching laboratory framework centered on the artificial systems

computational experiments

and parallel execution (ACP) method

while integrating the three-class human system. Through the construction of artificial systems

the execution of computational experiments

and the implementation of parallel execution

traditional teaching experiments were transformed into virtual-real integrated processes that were computable

analyzable

and verifiable. By systematically integrating next-generation AI technologies

including large language models and vision-language-action (VLA) models

autonomous operation and safety-controllable experimental teaching were supported. The architecture

key technologies

and operational mechanisms of the parallel teaching laboratory were analyzed

and the feasibility and application value of the framework in real educational settings were validated through the construction practice of the Smart Transportation AI Laboratory at Tianjin No. 2 High School. The results show that the parallel teaching laboratory provides a feasible pathway for promoting the evolution of K-12 experimental teaching from static facilities to intelligent systems.

关键词

Keywords

references

Feynman R P, Leighton R B, Sands M, et al. The Feynman lectures on physics; vol. I[J]. American Journal of Physics, 1965, 33(9): 750-752.

Abrahams I, Millar R. Does practical work really work? a study of the effectiveness of practical work as a teaching and learning method in school science[J]. International Journal of Science Education, 2008, 30(14): 1945-1969.

Chinn C A, Malhotra B A. Epistemologically authentic inquiry in schools: a theoretical framework for evaluating inquiry tasks[J]. Science Education, 2002, 86(2): 175-218.

Banilower E R, Smith P S, Weiss I R, et al. The status of K-12 science teaching in the United States: results from a national observation survey[C]//The Impact of State and National Standards on K-12 Science Technology.Leeds: Emerald Publishing Limited, 2006: 83-122.

Knight D, Hannigan M, Cheadle L, et al. Introducing university laboratory tools into K-12 classrooms: Benefits and challenges[C]//Proceedings of the 2017 IEEE Frontiers in Education Conference (FIE). Piscataway: IEEE Press, 2017: 1-6.

Lin Y Z, Alhamadah A H J, Redondo M W, et al. Transforming engineering education using generative ai and digital twin technologies[PP]. V1. (2024-11-01) [2026-01-10]. arXiv: arXiv.2411.14433.

Lo S, Baird S G, Schrier J, et al. Review of low-cost self-driving laboratories in chemistry and materials science: the "frugal twin" concept[J]. Digital Discovery, 2024, 3(5): 842-868.

Park S, Kim H. A comprehensive survey and taxonomy on large language model-based knowledge tracing[C]//International Conference on Generative Systems and Intelligent Tutoring Systems.Berlin: Springer, 2025: 246-258.

Wang F Y, Wang X, Li L X, et al. Steps toward parallel intelligence[J]. IEEE/CAA Journal of Automatica Sinica, 2016, 3(4): 345-348.

李柏, 郝金第, 孙跃硕, 等. 平行智能范式视角下的视觉-语言-动作模型发展现状与展望[J]. 智能科学与技术学报, 2025, 7(3): 290-303.

Li B, Hao J D, Sun Y S, et al. Vision-language-action models under parallel intelligence paradigm: the state of the art and future perspectives[J]. Chinese Journal of Intelligent Science and Technology, 2025, 7(3): 290-303.

王飞跃. 平行系统方法与复杂系统的管理和控制[J]. 控制与决策, 2004, 19(5): 485-489, 514.

Wang F Y. Parallel system methods for management and control of complex systems[J]. Control and Decision, 2004, 19(5): 485-489, 514.

王飞跃. 数字教师与平行教育: 关于ChatGPT之后教学变革的探讨[J]. 智能科学与技术学报, 2023, 5(4): 446-455.

Wang F Y. Digital teachers and parallel education: a paradigm shift in teaching and learning after ChatGPT[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 446-455.

王飞跃. 智能科技与K21教育: 未来社会的未来学校与未来师生[J]. 智能科学与技术学报, 2024, 6(3): 281-283.

Wang F Y. K21 education and intelligent sciences: future schools and teachers/students in future society[J]. Chinese Journal of Intelligent Science and Technology, 2024, 6(3): 281-283.

Zhu F H, Lv Y S, Chen Y Y, et al. Parallel transportation systems: toward IoT-enabled smart urban traffic control and management[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(10): 4063-4071.

Lin F, Yang J, Sun D L, et al. Autonomous drug discovery with parallel intelligence[J]. IEEE/CAA Journal of Automatica Sinica, 2025, 12(8): 1742-1744.

Hofstein A, Lunetta V N. The laboratory in science education: Foundations for the twenty-first century[J]. Science Education, 2004, 88(1): 28-54.

Koopmans M. Education is a complex dynamical system: challenges for research[J]. The Journal of Experimental Education, 2020, 88(3): 358-374.

Mital P, Moore R, Llewellyn D. Analyzing K-12 education as a complex system[J]. Procedia Computer Science, 2014, 28: 370-379.

Schauer F, Krbecek M, Beno P, et al. REMLABNET - open remote laboratory management system for e-experiments[C]//Proceedings of the 2014 11th International Conference on Remote Engineering and Virtual Instrumentation (REV). Piscataway: IEEE Press, 2014: 268-273.

Hu B K, Jia W Y, Mi S S, et al. The impact of computer simulation on students' conceptual understanding in K-12 science education: a meta-analysis[J]. Journal of Science Education and Technology, 2025. DOI: 10.1007/s10956-025-10264-7.

Lei Z C, Zhou H, Hu W S, et al. Web-based digital twin communication system of power systems for training and education[J]. IEEE Transactions on Power Systems, 2024, 39(2): 3592-3602.

Zhao P, Zhu H Y. Design and implementation of a virtual experimental teaching system for deep energy exploitation based on digital twin technology[J]. Systems, 2024, 12(10): 386.

Zhou Z L, Oveissi F, Langrish T. Applications of augmented reality (AR) in chemical engineering education: Virtual laboratory work demonstration to digital twin development[J]. Computers & Chemical Engineering, 2024, 188: 108784.

Taylor M V, Muwaffak Z, Penny M R, et al. Optimising digital twin laboratories with conversational AIs: enhancing immersive training and simulation through virtual reality[J]. Digital Discovery, 2025, 4(5): 1134-1141.

Taylor M, Bin Abdullah N, Al-Dargazelli A, et al. Breaking the access to education barrier: enhancing HPLC learning with virtual reality[J]. Journal of Chemical Education, 2024, 101(10): 4093-4101.

Acker J, Rogers I, Guerra-Zubiaga D, et al. Low-cost digital twin approach and tools to support industry and academia: a case study connecting high-schools with high degree education[J]. Machines, 2023, 11(9): 860.

张磊, 虎春艳, 张潍纤, 等. "实验室安全与规范"课程"四位一体"教学改革与实践[J]. 实验室研究与探索, 2025, 44(2): 258-262.

Zhang L, Hu C Y, Zhang W Q, et al. Reform and practice of "four-in-one" teaching system for laboratory safety and specification course[J]. Research and Exploration in Laboratory, 2025, 44(2): 258-262.

教育部. 中小学实验室规程[S]. 北京: 中华人民共和国教育部, 2009.

Ministry of Education of the People's Republic of China. Regulations on primary and secondary school laboratories[S]. Beijing: Ministry of Education, 2009.

American Chemical Society. Committee on chemical safety. guidelines for chemical laboratory safety in secondary Schools[M]. Washington: American Chemical Society, 2016.

American Chemical Society Committee on Chemical Safety. Identifying and evaluating hazards in research laboratories[R]. 2015.

Harada T, Hayashi R, Tomita K. Prediction of hazards in laboratory work using deep learning models learnt from past laboratory accidents[J]. Journal of Environment and Safety, 2024, 15(2): 17-24.

Ali L, Alnajjar F, Parambil M M A, et al. Development of YOLOv5-based real-time smart monitoring system for increasing lab safety awareness in educational institutions[J]. Sensors, 2022, 22(22): 8820.

Gupta K, Brundage P, Palassis J. School chemistry laboratory safety guide[R]. 2006.

Qin H T, Jin C S, Zhou T, et al. A LoRa-based multi-node system for laboratory safety monitoring and intelligent early-warning: towards multi-source sensing and heterogeneous networks[J]. Sensors, 2025, 25(21): 6516.

徐文, 杜坤, 李江, 等. 数字孪生技术在高校实验室安全管理中的实践[J]. 实验技术与管理, 2024, 41(4): 222-227.

Xu W, Du K, Li J, et al. Research on the application of digital twin technology in university laboratory safety management[J]. Experimental Technology and Management, 2024, 41(4): 222-227.

Rickel J W. Intelligent computer-aided instruction: a survey organized around system components[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1989, 19(1): 40-57.

Bitzer D, Braunfeld P, Lichtenberger W. PLATO: an automatic teaching device[J]. IRE Transactions on Education, 1961, 4(4): 157-161.

Graesser A C. Conversations with AutoTutor help students learn[J]. International Journal of Artificial Intelligence in Education, 2016, 26(1): 124-132.

Dougiamas M, Taylor P. Moodle: using learning communities to create an open source course management system[C]//ED-MEDIA 2003-World Conference on Educational Multimedia, Hypermedia & Telecommunications. Chesapeake: Association for the Advancement of Computing in Education, 2003: 171-178.

教育部. 国家中小学智慧教育平台上线试运行[EB]. (2022-03-28)[2026-01-10].

Ministry of Education of the People's Republic of China. National Smart Education Platform for Primary and Secondary Schools Launched for Trial Operation[EB]. (2022-03-28)[2026-01-10].

清华大学. 学堂在线推出智慧教学工具——雨课堂[EB]. (2016-06-17)[2026-01-10].

Tsinghua University. XuetangX launches intelligent teaching tool "Yuketang (Rain Classroom)"[EB]. (2016-06-17)[2026-01-10].

Hurst A, Lerer A, Goucher A P, et al. GPT-4o system card[PP]. V1. (2024-10-25) [2026-01-10]. arXiv: arXiv.2410.21276.

Khan Academy. Meet Khanmigo: Khan Academy's AI-powered teaching assistant and tutor[EB]. [2026-01-10].

Quizlet. Introducing Q-chat, the world's first AI tutor built with OpenAI's ChatGPT API[EB]. (2023-02-28)[2026-01-10].

Duolingo. Explain my answer is now available to learners[EB]. (2023-10-11)[2026-01-10].

Vanzo A, Pal C S, Sachan M. GPT-4 as a homework tutor can improve student engagement and learning outcomes[C]//Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg: ACL, 2025: 31119-31136.

Kestin G, Miller K, Klales A, et al. AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational setting[J]. Scientific Reports, 2025, 15(1): 17458.

Tinker R. A history of probeware[R]. Concord: The Concord Consortium, 2000.

PASCO Scientific. PASCO wireless sensors & SPARKvue for science education[EB]. [2026-01-10].

Vernier Science Education. Science probeware & experiment software for teachers[EB]. [2026-01-10].

SMART Technologies. SMART Technologies launches new SMART Board RX series[EB]. (2023-11-16)[2026-01-10].

希沃. 第六代交互智能平板[EB]. (2023-04-22)[2026-01-10].

Seewo. 6th-generation interactive flat panel[EB]. (2023-04-22)[2026-01-10].

孙元成, 赵敏. 一种集成式的实验室顶部吊装系统: CN201921204274.7[P].(2020-06-05)[2026-01-10].

Sun Y C, Zhao M. Integrated laboratory ceiling-mounted system: CN210685124U[P]. (2020-06-05)[2026-01-10].

Karalekas G, Vologiannidis S, Kalomiros J. Teaching machine learning in K-12 using robotics[J]. Education Sciences, 2023, 13(1): 67.

Jara C A, Candelas F A, Torres F. Virtual and remote laboratory for robotics e-learning[C]//18th European Symposium on Computer Aided Process Engineering. Amsterdam: Elsevier, 2008: 1193-1198.

Song T, Luo M, Zhang X L, et al. A multiagent-driven robotic AI chemist enabling autonomous chemical research on demand[J]. Journal of the American Chemical Society, 2025, 147(15): 12534-12545.

Zhang J Y, Huang J X, Jin S, et al. Vision-language models for vision tasks: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(8): 5625-5644.

Lan Z Q, Jiang Y X, Wang R Q, et al. AutoBio: a simulation and benchmark for robotic automation in digital biology laboratory[PP]. V3. (2025-05-29)[2026-01-10]. arXiv: arXiv.2505.14030.

Khan M H, Asfaw S, Iarchuk D, et al. Shake-VLA: vision-language-action model-based system for bimanual robotic manipulations and liquid mixing[C]//Proceedings of the 2025 20th ACM/IEEE International Conference on Human-Robot Interaction (HRI). Piscataway: IEEE Press, 2025: 1393-1397.

张俊, 许沛东, 王飞跃. 平行系统和数字孪生的一种数据驱动形式表示及计算框架[J]. 自动化学报, 2020, 46(7): 1346-1356.

Zhang J, Xu P D, Wang F Y. Parallel systems and digital twins: a data-driven mathematical representation and computational framework[J]. Acta Automatica Sinica, 2020, 46(7): 1346-1356.

杨林瑶, 陈思远, 王晓, 等. 数字孪生与平行系统: 发展现状、对比及展望[J]. 自动化学报, 2019, 45(11): 2001-2031.

Yang L Y, Chen S Y, Wang X, et al. Digital twins and parallel systems: state of the art, comparisons and prospect[J]. Acta Automatica Sinica, 2019, 45(11): 2001-2031.

薛霄, 于湘凝, 周德雨, 等. 计算实验方法的溯源、现状与展望[J]. 自动化学报, 2023, 49(2): 246-271.

Xue X, Yu X N, Zhou D Y, et al. Computational experiments: past, present and perspective[J]. Acta Automatica Sinica, 2023, 49(2): 246-271.

Yang J, Wang X, Tian Y L, et al. Parallel intelligence in CPSSs: being, becoming, and believing[J]. IEEE Intelligent Systems, 2023, 38(6): 75-80.

Lewis P, Perez E, Piktus A, et al. Retrieval-augmented generation for knowledge-intensive NLP tasks[C]//Proceedings of the 34th International Conference on Neural Information Processing Systems. New York: ACM Press, 2020: 9459-9474.

Zhang J T, Li G L, Su J Y. SAGE: a framework of precise retrieval for RAG[PP]. V3. (2025-06-06)[2026-01-10]. arXiv: arXiv.2503.01713.

Quigley M, Conley K, Gerkey B, et al. ROS: an open-source robot operating system[C]//ICRA Workshop on Open Source Software.Piscataway: IEEE Press, 2009: 1-6.

Unity Technologies. Unity user manual 2022.1[EB]. (2022-11-18) [2026-01-10].

Dassault Systèmes. SOLIDWORKS 2024 Web Help[EB]. (2024-01-10) [2026-01-10].

Dissanayake M W M G, Newman P, Clark S, et al. A solution to the simultaneous localization and map building (SLAM) problem[J]. IEEE Transactions on Robotics and Automation, 2001, 17(3): 229-241.

Mildenhall B, Srinivasan P P, Tancik M, et al. NeRF: representing scenes as neural radiance fields for view synthesis[J]. Communications of the ACM, 2021, 65(1): 99-106.

Kerbl B, Kopanas G, Leimkuehler T, et al. 3D Gaussian splatting for real-time radiance field rendering[J]. ACM Transactions on Graphics, 2023, 42(4): 1-14.

Lin C H, Gao J, Tang L M, et al. Magic3D: high-resolution text-to-3D content creation[C]//Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2023: 300-309.

Jia M L, Tang L M, Chen B C, et al. Visual prompt tuning[C]//European Conference On Computer Vision. Berlin: Springer, 2022: 709-727.

Yu B T, Baker F N, Chen Z Q, et al. LlaSMol: advancing large language models for chemistry with a large-scale, comprehensive, high-quality instruction tuning dataset[PP]. V4. (2024-08-10) [2026-01-10]. arXiv: arXiv.2402.09391.

Tran H, Yang Z C, Yao Z H, et al. BioInstruct: instruction tuning of large language models for biomedical natural language processing[J]. Journal of the American Medical Informatics Association, 2024, 31(9): 1821-1832..

Dan Y H, Lei Z K, Gu Y Y, et al. EduChat: a large-scale language model-based chatbot system for intelligent education[PP]. V1. (2023-08-05) [2026-01-10]. arXiv: arXiv.2308.02773.

Holmes W, Miao F. Guidance for generative AI in education and research[R]. 2023.

Jiang Y H, Lu Y J, Dai L, et al. Agentic workflow for education: concepts and applications[PP]. V1. (2025-09-01)[2026-01-10]. arXiv: arXiv. 2509.01517.

Jain R, Rout S, Srivastava G. ChemVR-AI: an immersive virtual reality laboratory with pedagogical agents[C]//Proceedings of the 27th International Conference on Mobile Human-Computer Interaction. New York: ACM Press, 2025: 1-5.

Bosma M, Chi E, Ichter B, et al. Chain-of-thought prompting elicits reasoning in large language models[C]//Proceedings of the Advances in Neural Information Processing Systems. Massachusetts: MIT Press, 2022: 24824-24837.

Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models[J]. Advances in Neural Information Processing Systems, 2020, 33: 6840-6851.

Lipman Y, Chen R T Q, Ben-Hamu H, et al. Flow Matching for Generative Modeling[PP]. V3. (2024-02-08)[2026-01-10]. arXiv: arXiv. 2210.02747.

Chi C, Xu Z J, Feng S Y, et al. Diffusion policy: Visuomotor policy learning via action diffusion[J]. The International Journal of Robotics Research, 2025, 44(10/11): 1684-1704.

Kim M J, Pertsch K, Karamcheti S, et al. OpenVLA: an open-source vision-language-action model[PP]. V3. (2024-09-05)[2026-01-10]. arXiv: arXiv.2406.09246.

Black K, Brown N, Driess D, et al. π 0 : a vision-language-action flow model for general robot control[PP ] . V4. (2026-01-08)[2026-01-10 ] . arXiv: arXiv. 2410.24164.

Liang J, Huang W L, Xia F, et al. Code as policies: language model programs for embodied control[C]//Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE Press, 2023: 9493-9500.

国务院. 国务院关于印发新一代人工智能发展规划的通知[EB]. (2017-07-20)[2026-01-11].

State Council of the People's Republic of China. Notice of the state council on issuing the new generation artificial intelligence development plan[EB]. (2017-07-20)[2026-01-11].

Dosovitskiy A, Ros G, Codevilla F, et al. CARLA: an open urban driving simulator[C]//Proceedings of the 1st Annual Conference on Robot Learning. [S.l.]: PMLR, 2017: 1-16.

Tzutalin. LabelImg: graphical image annotation tool[EB]. [2026-01-10].

Liu A X, Mei A X, Lin B C, et al. DeepSeek-V3.2: pushing the frontier of open large language models[PP]. V1. (2025-12-02)[2026-01-10]. arXiv: arXiv. 2512.02556.

Yang A, Li A F, Yang B S, et al. Qwen3 technical report[PP]. V1. (2025-05-14)[2026-01-10]. arXiv: arXiv. 2505.09388.

JetBrains. PyCharm: the only Python IDE you need[EB]. [2026-01-10].

Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2016: 779-788.

Warden P, Situnayake D. Tinyml: Machine learning with tensorflow lite on arduino and ultra-low-power microcontrollers[M]. Sebastopol: O'Reilly Media, 2019.

Bradski G. The OpenCV library[J]. Dr. Dobb's Journal: Software Tools for the Professional Programmer, 2000, 25(11): 120-123.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

平行澳门野外观测站：全域智能观测与应急管理自主系统

平行奥森：智慧生态与健康生活共融的元宇宙公园

平行药物系统：基于大语言模型和三类人的框架与方法

Cerberus：基于深度学习的跨站社交网络机器人检测系统