人工智能时代教育研究的死亡与重生：问题与前景

doi:10.16382/j.cnki.1000-5560.2026.03.001

摘要/Abstract

摘要：

在生成式人工智能迅速发展的背景下，教育研究正面临深刻的认识论与方法论危机。传统教育研究长期受到若干结构性问题困扰，包括同行评审质量不稳、量化偏见及其带来的虚假精确性、定量与定性范式之争、跨情境过度推论、忽视学习者个体差异、以典型性假设主导研究想象，以及对教育成果的狭隘定义，等等。这些问题限制了教育研究的解释力、相关性与实际影响力。人工智能的出现不仅加剧了既有挑战，也带来了新的复杂性：AI技术迭代速度远超研究周期，使教育干预难以保持稳定；AI改变了“什么值得学习”的根本问题；人机协作学习情境的兴起要求研究者采用复杂性理论和分布式认知视角；教育研究必须面对监控、偏见与不平等等社会技术议题。同时，AI正在重塑文献综述、研究设计和知识生产本身，促使教育研究从关注因果链转向理解动态系统，从以人为中心的解释转向人机共生的认识论。本研究指出，教育研究需要从现有范式的局限中走出，发展更具适应性、参与性、多元性和面向未来的方法论框架，实现教育研究的“重生”。唯有如此，教育研究才能在AI时代保持其科学性、伦理性与社会价值。

关键词: 人工智能, 教育研究, 分布式认知, 复杂性, 方法论多元主义, 范式转型

Abstract:

The rapid advancement of generative artificial intelligence is reshaping the foundations of educational research, exposing longstanding methodological and epistemological limitations while introducing new complexities. Traditional educational research has been constrained by inconsistent peer review, quantitative bias and false precision, paradigm wars between qualitative and quantitative traditions, overgeneralization across diverse contexts, neglect of learner individuality, a dominant focus on typical rather than possible educational futures, and an overly narrow definition of educational outcomes. These issues have limited the field’s relevance, validity, and impact. AI amplifies these challenges by accelerating the obsolescence of educational interventions, fundamentally altering the question of what knowledge is of most worth, and transforming learning environments through human–AI collaboration. These developments demand research approaches informed by complexity science, distributed cognition, and sociotechnical critique, particularly in relation to ethics, equity, algorithmic bias, and surveillance. At the same time, AI is transforming literature reviews, research design, and epistemic assumptions, shifting educational research from static causal models toward dynamic, co-evolving systems, and from human-centered interpretation to hybrid human–machine knowledge production. This paper argues that educational research must move beyond existing paradigmatic limitations and embrace more adaptive, participatory, pluralistic, and future-oriented methodologies. Such a transformation represents not merely an improvement but a rebirth of educational research necessary for maintaining scientific rigor, ethical responsibility, and societal relevance in the age of AI.

Key words: artificial intelligence, educational research, distributed cognition, complexity, methodological pluralism, paradigm shift

赵勇, 尼尔·金斯顿, 里克·金斯伯格. 人工智能时代教育研究的死亡与重生：问题与前景[J]. 华东师范大学学报(教育科学版), 2026, 44(3): 1-14.

Zhao Yong, Neal Kingston, Rick Ginsberg. The Death and Rebirth of Educational Research in the Age of AI: Problems and Promises[J]. Journal of East China Normal University(Educationa, 2026, 44(3): 1-14.

参考文献

	Aczel B., Barwich A. -S., Diekman A. B., Fishbach A., Goldstone R., Gomez P., Gundersen O. E., von Hippel P. T., Holcombe A. O., Lewandowsky S., Nozari N., Pestilli F., Ioannidis J. P. A. (2025). The present and future of peer review: Ideas, interventions, and evidence. Proceedings of the National Academy of Sciences, 122(5), Article e2401232121.
	Amershi S., Weld D., Vorvoreanu M., Fourney A., Nushi B., Collisson P., Suh J., Iqbal S., Bennett P. N., Inkpen K. (2019). Guidelines for human-AI interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). Association for Computing Machinery, New York, NY, USA, Paper 3, 1–13.
	Baker R. S., Siemens G. (2014). Educational data mining and learning analytics. In Spector J. M., David Merrill M., Elen J., Bishop M. J. (Eds. ), Handbook of research on educational communications and technology (pp. 253–272). Springer.
	Bang M., Vossoughi S. (2016). Participatory design research and educational justice: Studying learning and relations within social change making. Cognition and Instruction, 34 (3), 173- 193.
	Barab S., Squire K. (2004). Design-based research: Putting a stake in the ground. Journal of the Learning Sciences, 13 (1), 1- 14.
	Barone T. , Eisner E. W. (2012). Arts based research. Sage.
	Bar-Yam Y. (2004). Multiscale variety in complex systems. Complexity, 9 (4), 37- 45.
	Benjamin R. (2019). Race after technology: Abolitionist tools for the new Jim code. Polity Press.
	Berliner D. C. (2002). Educational research: The hardest science of all. Educational Researcher, 31 (8), 18- 20.
	Berliner D. C., Glass G. V. (2014). 50 myths and lies that threaten America’s public schools: The real crisis in education. Teachers College Press.
	Biesta G. (2007). Why “what works” won’t work: Evidence-based practice and the democratic deficit in educational research. Educational Theory, 57 (1), 1- 22.
	Biesta G. (2009). Good education in an age of measurement: On the need to reconnect with the question of purpose in education. Educational Assessment, Evaluation and Accountability, 21, 33—46.
	Biesta G. (2010). Good education in an age of measurement: Ethics, politics, democracy. Routledge.
	Biesta G. (2020). Educational research: An unorthodox introduction. Bloomsbury Publishing.
	Boell S. K., Cecez-Kecmanovic D. (2015). On being ‘systematic’ in literature reviews in IS. Journal of Information Technology, 30 (2), 161- 173.
	Bornmann L., Mutz R., Daniel H. -D. (2010). A reliability-generalization study of journal peer reviews: A multilevel meta-analysis of inter-rater reliability and its determinants. PLoS ONE, 5(12), e14331. https://doi.org/10.1371/journal.pone.0014331.
	Brydon-Miller M., Greenwood D., Maguire P. (2003). Why action research? Action Research, 1(1), 9—28.
	Center for Innovation, Design, and Digital Learning. (2024). Artificial intelligence: The impact of AI on education for all learners. Author.
	Clandinin D. J. , Connelly F. M. (2000). Narrative inquiry: Experience and story in qualitative research. Jossey-Bass.
	Cobb P., Confrey J., diSessa A., Lehrer R., Schauble L. (2003). Design experiments in educational research. Educational Researcher, 32 (1), 9- 13.
	Cohen D. K., Spillane J. P. (1992). Chapter 1: Policy and practice: The relations between governance and instruction. Review of Research in Education, 18 (1), 3- 49.
	Crossley M., Watson K. (2003). Comparative and international research in education: Globalisation, context and difference. Routledge.
	Cuban L. (2013). Inside the black box of classroom practice: Change without reform in American education. Harvard Education Press.
	Darling-Hammond L. (2010). The flat world and education: How America’s commitment to equity will determine our future. Teachers College Press.
	Dhawan N., Batra S. (2021). Artificial intelligence in research: A boon or a bane. International Journal of Management, 12 (5), 31- 38.
	Duckworth A. L., Yeager D. S. (2015). Measurement matters: Assessing personal qualities other than cognitive ability for educational purposes. Educational Researcher, 44 (4), 237- 251.
	Eisner E. W. (2002). The arts and the creation of mind. Yale University Press.
	Foley Nicpon M., Allmon A., Sieck B., Stinson R. D. (2011). Empirical investigation of twice-exceptionality: Where have we been and where are we going? Gifted Child Quarterly, 55(1), 3—17.
	Fullan M. (2007). The new meaning of educational change (4th ed. ). Teachers College Press.
	Gage N. L. (1989). The paradigm wars and their aftermath: A “historical” sketch of research on teaching since 1989. Educational Researcher, 18 (7), 4- 10.
	Gardner H. (1983). Frames of mind: The theory of multiple intelligences. Basic Books.
	Gardner H., Hatch T. (1989). Multiple intelligences go to school: Educational implications of the theory of multiple intelligences. Educational Researcher, 18 (8), 4- 10.
	Gigerenzer G. (2004). Mindless statistics. The Journal of Socio-Economics, 33 (5), 587- 606.
	Gilson L. L., Maynard M. T., Jones Young N. C., Vartiainen M., Hakonen M. (2024). Artificial intelligence and literature reviews: Enhancing rigor and innovation in management research. Academy of Management Learning & Education, 23 (1), 1- 17.
	Ginsberg R., Zhao Y. (2025). Reconsidering literacy in an AI world. Phi Delta Kappan, 106 (7-8), 44- 47.
	Grabarić Andonovski I., Pongrac Habdija Z., Mrša V. (2019). What can we do to improve the peer review system? A short survey of food technology and biotechnology peer reviewers’ experience. Food Technology and Biotechnology, 57 (4), 436- 437.
	Greene M. (1995). Releasing the imagination: Essays on education, the arts, and social change. Jossey-Bass.
	Hanford E. (2019). Hard words: Why aren’t kids being taught to read? APM Reports.
	Hatch T. (1997). Getting specific about multiple intelligences. Educational Leadership, 54 (6), 26- 29.
	Heckman J. J., Kautz T. (2012). Hard evidence on soft skills. Labour Economics, 19 (4), 451- 464.
	HighWire. (2023, April 12). Navigating the peer review dilemma: Understanding shortages and charting a way forward.
	Hoffmann A. L. (2019). Where fairness fails: Data, algorithms, and the limits of antidiscrimination discourse. Information, Communication & Society, 22(7), 900—915.
	Hollan J., Hutchins E., Kirsh D. (2000). Distributed cognition: Toward a new foundation for human-computer interaction research. ACM Transactions on Computer-Human Interaction, 7 (2), 174- 196.
	Huff C. (2024, October 1). The promise and perils of using AI for research and writing. American Psychological Association, Psychological Topics.
	Huisman J., Smits J. (2017). Duration and quality of the peer review process: The author’s perspective. Scientometrics, 113 (1), 633- 650.
	Ito M., Soep E., Kligler-Vilenchik N., Shresthova S., Gamber-Thompson L., Zimmerman A. (2020). By any media necessary: The new youth activism. NYU Press.
	John O. P. , Naumann L. P. , Soto C. J. (2008). Paradigm shift to the integrative Big Five trait taxonomy: History, measurement, and conceptual issues. In John O. P., Robins R. W. Pervin L. A. (Eds. ), Handbook of personality: Theory and research (3rd ed. , pp. 114–158). The Guilford Press.
	Jovanović J., Gašević D., Dawson S., Pardo A., Mirriahi N. (2021). Learning analytics to improve literature reviews in education: An exploratory study. Computers & Education, 166, 104172.
	Kareiva P., Marvier M. (2012). What is conservation science? BioScience, 62(11), 962—969.
	Knox J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298—311.
	Koretz D. (2017). The testing charade: Pretending to make schools better. University of Chicago Press.
	Kuhn T. S. (1962). The structure of scientific revolutions. University of Chicago Press.
	Lagemann E. C. (2002). An elusive science: The troubling history of education research. University of Chicago Press.
	Lagemann E. C. (2008). Comments on Bulterman-Bos: Education research as a distributed activity across universities. Educational Researcher, 37 (7), 424- 428.
	Lagemann E. C., Shulman L. S. (1999). Issues in education research: Problems and possibilities. Jossey-Bass.
	Leitner M., Bernsteiner R. , Leitner D. (2023). Opportunities and pitfalls of AI-based tools for literature review: A critical review. International Journal of Educational Technology in Higher Education, 20(36). https://doi.org/10.1186/s41239-023-00403-x.
	Lewontin R. (2001). The triple helix: Gene, organism, and environment. Harvard University Press.
	Lincoln Y. S., Guba E. G. (1985). Naturalistic inquiry. Sage Publications.
	Lotriet C. J. (2012). Reviewing the review process: Identifying sources of delay. Australasian Medical Journal, 5 (1), 26- 29.
	Luckin R., Cukurova M. (2019). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology, 50 (6), 2824- 2838.
	Luckin R., Holmes W., Griffiths M., Forcier L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
	Lund B. D., Wang T., Mannino M., Chatfield J. (2023). ChatGPT and a new academic reality: Artificial intelligence written assignments and the future of higher education. Journal of Library Administration, 63 (3), 293- 312.
	Madanchian M., Taherdoost H. (2025). How artificial intelligence is changing the face of engineering research. International Journal of Engineering Research and Applications, 15 (2), 1- 15.
	Marshall G., Wallace M. (2019). Systematic reviews in educational research: Methodology, perspectives and application. Springer.
	McCloskey D. N., Ziliak S. T. (2010). The cult of statistical significance: How the standard error costs us jobs, justice, and lives. University of Michigan Press.
	Mckenney S., Reeves T. (2018). Conducting educational design research. Routledge.
	Mishra P., Mehta R. (2017). What we educators get wrong about 21st-century learning: Results of a survey. Journal of Digital Learning in Teacher Education, 33 (1), 6- 19.
	Mollick E., Mollick L. (2024). Instructors as innovators: A future-focused approach to new AI learning opportunities, with prompts. Arvix: Open-Access Repository. 2407.05181.
	Noble S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
	No Child Left Behind Act of 2001, 20 U. S. C. § 6319 (2011).
	OECD. (2016). PISA 2015 results (Volume I): Excellence and equity in education.
	O’Neil C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
	Papaspyridis A. (2020). The role of artificial intelligence in scientific research. International Journal of Scientific Research and Engineering Development, 3 (6), 160- 166.
	Perrotta C., Selwyn N. (2020). Deep learning goes to school: Toward a relational understanding of AI in education. Learning, Media and Technology, 45(3), 251—269.
	Phillips D., Schweisfurth M. (2014). Comparative and international education: An introduction to theory, method, and practice (2nd ed. ). Bloomsbury.
	Phillips D. C. , Burbules N. C. (2000). Postpositivism and educational research. Bloomsbury Publishing PLC.
	Pier E. L., Brauer M., Filut A., Kaatz A., Raclaw J., Nathan M. J., Ford C. E., Carnes M. (2018). Low agreement among reviewers evaluating the same NIH grant applications. Proceedings of the National Academy of Sciences, 115 (12), 2952- 2957. doi: 10.1073/pnas.1714379115
	Popper K. R. (1935). Logik der Forschung. Zur Erkenntnistheorie der modernen Naturwissenschaft. Julius Springer, Hutchinson & Co.
	Popper K. R. (1959). The logic of scientific discovery. Hutchinson and Co.
	Publons. (2018). Global state of peer review. Publons. https://publons.com/static/Publons-Global-State-Of-Peer-Review-2018.pdf.
	Ravitch S. M., Riggan M. (2016). Reason and rigor: How conceptual frameworks guide research. Sage publications.
	Reis S. M., Baum S. M., Burke E. (2014). An operational definition of twice-exceptional learners. Gifted Child Quarterly, 58 (3), 217- 230.
	Reiss S. (2000). Who am I? : The 16 basic desires that motivate our behavior and define our personality. Jeremy P. Tarcher/Putnam.
	Reiss S. (2004). Multifaceted nature of intrinsic motivation: The theory of 16 basic desires. Review of General Psychology, 8(3), 179—183. http://sitemaker.umich.edu/cognition.and.environment/files/reiss-intrinsic-mot.pdf.
	Ridley M. (2003). Nature via nurture: Genes, experience, and what makes us human. HarperCollins.
	Rigger S. (2009). The Perestroika movement in American political science and its lessons for Chinese political studies. Journal of Chinese Political Science, 14 (4), 369- 382.
	Ronksley-Pavia M. (2015). A model of twice-exceptionality. Australasian Journal of Gifted Education, 24 (1), 51- 68.
	Sahlberg P. (2015). Finnish lessons 2.0: What can the world learn from educational change in Finland? Teachers College Press.
	Schoenfeld A. H. (2004). The math wars. Educational Policy, 18 (1), 253- 286.
	Sellar S., Lingard B. (2013). PISA And the expanding role of the OECD in global educational governance. Educational Researcher, 42 (1), 7- 17.
	Selwyn N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
	Selwyn N., Hillman T., Bergviken Rensfeldt A., Perrotta C. (2023). Digital technologies and the automation of education—Key questions and concerns. Postdigital Science and Education, 5 (1), 15- 24.
	Shavelson R. J., Towne L. (2002). Scientific research in education. National Academy Press.
	Siemens G., Baker R. S. J. D. (2012). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 252–254).
	Slade S., Prinsloo P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57 (10), 1510- 1529.
	Slaughter R. A. (2002). Futures education: Catalyst for human and social development. Swinburne University.
	Spencer H. (1860). What knowledge is of most worth?. In Spencer H. (Ed. ), Education: Intellectual, moral, and physical (pp. 21–96). D. Appleton & Company. https://doi.org/10.1037/12158-001.
	Stake R. E. (2005). Qualitative case studies. In Denzin N. K. , Lincoln Y. S. (Eds. ), The Sage handbook of qualitative research (3rd ed. , pp. 443–466). Sage.
	Tan C. Y., Dimmock C. (2014). How Singapore interprets and implements “successful” education policies from elsewhere: A perspective from the school leadership for systematic improvement (LSI) program. Educational Management Administration & Leadership, 42 (2), 237- 258.
	Tennant J. P., Ross-Hellauer T. (2020). The limitations to our understanding of peer review. Research Integrity and Peer Review, 5 (1), 6. doi: 10.1186/s41073-020-00092-1
	Trail B. (2021). Twice-exceptional gifted children: Understanding, teaching, and counseling gifted students. Prufrock Press.
	Tropini C., et al. (2023). The peer review dilemma: Causes, consequences, and potential solutions. Microbiology Spectrum, 14 (3), e01091- 23.
	Trout J. D. (2002). Scientific explanation and the sense of understanding. Philosophy of Science, 69 (2), 212- 233. doi: 10.1086/341050
	van de Schoot R., de Bruin J., Schram R., Zahedi P., de Boer J., Weijdema F., Kramer B., Huijts M., Hoogerwerf M., Ferdinands G., Harkema A., Willemsen J., Ma Y., Fang Q., Hindriks S., Tummers L., Oberski D. L. (2021). An open source machine learning framework for efficient and transparent systematic reviews. Nature Machine Intelligence, 3 (2), 125- 133.
	Williamson B. (2021). Big data in education: The digital future of learning, policy and practice. Sage.
	Williamson B., Eynon R., Potter J. (2020). Pandemic politics, pedagogies and practices: Digital technologies and distance education during the coronavirus emergency. Learning, Media and Technology, 45(2), 107—114.
	Wills M. (2024). The history of peer review is more interesting than you think. JSTOR Daily.
	Yin L., Li Y., Qian X., Yu C. (2022). Artificial intelligence in systematic literature review: A systematic review. Scientometrics, 127, 905- 938.
	Yin R. K. (2017). Case study research and applications: Design and methods (6th ed. ). Sage.
	Zawacki-Richter O., Marín V. I., Bond M., Gouverneur F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39.
	Zhai X., Chu X., Chai C. S., Jong M. S. Y., Istenic A., Spetor M., Liu J., Yuan J., Li Y. (2021). A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity, 2021, 8812542.
	Zhao Y. (2017). What works can hurt: Side effects in education. Journal of Educational Change, 18 (1), 1- 19.
	Zhao Y. (2018). What works may hurt: Side effects in education. Teachers College Press.
	Zhao Y. (2020). Two decades of havoc: A synthesis of criticism against PISA. Journal of Educational Change, 21 (2), 1- 22.
	Zhao Y. (2022). Learners without borders: New learning pathways for all students. Teachers College Press.
	Zhao Y. (2024). Artificial intelligence and education: End the grammar of schooling. ECNU Review of Education, 8 (1), 3- 20.
	Zhao Y., Basham J., Travers J. (2022). Redefining human talents: Gifted education in the age of smart machines. In R. J. Sternberg, D. Ambrose, & S. Karami (Eds. ), The Palgrave handbook of transformational giftedness for education (pp. 403–425). Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-91618-3_21.
	Zhao Y., Beghetto R. A. (2024). Effects and side effects: What is missing in education research. Review of Research in Education, 48 (1), vii- xxviii.
	Zhao Y., Gearin B. (2018). Reach for greatness: Personalizable education for all children. Corwin.
	Zhao Y., Watterston J. (2021). The changes we need: Education post COVID-19. Journal of Educational Change, 22, 3- 12.
	Zhao Y., Zhong R. (2024). Paradigm shifts in education: An ecological analysis. ECNU Review of Education, 8 (1), 21- 40.
	Zhong R., Zhao Y. (2025). Education paradigm shifts in the age of AI: A spatiotemporal analysis of learning. ECNU Review of Education, 8 (2), 319- 342.
	Zul M. (2023). How many journal articles have been published. PublishingState. https://publishingstate.com/how-many-journal-articles-have-been-published/2023/.

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