人工智能的语言优势和不足：基于大语言模型与真实学生语文能力的比较

doi:10.3724/SP.J.1041.2025.0947

摘要/Abstract

摘要：

采用定量和定性相结合的混合研究方法, 从准确性、规范性、情感性和创造性四个维度评估了人工智能的语言优势和不足。研究1发现, 相对于真实学生, GPT-4现代文知识(尤其概念知识)的准确性较高, 但其古代诗文和语言文字运用的准确性较低; GPT-4规范性得分与真实学生相当, 情感性和创造性超过及格水平、但低于真实学生, 且前者最优个体的规范性、情感性得分与真实学生最高分持平。研究2基于文心ERNIE-4重复验证了上述结果, 且ERNIE-4的规范性得分高于真实学生。研究揭示了人工智能在现代文知识、规范领域的优势和古代诗文知识的不足, 以及情感性与创造性方面的潜力。这些发现有助于理解和提升人工智能的文化适应性和人性化、个性化生成能力, 也对反思和培养人类的独特优势具有重要启发。

关键词: 大语言模型, 语文能力, 准确性, 情感性, 创造性

Abstract:

Previous research on generative artificial intelligence (AI) has been primarily conducted in the English context, but it remains unclear about linguistic strength and weakness of generative AI in the Chinese context. This study focuses on the accuracy and normativity, affectivity, and creativity of AI in generating language knowledge, and explores its cultural adaptability and ability to generate humanized and personalized content. Evaluating and analyzing these key indicators helps us gain a deeper understanding of the linguistic strengths and weaknesses of AI, as well as cultivating the unique advantages of humans in education.

By combining quantitative and qualitative methods, we evaluated the differences in knowledge accuracy, normativity, affectivity, and creativity between large language models and real students. Specifically, using an explanatory sequential design in the mixed-methods framework, we first tested group differences in each indicator among GPT-4 and ERNIE-4 versus real students on knowledge accuracy, normativity, affectivity, and creativity to test the. Next, through content analyses, we explored the specific performance of large language models on each indicator and the mechanism of their linguistic strengths and weaknesses.

Study 1 found that compared to real students, GPT-4 exhibited higher accuracy in modern text knowledge (especially conceptual knowledge), but lower accuracy in ancient poetry and language usage. The knowledge normativity of GPT-4 were comparable to those of real students, while its affectivity and creativity were lower than those of real students. Moreover, the highest individual scores of GPT-4 in normativity and emotionality were on comparable with the highest scores of real students. Study 2, based on ERNIE-4, confirmed the aforementioned results, and the accuracy in ancient poetry was still lower than that of real students. The results exhibited the advantages of artificial intelligence in the areas of modern knowledge and norms, its shortcomings in ancient poetry knowledge, and its potential in affective and creative expressions.

Taken together, the current findings demonstrate the linguistic strength of generative AI in the knowledge accuracy of modern Chinese literary, and the weakness regarding ancient Chinese poetry and affective and creative writings, as well as generative AI’s potential in normative and affective expressions. This sheds light on the field of the cultural adaptability, affective and creative expressions of generative AI, and has valuable implications for the AI-assistant teaching practice in the Chinese context.

Key words: large language models, language proficiency, accuracy, emotionality, creativity

中图分类号:

B842

高承海, 党宝宝, 王冰洁, 吴胜涛. (2025). 人工智能的语言优势和不足：基于大语言模型与真实学生语文能力的比较. 心理学报, 57(6), 947-966.

GAO Chenghai, DANG Baobao, WANG Bingjie, WU Michael Shengtao. (2025). The linguistic strength and weakness of artificial intelligence: A comparison between Large Language Model(s) and real students in the Chinese context. Acta Psychologica Sinica, 57(6), 947-966.

图/表 6

参考文献 92

[1]	Adeshola I., & Adepoju A. P. (2024). The opportunities and challenges of ChatGPT in education. Interactive Learning Environments, 32(10), 6159-6172.
[2]	Adiguzel T., Kaya M. H., & Cansu F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429.
[3]	AlAfnan M. A., Dishari S., Jovic M., & Lomidze K. (2023). ChatGPT as an educational tool: Opportunities, challenges, and recommendations for communication, business writing, and composition courses. Journal of Artificial Intelligence and Technology, 3(2), 60-68.
[4]	Alawida M., Mejri S., Mehmood A., Chikhaoui B., & Isaac Abiodun O. (2023). A comprehensive study of ChatGPT: Advancements, limitations, and ethical considerations in natural language processing and cybersecurity. Information, 14(8), 462.
[5]	Ali O., Murray P. A., Momin M., Dwivedi Y. K., & Malik T. (2024). The effects of artificial intelligence applications in educational settings: Challenges and strategies. Technological Forecasting and Social Change, 199, 123076.
[6]	Alkaissi H., & McFarlane S. I. (2023). Artificial hallucinations in ChatGPT: Implications in scientific writing. Cureus, 15(2), e35179.
[7]	Alneyadi S., & Wardat Y. (2023). ChatGPT: Revolutionizing student achievement in the electronic magnetism unit for eleventh-grade students in Emirates schools. Contemporary Educational Technology, 15(4), ep448.
[8]	AlZu'bi S., Mughaid A., Quiam F., & Hendawi S. (2024). Exploring the capabilities and limitations of Chatgpt and alternative big language models. Artificial intelligence and applications, 2(3), 28-37.
[9]	Amaro I., Della Greca A., Francese R., Tortora G., & Tucci C. (2023). AI unreliable answers:A case study on ChatGPT. In Majeed, A., & Hwang, S. O. (Eds.), International conference on human-computer interaction (pp. 23-40). Cham: Springer Nature Switzerland.
[10]	Antar D. (2023). The effectiveness of using ChatGPT4 in creative writing in Arabic: Poetry and short story as a model. Information Sciences Letters, 12(12), 2245-2459.
[11]	Ardichvili A., Page V., & Wentling T. (2003). Motivation and barriers to participation in virtual knowledge-sharing communities of practice. Journal of Knowledge Management, 7(1), 64-77.
[12]	Arif T. B., Munaf U., & Ul-Haque I. (2023). The future of medical education and research: Is ChatGPT a blessing or blight in disguise? Medical Education Online, 28(1), 2181052.
[13]	Ariyaratne S., Iyengar K. P., Nischal N., Chitti Babu N., & Botchu R. (2023). A comparison of ChatGPT-generated articles with human-written articles. Skeletal Radiology, 52(9), 1755-1758. doi: 10.1007/s00256-023-04340-5 pmid: 37059827
[14]	Assunção G., Patrão B., Castelo-Branco M., & Menezes P. (2022). An overview of emotion in artificial intelligence. IEEE Transactions on Artificial Intelligence, 3(6), 867-886.
[15]	Atari M., Xue M. J., Park P. S., Blasi D., & Henrich J. (2023). Which humans? (Working Paper). https://doi.org/10.31234/osf.io/5b26t
[16]	Babina T., Fedyk A., He A., & Hodson J. (2024). Artificial intelligence, firm growth, and product innovation. Journal of Financial Economics, 151, 103745.
[17]	Baidoo-Anu D., & Owusu Ansah L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potentialbenefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62.
[18]	Boden M. A. (1998). Creativity and artificial intelligence. Artificial Intelligence, 103(1-2), 347-356.
[19]	Bossaerts P., & Murawski C. (2017). Computational complexity and human decision-making. Trends in Cognitive Sciences, 21(12), 917-929. doi: S1364-6613(17)30193-6 pmid: 29149998
[20]	Cai Z. G., Haslett D. A., Duan X., Wang S., & Pickering M. J. (2023). Does ChatGPT resemble humans in language use? arXiv preprint arXiv:2303.08014.
[21]	Chen B. Y., & Chen Y. (2024). The firsthand epistemic reduction model of human language acquisition: A discussion based on the linguistic epistemic reduction model of ChatGPT. Journal of Peking University (Philosophy and Social Sciences), (2), 167-174.
	[陈保亚, 陈樾. (2024). 人类语言习得的亲知还原模式——从ChatGPT的言知还原模式说起. 北京大学学报(哲学社会科学版), (2), 167-174.]
[22]	Chen H. S. (2020). Unified compilation of junior high school Chinese textbooks and the cultivation of classical Chinese reading skills. Curriculum, Teaching Material and Method, 40(7), 57-62.
	[陈恒舒. (2020). 统编初中语文教材与文言文阅读能力培养. 课程·教材·教法, 40(7), 57-62.]
[23]	Chen L., Chen P., & Lin Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278.
[24]	Chignell M., Wang L., Zare A., & Li J. (2023). The evolution of HCI and human factors: Integrating human and artificial intelligence. ACM Transactions on Computer- human Interaction, 30(2), 1701-1730.
[25]	Cooper G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32(3), 444-452.
[26]	Cotton D. R., Cotton P. A., & Shipway J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 60(3), 228-239.
[27]	Creswell J. W. (2009). Mapping the field of mixed methods research. Journal of Mixed Methods Research, 3(2), 95-108.
[28]	Dillion D., Tandon N., Gu Y., & Gray K. (2023). Can AI language models replace human participants? Trends in Cognitive Sciences, 27(7), 597-600. doi: 10.1016/j.tics.2023.04.008 pmid: 37173156
[29]	Divekar R. R., Drozdal J., Chabot S., Zhou Y., Su H., Chen Y.,... Braasch J. (2022). Foreign language acquisition via artificial intelligence and extended reality: Design and evaluation. Computer Assisted Language Learning, 35(9), 2332-2360.
[30]	Esmaeilzadeh P. (2023). The role of ChatGPT in disrupting concepts, changing values, and challenging ethical norms: A qualitative study. AI and Ethics, 3(5), 291-304.
[31]	Eysenbach G. (2023). The role of ChatGPT, generative language models, and artificial intelligence in medical education: A conversation with ChatGPT and a call for papers. JMIR Medical Education, 9(1), e46885.
[32]	Fergus S., Botha M., & Ostovar M. (2023). Evaluating academic answers generated using ChatGPT. Journal of Chemical Education, 100(4), 1672-1675.
[33]	Filippi S. (2023). Measuring the impact of ChatGPT on fostering concept generation in innovative product design. Electronics, 12(16), 3535.
[34]	Fui-Hoon Nah F., Zheng R., Cai J., Siau K., & Chen L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277-304.
[35]	Fyfe P. (2023). How to cheat on your final paper: Assigning AI for student writing. AI & Society, 38(4), 1395-1405
[36]	Gala D., & Makaryus A. N. (2023). The utility of language models in cardiology: A narrative review of the benefits and concerns of ChatGPT-4. International Journal of Environmental Research and Public Health, 20(15), 6438.
[37]	Gao C. A., Howard F. M., Markov N. S., Dyer E. C., Ramesh S., Luo Y., & Pearson A. T. (2023). Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers. NPJ Digital Medicine, 6(1), 75.
[38]	Gao H., & Chen H. B. (2021). On the differences between artificial intelligence and human intelligence. Journal of Northeastern University: Social Science Edition, 23(2), 15-20.
	[高华, 陈红兵. (2021). 论人工智能与人类智能之差异. 东北大学学报:社会科学版, 23(2), 15-20.]
[39]	Grassini S. (2023). Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), 692.
[40]	Guo B., Zhang X., Wang Z., Jiang M., Nie J., Ding Y.,... Wu Y. (2023). How close is ChatGPT to human experts? comparison corpus, evaluation, and detection. arXiv preprint arXiv:2301.07597.
[41]	Herbold S., Hautli-Janisz A., Heuer U., Kikteva Z., & Trautsch A. (2023). A large-scale comparison of human-written versus ChatGPT-generated essays. Scientific Reports, 13(1), 18617.
[42]	Hofweber J., & Graham S. (2017). Linguistic creativity in language learning: Investigating the impact of creative text materials and teaching approaches in the second language classroom. Scottish Languages Review, 33, 19-28.
[43]	Imran M., & Almusharraf N. (2023). Analyzing the role of ChatGPT as a writing assistant at higher education level: A systematic review of the literature. Contemporary Educational Technology, 15(4), ep464.
[44]	Jauhiainen J. S., & Guerra A. G. (2023). Generative AI and ChatGPT in school children’s education: Evidence from a school lesson. Sustainability, 15(18), 14025.
[45]	King M. R., & ChatGPT. (2023). A conversation on artificial intelligence, chatbots, and plagiarism in higher education. Cellular and Molecular Bioengineering, 16(1), 1-2. doi: 10.1007/s12195-022-00754-8 pmid: 36660590
[46]	Kiryakova G., & Angelova N. (2023). ChatGPT—A challenging tool for the university professors in their teaching practice. Education Sciences, 13(10), 1056.
[47]	Kumar S., & Choudhury S. (2023). Normative ethics, human rights, and artificial intelligence. AI and Ethics, 3(2), 441-450.
[48]	Kurlinkus W. (2023). Teaching ChatGPT for grant writing: An English department senior capstone. Writers: Craft & Context, 4(1), 115-124.
[49]	Li Z., Qiu W., Ma P., Li Y., Li Y., He S.,... Gu W. (2024). An empirical study on large language models in accuracy and robustness under Chinese industrial scenarios. arXiv preprint arXiv:2402.01723.
[50]	Li Z. M. (2023). The Nature of ChatGPT and Its Impact on Education. Chinese Journal of ICT in Education, 29(3), 12-18.
	[李志民. (2023). ChatGPT本质分析及其对教育的影响. 中国教育信息化, 29(3), 12-18.]
[51]	Lin C. D. (2014). A few studies on psychology of creativity. Journal of Shandong Normal University (Humanities and Social Sciences), 59(6), 5-14.
	[林崇德. (2014). 创造性心理学的几项研究. 山东师范大学学报(人文社会科学版), 59(6), 5-14.]
[52]	Lingard L. (2023). Writing with ChatGPT: An illustration of its capacity, limitations & implications for academic writers. Perspectives on Medical Education, 12(1), 261-270.
[53]	Liu Y., Han T., Ma S., Zhang J., Yang Y., Tian J.,... Ge B. (2023). Summary of ChatGPT-related research and perspective towards the future of large language models. Meta-Radiology, 1(2), 100017.
[54]	Lo C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410.
[55]	Loos E., Gröpler J., & Goudeau M. L. S. (2023). Using ChatGPT in education: Human reflection on ChatGPT’s self-reflection. Societies, 13(8), 196.
[56]	Lund B. D., & Wang T. (2023). Chatting about ChatGPT: How may AI and GPT impact academia and libraries? Library Hi Tech News, 40(3), 26-29.
[57]	Martin J. L. (2023). The ethico-political universe of ChatGPT. Journal of Social Computing, 4(1), 1-11.
[58]	Mei Q., Xie Y., Yuan W., & Jackson M. O. (2024). A Turing test of whether AI chatbots are behaviorally similar to humans. Proceedings of the National Academy of Sciences, 121(9), e2313925121.
[59]	Minaee S., Mikolov T., Nikzad N., Chenaghlu M., Socher R., Amatriain X., & Gao J. (2024). Large language models: A survey. arXiv preprint arXiv:2402.06196.
[60]	Mindner L., Schlippe T., & Schaaff K. (2023). Classification of human-and AI-generated texts:Investigating features for ChatGPT. In International Conference on Artificial Intelligence in Education Technology (pp. 152-170). Singapore: Springer Nature Singapore.
[61]	Naous T., Ryan M. J., Ritter A., & Xu W. (2023). Having beer after prayer? Measuring cultural bias in large language models. arXiv preprint arXiv:2305.14456.
[62]	Nazir A., & Wang Z. (2023). A comprehensive survey of ChatGPT: Advancements, applications, prospects, and challenges. Meta-radiology, 1(2), 100022.
[63]	Peng B., Li C., He P., Galley M., & Gao J. (2023). Instruction tuning with GPT-4. arXiv preprint arXiv: 2304.03277.
[64]	Rahman M. M., & Watanobe Y. (2023). ChatGPT for education and research: Opportunities, threats, and strategies. Applied Sciences, 13(9), 5783.
[65]	Ray P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3, 121-154.
[66]	Richard F. D., Bond Jr C. F., & Stokes-Zoota J. J. (2003). One hundred years of social psychology quantitatively described. Review of General Psychology, 7(4), 331-363.
[67]	Rimban E. L. (2023). Challenges and limitations of ChatGPT and other large language models. International Journal of Arts and Humanities, 4(1), 147-152.
[68]	Roumeliotis K. I., & Tselikas N. D. (2023). ChatGPT and open-AI models: A preliminary review. Future Internet, 15(6), 192.
[69]	Rudolph J., Tan S., & Tan S. (2023). War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education. Journal of Applied Learning and Teaching, 6(1), 364-389.
[70]	Sallam M. (2023). ChatGPT utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare, 11(6), 887.
[71]	Seals S. M., & Shalin V. L. (2023). Evaluating the deductive competence of large language models. arXiv preprint arXiv:2309.05452.
[72]	Sharma S., & Yadav R. (2022). Chat GPT-A technological remedy or challenge for education system. Global Journal of Enterprise Information System, 14(4), 46-51.
[73]	Shidiq M. (2023). The use of artificial intelligence-based Chat-gpt and its challenges for the world of education; from the viewpoint of the development of creative writing skills. In Mundiri, A. (Ed.), Proceeding of International Conference on Education, Society and Humanity (pp. 353-357). Indonesia: Postgraduate program of Nurul Jadid University.
[74]	Shoufan A. (2023). Can students without prior knowledge use ChatGPT to answer test questions? An empirical study. ACM Transactions on Computing Education, 23(4), 1-29.
[75]	Simon W. (2023). Distinguishing between student and AI-generated writing: A critical reflection for teachers. Metaphor, (3), 16-23.
[76]	Spennemann D. H. (2023). ChatGPT and the generation of digitally born “knowledge”: How does a generative AI language model interpret cultural heritage values? Knowledge, 3(3), 480-512.
[77]	Storch N. (2005). Collaborative writing: Product, process, and students’ reflections. Journal of Second Language Writing, 14(3), 153-173.
[78]	Strachan J. W., Albergo D., Borghini G., Pansardi O., Scaliti E., Gupta S.,... Becchio C. (2024). Testing theory of mind in large language models and humans. Nature Human Behaviour, 8, 1285-1295. doi: 10.1038/s41562-024-01882-z pmid: 38769463
[79]	Suárez A., Díaz‐Flores García V., Algar J., Gómez Sánchez M., Llorente de Pedro M., & Freire Y. (2024). Unveiling the ChatGPT phenomenon: Evaluating the consistency and accuracy of endodontic question answers. International Endodontic Journal, 57(1), 108-113.
[80]	Taniguchi T., Ugur E., Hoffmann M., Jamone L., Nagai T., Rosman B.,... Wörgötter F. (2018). Symbol emergence in cognitive developmental systems: A survey. IEEE transactions on Cognitive and Developmental Systems, 11(4), 494-516.
[81]	Tian S., Jin Q., Yeganova L., Lai P. T., Zhu Q., Chen X.,... Lu Z. (2024). Opportunities and challenges for ChatGPT and large language models in biomedicine and health. Briefings in Bioinformatics, 25(1), 493.
[82]	Thirunavukarasu A. J., Ting D. S. J., Elangovan K., Gutierrez L., Tan T. F., & Ting D. S. W. (2023). Large language models in medicine. Nature Medicine, 29(8), 1930-1940. doi: 10.1038/s41591-023-02448-8 pmid: 37460753
[83]	Wang F. (2023). ChatGPT: Updating our understanding of machine intelligence. Exploration and Controversy, (5), 22-24.
	[王峰. (2023). ChatGPT:更新对机器智能的认知. 探索与争鸣, (5), 22-24.]
[84]	Wen H. B., & Yang J. Q. (2020). The Impact of scoring methods for subjective questions in Chinese language Gaokao reading on exam quality. China Examinations, (3), 1-5.
	[温红博, 杨建强. (2020). 高考语文阅读主观题评分方法对考试质量的影响. 中国考试, (3), 1-5.]
[85]	Wu T., He S., Liu J., Sun S., Liu K., Han Q. L., & Tang Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122-1136.
[86]	Yeadon W., Inyang O. O., Mizouri A., Peach A., & Testrow C. P. (2023). The death of the short-form physics essay in the coming AI revolution. Physics Education, 58(3), 035027.
[87]	Yu H., Zhao J. Y., & Li Y. (2018). Research on the positioning, function, and content of the new Gaokao Chinese language subject. Curriculum, Teaching Material and Method, 38(5), 11-16.
	[于涵, 赵静宇, 李勇. (2018). 新高考语文科的定位、功能和考查内容研究. 课程·教材·教法, 38(5), 11-16.]
[88]	Zhao Y., Huang Z., Seligman M., & Peng K. (2024). Risk and prosocial behavioural cues elicit human-like response patterns from AI chatbots. Scientific Reports, 14(1), 7095.
[89]	Zhang H. P., Li L. H., & Li C. J. (2023). ChatGPT performance evaluation on Chinese language and risk measures. Data Analysis and Knowledge Discovery, 7(3), 16-25. doi: 10.11925/infotech.2096-3467.2023.0214
	[张华平, 李林翰, 李春锦. (2023). ChatGPT中文性能测评与风险应对. 数据分析与知识发现, 7(3), 16-25.] doi: 10.11925/infotech.2096-3467.2023.0214
[90]	Zhang Q. L. (2010). Research on the linguistic features of “simple” classical Chinese texts: A study based on classical Chinese selections in junior high school textbooks over the past century. Application of Language and Script, (3), 115-122.
	[张秋玲. (2010). 文言文“浅易”的语词特征研究——以百年来初中教科书中的文言选篇为研究对象. 语言文字应用, (3), 115-122.]
[91]	Zhang Y. X. (2023). Characteristics and development paths of verbal thinking in Chinese language learning. Curriculum, Teaching Material and Method, 43(9), 93-100.
	[张永祥. (2023). 语文学习中言语思维的特点与发展路径. 课程·教材·教法, 43(9), 93-100.]
[92]	Zhou J., Ke P., Qiu X., Huang M., & Zhang J. (2023). ChatGPT: Potential, prospects, and limitations. Frontiers of Information Technology & Electronic Engineering, 25(2), 6-11.

知识类型	GPT-4 (N = 84)				真实学生(N = 84)				t	p	Cohen's d
知识类型	Min	Max	M	SD	Min	Max	M	SD	t	p	Cohen's d
现代文	20.00	31.00	26.44	2.82	5.00	25.00	15.51	3.60	21.90	< 0.001	3.23
古代诗文	11.00	22.00	17.58	2.73	18.00	34.00	26.51	3.67	−17.91	< 0.001	3.23
语言文字运用	4.00	17.00	10.88	2.11	6.00	20.00	13.60	3.06	−6.70	< 0.001	2.63

知识类型	GPT-4 (N = 84)				真实学生(N = 84)				t	p	Cohen's d
知识类型	Min	Max	M	SD	Min	Max	M	SD	t	p	Cohen's d
现代文	20.00	31.00	26.44	2.82	5.00	25.00	15.51	3.60	21.90	< 0.001	3.23
古代诗文	11.00	22.00	17.58	2.73	18.00	34.00	26.51	3.67	−17.91	< 0.001	3.23
语言文字运用	4.00	17.00	10.88	2.11	6.00	20.00	13.60	3.06	−6.70	< 0.001	2.63

维度	GPT-4 (N = 84)				真实学生(N = 84)				t	p	Cohen's d
维度	Min	Max	M	SD	Min	Max	M	SD	t	p	Cohen's d
规范性	5.00	8.50	7.45	0.65	1.00	8.50	7.36	0.96	0.75	0.453	0.82
情感性	5.00	8.50	6.52	1.06	1.50	8.50	7.23	0.80	−4.91	<0.001	0.94
创造性	6.13	7.50	6.62	0.24	3.38	8.75	7.35	0.73	−8.70	<0.001	0.54
写作总分	16.25	23.25	20.57	1.36	6.50	25.25	21.89	2.21	−4.66	<0.001	1.83

维度	GPT-4 (N = 84)				真实学生(N = 84)				t	p	Cohen's d
维度	Min	Max	M	SD	Min	Max	M	SD	t	p	Cohen's d
规范性	5.00	8.50	7.45	0.65	1.00	8.50	7.36	0.96	0.75	0.453	0.82
情感性	5.00	8.50	6.52	1.06	1.50	8.50	7.23	0.80	−4.91	<0.001	0.94
创造性	6.13	7.50	6.62	0.24	3.38	8.75	7.35	0.73	−8.70	<0.001	0.54
写作总分	16.25	23.25	20.57	1.36	6.50	25.25	21.89	2.21	−4.66	<0.001	1.83

知识类型	ERNIE-4 (N = 84)				真实学生(N = 84)				t	p	Cohen's d
知识类型	Min	Max	M	SD	Min	Max	M	SD	t	p	Cohen's d
现代文	20.00	31.00	26.10	2.40	5.00	25.00	15.14	3.94	21.75	< 0.001	3.26
古代诗文	18.00	25.00	21.38	1.33	17.00	33.00	26.37	3.89	−11.12	< 0.001	2.91
语言文字运用	9.00	15.00	12.58	1.40	7.00	20.00	13.33	3.08	−2.03	0.044	2.39