[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fYQsY1_MSb6TyKPejYOGdAp9p1gz3ulQTl6OaO6oPaVE":3},{"code":4,"msg":5,"data":6},200,"操作成功",{"id":7,"title":8,"content":9,"digest":10,"source":10,"coverPath":11,"thumbsCoverPath":12,"isTop":13,"isShow":14,"baseClick":13,"clickCount":15,"createTime":16,"typeId":17,"isNewest":18,"newsInfoTypeRespVo":19,"voiceUrl":22,"voiceSize":23,"taskId":24,"releaseTime":25,"titleEn":26,"contentEn":27,"voiceUrlEn":28,"taskIdEn":29,"voiceSizeEn":30},1312,"人工智能助力教育质性研究范式创新发展","\u003Cp class=\"ql-align-justify\">\u003Cstrong style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">教育质性研究范式是以研究者本人为研究工具，在自然情境下运用参与观察、深度访谈等多种资料收集方法对教育现象进行整体性探究，使用归纳法分析资料并形成理论，通过与研究对象互动对其行为和意义建构获得解释性理解的研究范式。质性研究范式以其叙事的价值性、体验的独特性、研究的文化性，引发人们对客观科学的定量研究范式的反思，强化了教育研究与社会文化、价值意义及意识形态的关系，使教育研究成为一种具有深刻文化意蕴和价值反思的活动。随着新一代人工智能技术的迅猛发展，教育质性研究范式面临新的挑战与机遇。如何让人工智能技术助力教育质性研究范式的创新发展，成为重要的研究议题。人工智能驱动的科学研究（AI for science，AI4S）推动科研全过程智能化，开启了科学研究的“第五范式”。\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cstrong style=\"font-size: 18px; color: rgb(255, 153, 0);\" class=\"ql-lineHeight-1-75\">构建“质”“量”融合的方法论\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">第五范式基于算法建模和概率统计而实现的机器智能涌现与自动化深度学习，超越了以大数据管理和统计分析为特征的数据密集型科学研究“第四范式”。其在科研领域的广泛应用，促使科学研究不断取得突破性进展，已成为科学研究的强大动力。李国杰院士预言，再过10—20年，“第五范式”会逐步成为科学研究的主流范式之一。新一代人工智能与质性研究的深度融合，有望在解释世界、理论创新中激发新灵感、发现新概念、揭示新机制，进而重构质性研究的理论与实践样态。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">人工智能将在方法论上弥合定量研究与质性研究之间的分歧，跨越微观视角与宏观视角之间的鸿沟，构建数字与人文交叉融合的混合范式。人工智能将计算思维引入教育研究活动中，用检索和数据描述等定量研究方法分析质性研究资料，用质性研究的文化阐释深化定量研究结果，推进跨学科的理论融合创新。在新范式下，质性研究与定量研究的资料收集与分析过程逐渐趋同，均转向海量大数据挖掘和计算建模，以探索教育现象背后的理论模型，揭示教育要素的本质关系。此外，新一代人工智能的大模型、算法及算力不断提升，与计算机科学、数据科学、教育学、心理学等学科交叉融合、相互促进，形成以新一代人工智能技术支撑教育基础理论发展和学习科学前沿研究的新模式。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cstrong style=\"font-size: 18px; color: rgb(255, 153, 0);\" class=\"ql-lineHeight-1-75\">形成“人机协同”的研究方式\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">新一代人工智能技术可以充当研究者的助手，深度介入质性研究知识生产过程，辅助研究者完成研究对象选取、资料收集与分析、文本撰写等任务，提升研究效率。在对象选取阶段，研究者可以将自我意识与新一代人工智能协同产生的意识经验结合起来，从“人机融合”的视角出发，在现实生活或网络平台上寻找符合理论意图的样本，利用大数据技术从海量规模的样本中寻找教育发展规律，从大历史、大时空的视角分析特定教育主题的发展脉络，推进兼顾规模效率与参与深度的质性研究。在资料分析阶段，研究可以借助新一代人工智能的海量知识数据库开展理论分析，运用不断迭代升级的算法和算力平台分析编码与解释数据资料，还可以利用人工智能技术查阅文献、翻译文本、设计访谈提纲、收集访谈资料、分析和整理资料、形成编码等。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">在质性研究过程中，研究者在诠释教育现象及问题、理解情景化细节和进行理论分析方面的能力远远胜过人工智能，而人工智能所具有的自然文本生成、关键概念识别和整理分析资料的能力胜过研究者，因此，研究者与人工智能技术在协同中发挥各自特长，可以极大提高质性研究的规范性与实效性。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cstrong style=\"font-size: 18px; color: rgb(255, 153, 0);\" class=\"ql-lineHeight-1-75\">运用嵌入“计算思维和工具”的研究方法\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">人工智能科学提供了以量化方式分析质性资料数据的全新方法技术，推动了以计算思维和计算工具赋能质性研究的进程。传统质性研究以个案为研究对象，聚焦微观层面的教育行为，揭示具体教育活动的运行机制，但难以对宏观层面复杂的教育行为和涌现的教育活动进行有效预测。基于行动者建模方法（Agent-based Modeling，ABM）是一种基于计算机模拟的教育思想实验，它利用计算机编程模拟行动者的个体行为与社会互动，呈现复杂性教育活动的演化发展过程，进而揭示教育活动的生成机制与行为模式。该方法能够将教育活动的微观层面与宏观层面联系起来，从经验实证视角探索教育行为的复杂运行模式。传统扎根理论运用归纳方式进行分析，分析过程具有主观性特征，不能对非结构化数据进行解释，研究结论备受质疑。而计算扎根理论以复杂涌现为立场，运用机器学习与溯源算法，通过词典进行可复制迭代编码，从而发现大规模数据中的复杂关系，助推新的概念和理论的产生。“掌握计算扎根方法不啻获得了米尔斯所提出的社会学想象力之外的补充。”计算扎根理论提供了一种借助数据和模型来构建概念和理论的研究路径，兼具质性研究的解释性与定量研究的预测性特质，不仅可以为教育学科发展服务，助推理论知识的生产，而且可以为政府制定教育政策提供决策支持，为教育治理寻找关键干预变量。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">为应对数智时代复杂的研究生态，有必要在质性研究方法课程与教材中增加计算思维与方法等模块内容，推进计算思维和工具有效融入质性研究的课程与教学。在教育研究过程中，应加强跨学科的交流与合作，构建由人工智能专家、工程师、教育研究者、实践工作者与政策制定者等共同组成的研究团队，推进教育学科知识生产方式的变革。\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cimg alt=\"undefined\" src=\"https:\u002F\u002Fimage.51xinwei.com\u002F2025\u002F09\u002Fd026edc6e1a94f9c8bead76499b7210f\u002F下载.jpg\" width=\"undefined\" height=\"undefined\" style=\"display: block; margin: auto;\">\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"color: rgb(187, 187, 187);\">【新闻来源】\u003C\u002Fspan>\u003Cspan style=\"color: rgb(187, 187, 187); background-color: rgb(56, 56, 56);\">中国社会科学报 \u003C\u002Fspan>\u003Cspan style=\"color: rgb(187, 187, 187);\">作者系首都师范大学教育学院教授 \u003C\u002Fspan>\u003Ca href=\"https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FK89QSRJ6051495OJ.html\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(187, 187, 187);\">https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FK89QSRJ6051495OJ.html\u003C\u002Fa>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"color: rgb(187, 187, 187);\">（本网转发此文章，旨在为读者提供更多的信息资讯，所涉内容不构成投资、消费建议。文章事实如有疑问，请与有关方核实，文章观点非本网观点，仅供读者参考。）\u003C\u002Fspan>\u003C\u002Fp>","","https:\u002F\u002Fimage.51xinwei.com\u002F2025\u002F09\u002Fa10fdfadcd35482f9bdd340b3efdd12a\u002F教育生态.jpg","https:\u002F\u002Fimage.51xinwei.com\u002F2025\u002F09\u002Fthumbs\u002Fa10fdfadcd35482f9bdd340b3efdd12a\u002F教育生态.jpg",0,1,56,"2025-09-02 15:24",2,false,{"id":17,"name":20,"enName":21},"芯位视野","Xinwei Vision","https:\u002F\u002Fxinwei-dev-test.oss-cn-shenzhen.aliyuncs.com\u002Fintelligent\u002Faudio%3Af45ac5d9-bece-4e43-bb92-2549b5599a16%3A0.wav?Expires=1756874649&OSSAccessKeyId=LTAI5tNvY2RkKjZw4LLWsrPK&Signature=aBdA5kLmlM6eccvxtmjQhzqN7wA%3D",11066866,"f45ac5d9-bece-4e43-bb92-2549b5599a16","2025-09-02 15:09","Artificial Intelligence Empowers the Innovative Development of the Paradigm of Qualitative Educational Research","\u003Cp class=\"ql-align-justify\">\u003Cstrong style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">The paradigm of qualitative educational research is a research approach where the researcher themselves serve as the research tool, conducting holistic exploration of educational phenomena through methods such as participant observation and in-depth interviews in natural settings. It uses inductive methods to analyze data and form theories, and gains interpretive understanding of the behaviors and meaning construction of research subjects through interaction with them. The qualitative research paradigm, with its narrative value, unique experience, and cultural nature, has prompted reflection on the quantitative research paradigm based on objective science, strengthening the relationship between educational research and social culture, values, and ideology, making educational research an activity with profound cultural significance and value reflection. With the rapid development of new-generation artificial intelligence technology, the paradigm of qualitative educational research faces new challenges and opportunities. How to enable artificial intelligence technology to support the innovative development of the paradigm of qualitative educational research has become an important research topic. AI for Science (AI4S) promotes the intelligence of the entire scientific research process, opening up the \"fifth paradigm\" of scientific research.\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cstrong style=\"font-size: 18px; color: rgb(255, 153, 0);\" class=\"ql-lineHeight-1-75\">Building a Methodology that Integrates \"Quality\" and \"Quantity\"\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">The fifth paradigm, which achieves machine intelligence emergence and automated deep learning through algorithm modeling and probabilistic statistics, surpasses the fourth paradigm characterized by big data management and statistical analysis. Its wide application in the field of scientific research has continuously driven breakthroughs and has become a powerful driving force for scientific research. Academician Li Guojie predicted that within the next 10–20 years, the fifth paradigm will gradually become one of the mainstream paradigms of scientific research. The deep integration of the new generation of artificial intelligence with qualitative research is expected to inspire new ideas, discover new concepts, and reveal new mechanisms in explaining the world and theoretical innovation, thereby reconstructing the theoretical and practical forms of qualitative research.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">Artificial intelligence will bridge the gap between quantitative research and qualitative research at the methodological level, cross the divide between micro and macro perspectives, and build a hybrid paradigm that integrates digital and humanities. Artificial intelligence introduces computational thinking into educational research activities, using quantitative research methods such as retrieval and data description to analyze qualitative research materials, and using qualitative research's cultural interpretation to deepen quantitative research results, promoting interdisciplinary theoretical integration and innovation. Under the new paradigm, the processes of data collection and analysis in qualitative and quantitative research are gradually converging, shifting towards massive data mining and computational modeling to explore theoretical models behind educational phenomena and reveal the essential relationships among educational elements. In addition, the large models, algorithms, and computing power of the new generation of artificial intelligence continue to improve, integrating and promoting each other with disciplines such as computer science, data science, education, and psychology, forming a new model that supports the development of basic educational theories and cutting-edge research in learning science with the new generation of artificial intelligence technology.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cstrong style=\"font-size: 18px; color: rgb(255, 153, 0);\" class=\"ql-lineHeight-1-75\">Forming a \"Human-Machine Collaboration\" Research Approach\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">New generation artificial intelligence technology can act as an assistant to researchers, deeply intervening in the knowledge production process of qualitative research, assisting researchers in tasks such as selecting research subjects, collecting and analyzing data, and writing texts, thus improving research efficiency. During the subject selection phase, researchers can combine their self-awareness with the conscious experiences generated by the collaboration between new generation artificial intelligence, and from the perspective of \"human-machine integration,\" find samples that align with theoretical intentions in real life or online platforms. Using big data technology, they can identify educational development patterns from massive sample sizes, analyze the development trajectory of specific educational topics from a macro historical and spatial perspective, and promote qualitative research that balances scale efficiency and participation depth. During the data analysis phase, research can leverage the vast knowledge database of the new generation of artificial intelligence for theoretical analysis, use continuously upgraded algorithms and computing platforms to analyze coded and interpreted data, and also use artificial intelligence technology to access literature, translate texts, design interview outlines, collect interview data, analyze and organize data, and create codes.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">During the process of qualitative research, researchers far exceed artificial intelligence in interpreting educational phenomena and issues, understanding contextual details, and performing theoretical analysis. However, artificial intelligence outperforms researchers in natural text generation, key concept identification, and data organization and analysis. Therefore, when researchers and artificial intelligence technologies collaborate, they can greatly enhance the standardization and effectiveness of qualitative research.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cstrong style=\"font-size: 18px; color: rgb(255, 153, 0);\" class=\"ql-lineHeight-1-75\">Applying Research Methods Embedded with \"Computational Thinking and Tools\"\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">Artificial intelligence science provides new methodological techniques for quantitatively analyzing qualitative data, advancing the process of empowering qualitative research with computational thinking and tools. Traditional qualitative research focuses on individual cases, examining micro-level educational behaviors and revealing the operational mechanisms of specific educational activities, but it struggles to effectively predict complex macro-level educational behaviors and emergent educational activities. Agent-based Modeling (ABM), a method based on computer simulation, is an educational thought experiment that simulates individual behaviors and social interactions of actors through computer programming, presenting the evolutionary development process of complex educational activities and thereby revealing the generation mechanism and behavioral patterns of educational activities. This method connects the micro-level and macro-level of educational activities, exploring the complex operational patterns of educational behaviors from an empirical perspective. Traditional grounded theory uses inductive analysis, which has subjective characteristics and cannot explain unstructured data, leading to doubts about research conclusions. In contrast, computational grounded theory takes the perspective of complex emergence, using machine learning and tracing algorithms to perform replicable iterative coding through dictionaries, thus discovering complex relationships in large-scale data and promoting the emergence of new concepts and theories. \"Mastering computational grounded methods is not only an addition to the sociological imagination proposed by Mills.\" Computational grounded theory offers a research path that builds concepts and theories through data and models, combining the explanatory nature of qualitative research with the predictive nature of quantitative research. Not only can it serve the development of the educational discipline and promote the production of theoretical knowledge, but it can also provide decision-making support for governments to formulate educational policies and seek key intervention variables for educational governance.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"font-size: 18px;\" class=\"ql-lineHeight-1-75\">In order to cope with the complex research ecology of the digital and intelligent era, it is necessary to add modules on computational thinking and methods in courses and textbooks on qualitative research methods, promoting the effective integration of computational thinking and tools into courses and teaching of qualitative research. During the educational research process, it is necessary to strengthen interdisciplinary communication and cooperation, building a research team composed of artificial intelligence experts, engineers, educational researchers, practitioners, and policymakers to promote the transformation of knowledge production methods in the educational discipline.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cimg alt=\"undefined\" src=\"https:\u002F\u002Fimage.51xinwei.com\u002F2025\u002F09\u002Fd026edc6e1a94f9c8bead76499b7210f\u002F下载.jpg\" width=\"undefined\" height=\"undefined\" style=\"display: block; margin: auto;\">\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"color: rgb(187, 187, 187);\">[News Source]\u003C\u002Fspan>\u003Cspan style=\"color: rgb(187, 187, 187); background-color: rgb(56, 56, 56);\">China Social Sciences Daily \u003C\u002Fspan>\u003Cspan style=\"color: rgb(187, 187, 187);\">Author is a professor at the School of Education, Capital Normal University \u003C\u002Fspan>\u003Ca href=\"https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FK89QSRJ6051495OJ.html\" rel=\"noopener noreferrer\" target=\"_blank\" style=\"color: rgb(187, 187, 187);\">https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FK89QSRJ6051495OJ.html\u003C\u002Fa>\u003C\u002Fp>\u003Cp class=\"ql-align-justify\">\u003Cspan style=\"color: rgb(187, 187, 187);\">（This article is reprinted by this website to provide readers with more information and news. The content does not constitute investment or consumption advice. If there are any questions about the facts of the article, please verify with relevant parties. The views expressed in the article are not the views of this website and are for reference only.）\u003C\u002Fspan>\u003C\u002Fp>","https:\u002F\u002Fxinwei-dev-test.oss-cn-shenzhen.aliyuncs.com\u002Fintelligent\u002Faudio%3A4ebb3ce4-f38f-4359-99fb-af344e2f533f%3A0.wav?Expires=1774838479&OSSAccessKeyId=LTAI5tNvY2RkKjZw4LLWsrPK&Signature=JChFMRVTsUwxGmSqoJdqwrh0JrM%3D","4ebb3ce4-f38f-4359-99fb-af344e2f533f",15590830]