Published August 20, 2026 | Version v1
Journal article Open

APPLICATION SCENARIOS, EFFECTIVENESS, AND RISK BOUNDARIES OF GENERATIVE AI IN CLASSROOM TEACHING

Authors/Creators

  • 1. "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers" National Research University

Description

Abstract: Generative artificial intelligence (GenAI) is rapidly penetrating classroom teaching. However, existing studies mostly focus on single scenarios or isolated issues, lacking an integrated analysis of application scenarios, actual effectiveness, and risk boundaries. This study addresses three questions: What typical application scenarios has GenAI formed in classroom teaching? What effectiveness evidence has empirical research reported? How should risk boundaries be delineated? Following the PRISMA protocol, a systematic literature analysis was conducted. A total of 2,225 records were retrieved from Web of Science, Scopus, ERIC, IEEE Xplore, and CNKI, and 35 representative studies were included after screening. A three-dimensional scenario-effectiveness-risk framework was constructed for coding and synthesis. Findings: (1) GenAI has formed stable application patterns in five scenarios: lesson preparation and resource generation, in-class teaching assistance, practice and instant feedback, personalized tutoring, and academic assessment; (2) effectiveness evidence is clearly stratified: strongest for instant feedback and personalized tutoring, mixed for writing instruction and higher-order thinking, and still exploratory for open-ended assessment, while configurational evidence indicates that effectiveness stems from human-AI collaborative instructional design rather than the technology itself; (3) five major risks (academic integrity, cognitive overreliance, hallucination, digital divide, and data privacy) vary in severity across scenarios. Accordingly, this study proposes a risk-tiered principle for the human-AI division of labor: free use in low-risk scenarios, guided use in medium-risk scenarios, and restricted use in high-risk scenarios, with teachers remaining learning designers and final arbiters of assessment. The study offers an integrated framework and practical guidance for rational GenAI use in classroom teaching.

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References

  • Nikolic S., Vu B.A., Di Y., et al. Generative Artificial Intelligence (GenAI) 24x7 Tutor: A simulation of the capability of ChatGPT, Wolfram GPT and Tutor Me GPT to accurately and effectively tutor engineering and math content. STEM Education, 2026, Vol.6, No.1, pp.56-83. DOI: 10.3934/steme.2026004
  • Li C., Cui H., Hagedorn L.S. The cognitive impact of ChatGPT in higher education: A systematic review of critical and creative thinking outcomes. Computers and Education: Artificial Intelligence, 2026, Vol.10, Art.100571. DOI: 10.1016/j.caeai.2026.100571
  • Wei Shunping. Potential and risks of ChatGPT in higher education: Experience and implications from American universities. Modern Distance Education, 2024, No.3, pp.18-27. DOI: 10.13927/j.cnki.yuan.20240628.003
  • Lee V.R., Pope D., Miles S., Zarate R.C. Cheating in the age of generative AI: A high school survey study of cheating behaviors before and after the release of ChatGPT. Computers and Education: Artificial Intelligence, 2024, Vol.7, Art.100253. DOI: 10.1016/j.caeai.2024.100253
  • Wang Tianping, Qin Peng. The risk of thinking dependence in educational applications of generative AI and its avoidance. Modern Distance Education Research, 2026, Vol.38, No.3, pp.39-46. DOI: 10.3969/j.issn.1009-5195.2026.03.005
  • Alyafei R.A.S.M., Al-Kfairy M., Alfandi O., et al. Safeguarding students' academic integrity in the age of generative AI: challenges and solutions. Kybernetes, 2026. DOI: 10.1108/K-04-2025-0878
  • Mehrvarz M., Salimi G., Abdoli S., et al. How does students' perception of ChatGPT shape online learning engagement and performance? Computers and Education: Artificial Intelligence, 2025, Vol.9, Art.100459. DOI: 10.1016/j.caeai.2025.100459
  • Adel A., Alani N. Can generative AI reliably synthesise literature? exploring hallucination issues in ChatGPT. AI & Society, 2025, Vol.40, No.8, pp.6799-6812. DOI: 10.1007/s00146-025-02406-7
  • Lu Yu, Yu Jinglei, Chen Penghe, et al. Educational applications and prospects of generative artificial intelligence: A case study of the ChatGPT system. China Distance Education, 2023, No.4, pp.24-31,51. DOI: 10.13541/j.cnki.chinade.20230301.001
  • Yu Y., Wei W., Chen Z. Comparing learners' engagement strategies with feedback from a Generative AI chatbot and peers in an interpreter training programme: a quasi-experimental study. The Interpreter and Translator Trainer, 2025, Vol.19, No.3-4, pp.338-356. DOI: 10.1080/1750399X.2025.2533069
  • Beckman K., Apps T., Howard S.K., et al. The GenAI divide among university students: A call for action. The Internet and Higher Education, 2025, Vol.67, Art.101036. DOI: 10.1016/j.iheduc.2025.101036
  • Lu Yu, Tang Xiaoyu. Hierarchical forms and advancement paths of generative AI empowering classroom teaching. E-education Research, 2025, Vol.46, No.6, pp.75-82,106. DOI: 10.13811/j.cnki.eer.2025.06.010
  • Jiang Fengguang, Zhang Qinglin, Wu Xinyi, et al. Systematic reconstruction of medical classroom teaching driven by generative AI: A quasi-experimental study from the perspective of constructive alignment. Modern Educational Technology, 2026, Vol.36, No.1, pp.84-93. DOI: 10.3969/j.issn.1009-8097.2026.01.009
  • Liu Ming, Wu Zhongming, Yang Xiao, et al. The connotation, construction, and challenges of educational large language models. Modern Distance Education Research, 2024, Vol.36, No.5, pp.50-60. DOI: 10.3969/j.issn.1009-5195.2024.05.006
  • Reddig J.M., Arora A., MacLellan C.J. Generating In-Context, Personalized Feedback for Intelligent Tutors with Large Language Models. International Journal of Artificial Intelligence in Education, 2025, Vol.35, No.6, pp.3459-3500. DOI: 10.1007/s40593-025-00505-6
  • LaFrance J. Governing Generative Artificial Intelligence: Institutional Policies and Guidelines at America's Flagship Universities. Educational Policy, 2026. DOI: 10.1177/08959048261450995
  • Noorbehbahani F., Oyibo K. AI-overdependence and human cognitive decline: Hazards, evidence, and mitigation strategies. Computers in Human Behavior Reports, 2026, Vol.22, Art.101102. DOI: 10.1016/j.chbr.2026.101102
  • Gu Xiaoqing, et al. Mechanisms, demands, and pathways of generative AI empowering teaching. Journal of the Chinese Society of Education, 2025, No.4, pp.15-22.
  • Su C.S., Hsu C.C., Campos J.A.G., et al. ChatGPT As a Resource to Strengthen Mathematics Teaching and Learning: A Systematic Review. Journal of Computer Assisted Learning, 2026, Vol.42, No.3. DOI: 10.1002/jcal.70241
  • Liu Bangqi, Huang Qianqian, Liu Mengdie, et al. Configurational paths and practical strategies of factors influencing the effectiveness of GenAI teaching applications: An analysis of 29 typical university cases. Modern Educational Technology, 2026, Vol.36, No.4, pp.53-62. DOI: 10.3969/j.issn.1009-8097.2026.04.006
  • Tang X., Wong S., Huynh M., et al. SPHERE: Supporting Personalized Feedback at Scale in Programming Classrooms with Structured Review of Generative AI Outputs. Proceedings of the ACM, 2025. DOI: 10.1145/3706599.3720203
  • Alneyadi S., Wardat Y. Integrating ChatGPT in Grade 12 Quantum Theory Education: An Exploratory Study at Emirate School (UAE). International Journal of Information and Education Technology, 2024, Vol.14, No.3, pp.398-410. DOI: 10.18178/ijiet.2024.14.3.2061
  • Nasr N.R., Tu C.-H., Sujo-Montes L., et al. Generative artificial intelligence impact on critical thinking in higher education: a comprehensive bibliometric analysis and systematic review. Journal of Computers in Education, 2026. DOI: 10.1007/s40692-026-00396-z
  • Li B., Tan Y.L., Wang C., et al. Two years of innovation: A systematic review of empirical generative AI research in language learning and teaching. Computers and Education: Artificial Intelligence, 2025, Vol.9, Art.100445. DOI: 10.1016/j.caeai.2025.100445
  • Lin X., Tan H. A Systematic Review of Generative AI in K-12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model. Systems, 2025, Vol.13, No.10, Art.840. DOI: 10.3390/systems13100840
  • Lai C.-H., Lin C.-Y. Analysis of Learning Behaviors and Outcomes for Students with Different Knowledge Levels: A Case Study of Intelligent Tutoring System for Coding and Learning (ITS-CAL). Applied Sciences, 2025, Vol.15, No.4, Art.1922. DOI: 10.3390/app15041922
  • Li Sen, Zheng Lan. Challenges posed by generative artificial intelligence to classroom teaching and countermeasures. Curriculum, Teaching Material and Method, 2024, Vol.44, No.1, pp.39-46. DOI: 10.19877/j.cnki.kcjcjf.2024.01.003
  • Gruenhagen J.H., Sinclair P.M., Carroll J.-A., et al. The rapid rise of generative AI and its implications for academic integrity: Students' perceptions and use of chatbots for assistance with assessments. Computers and Education: Artificial Intelligence, 2024, Vol.7, Art.100273. DOI: 10.1016/j.caeai.2024.100273
  • Bidry M., Hanine M., Ouaguid A., et al. Transforming Education With Generative AI: A Comprehensive Review of Advancements, Challenges, and Future Opportunities. IEEE Access, 2025, Vol.13, pp.202938-202955. DOI: 10.1109/ACCESS.2025.3636891
  • An Y., Yu J.H., James S. Investigating the higher education institutions' guidelines and policies regarding the use of generative AI in teaching, learning, research, and administration. International Journal of Educational Technology in Higher Education, 2025, Vol.22, No.1, pp.1-23. DOI: 10.1186/s41239-025-00507-3
  • Liu Bangqi, Huang Qianqian, Zhang Jinxia. A multi-stakeholder collaborative governance framework and practical strategies for ethical risks of GenAI educational applications. Modern Distance Education Research, 2025, Vol.37, No.5, pp.48-58. DOI: 10.3969/j.issn.1009-5195.2025.05.006
  • Tan Jianchuan. Inclusive prudence: An analysis of Japan's governance logic for educational applications of generative AI. Comparative Education Review, 2026, Vol.48, No.3, pp.3-11,52. DOI: 10.20013/j.cnki.ICE.2026.03.01
  • Zhang T., Lai Y.C., Yu P.L.H. Generative artificial intelligence in K-12 education: A systematic review. Research and Practice in Technology Enhanced Learning, 2026, Vol.21. DOI: 10.58459/rptel.2026.21034
  • Ogunleye B., Zakariyyah K.I., Ajao O. A Systematic Review of Generative AI for Teaching and Learning Practice. Education Sciences, 2024, Vol.14, No.6, Art.636. DOI: 10.3390/educsci14060636
  • Rutecka P., Cicha K., Rizun M., et al. Generative AI in Curriculum Design: Empirical Insights Into Model Performance and Educational Constraints. IEEE Transactions on Learning Technologies, 2025, Vol.18, pp.757-768. DOI: 10.1109/TLT.2025.3587081