Can Generative Artificial Intelligence be a Good Teaching Assistant?—An Empirical Analysis Based on Generative AI ‐Assisted Teaching
Abstract
Background: Generative Artificial Intelligence (AI) shows promise in enhancing personalised learning and improving educational efficiency. However, its integration into education raises concerns about misinformation and over‐reliance, particularly among adolescents. Teacher supervision plays a critical role in mitigating these risks and ensuring the effective use of Generative AI in classrooms. Despite the growing interest in Generative AI, there is limited empirical research on its actual impact and the role of teacher oversight. Objective: The purpose of this study is to systematically assess the role of Generative AI in classroom teaching, with a specific focus on how teacher supervision shapes its effectiveness. Method: This study employed a quasi‐experimental design to examine differences in learning outcomes among students under three instructional methods: traditional computer‐assisted teaching, Generative AI‐assisted teaching without teacher supervision and Generative AI‐assisted teaching with teacher supervision. The study was implemented in the context of a two‐week Information Science and Technology course in a middle school, involving three classes with 45, 41 and 45 students, respectively. To ensure consistency in teaching styles, all classes were taught by the same experienced teacher. Data collection included a knowledge test to assess knowledge mastery, as well as questionnaires to measure learning satisfaction and engagement. The collected data were analysed using one‐way ANOVA to compare the effectiveness of the three teaching methods. Results and Conclusion: Compared with traditional computer‐assisted teaching, Generative AI‐assisted teaching can significantly enhance students' learning satisfaction, but can not improve their learning engagement and knowledge mastery level. Furthermore, in the process of Generative AI‐assisted teaching, teacher supervision can significantly increase students' learning engagement and knowledge mastery compared with situations without teacher supervision. This study indicated Generative AI's potential as an educational tool and underscored the essential role of teacher supervision. Implications: This study fills a critical gap by providing empirical evidence on how Generative AI and teacher supervision interact to improve classroom learning outcomes. It shows that Generative AI's potential to enhance learning outcomes is significantly amplified with teacher oversight.
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The feedback literacy and self-regulated learning objective aims to strengthen students' capacity to understand, act on, and self-regulate in response to AI-generated feedback.
“This objective aims to (1) strengthen students' capacity to understand, act on, and selfregulate in response to AI-generated feedback [124-127];”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [125]
AI adjusts the specificity, depth, and difficulty of feedback based on students' abilities and prior performance, enabling tailored prompts and tiered guidance.
“AI adjusts the specificity, depth, and difficulty of feedback based on students' abilities and prior performance, enabling tailored prompts and tiered guidance.”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [125]
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Lin, X., & Tan, H. (2025) · Systems · doi:10.3390/systems13100840 · Cited by 22 · see citing works
Navigating generative AI in higher education – six near future scenarios
Lindell, T. L., & Stöhr, C. (2025) · Learning Media and Technology · doi:10.1080/17439884.2025.2562405 · Cited by 19 · see citing works
“For example, some studies warn that excessive reliance on GenAI may hinder students’ critical thinking skills (Orduño-Osuna et al. 2023; Tang et al. 2025), while others argue that, when used appropriately, it can support deeper analytical thinking (Daniel, Msambwa, and Wen 2025).”
Assessing EFL students’ GenAI-assisted writing: Teachers’ pains, perceptions and practices
Chen, X., & Pi, X. (2025) · Assessing Writing · doi:10.1016/j.asw.2025.100996 · Cited by 12 · see citing works
Can Learners’ Use of GenAI Enhance Learning Engagement?—A Meta-Analysis
Wang, K., & Guo, Z. X. X. Z. P. (2025) · Education Sciences · doi:10.3390/educsci15121578 · Cited by 12 · see citing works
“Some studies report substantial positive effects (Chiu & Hwang, 2025), particularly on cognitive development and affective attitudes, attributing these gains to GenAI’s capacity to scaffold complex tasks and enhance motivation (J. Chen et al., 2025; Tang et al., 2025).”
Mindfulness and AI adoption: extending the technology acceptance model for Chinese media students
Lan, Y.-L., Liu, S.-H., Chen, H., & Xia, L. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1637502 · Cited by 9 · see citing works
Li, C., & Long, J. (2025) · Information · doi:10.3390/info16100895 · Cited by 12 · see citing works
“From early Intelligent Tutoring Systems [16] to learning analytics platforms grounded in cognitive science, AI has evolved from a teaching assistant to an active participant in learning processes [17].”
Boolzen, C., Kühn, J., Flegr, S., Rott, E.-M., Stausberg, N., & Küchemann, S. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11665-9 · Cited by 9 · see citing works
Zhang, T., Zhao, C., Lv, Q., Wang, X., & Zhang, Z. D. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-13999-x · Cited by 4 · see citing works
“…have explored the determinants of AI’s effectiveness in learning from the perspective of teachers and students, such as students’ learning patterns, AI literacy, educators’ technologi - cal and pedagogical skills, and teacher supervision (Bauer et al. 2025; Tang et al. 2025; Wang et al. 2025b).”
How humorous is AI? Exploring ChatGPT's role in humor generation and human-AI interaction
Cao, Y., Cao, J., Hou, Y., & Ji, L. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100807 · Cited by 4 · see citing works
Hao, X., Li, J., Huang, H., & Hao, B. (2026) · Sustainability · doi:10.3390/su18020727 · Cited by 3 · see citing works
Unpacking the Effects of GenAI on Cultivating Students’ Computational Thinking: A Meta-Analysis
Xu, J. F., Chen, Z., Chen, M., Li, Y., & Xu, X. (2026) · Journal of Educational Computing Research · doi:10.1177/07356331261419586 · Cited by 7 · see citing works
“…can be set to reduce learning anxiety through encouraging language; for higher grades, a “ tutor ” or “ teaching assistant ” role can be set to stimulate the desire for challenges, guiding students to participate deeply in tasks (Tang et al., 2025), thereby indirectly improving the effect of CT.”
Lin, Y., Zhang, Y., Yang, Y., Pan, S., Ren, X., & Chen, D. (2025) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2025.102070 · Cited by 2 · see citing works
Tang, Q., & Zhang, H. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2641762 · Cited by 2 · see citing works
“…current GAI systems often provide limited support for deeper conceptual understanding in programming, reinforcing the continued importance of teacher guidance in explaining underlying principles and supporting reflective thinking (Farrelly & Baker, 2023; Tang et al., 2025; Wang et al., 2025).”
Self-citationGao, X., Guo, X., Jiang, Q., Xiong, W., zhao, W., & Zhang, D. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-14037-6 · Cited by 2 · see citing works
“Compared with more general-purpose large language models (LLMs), SparkDesk is a Chinese-centered model optimized for Chinese-language understanding and generation (Tang et al., 2025 ).”
De corrector a mentor: el docente ante la inteligencia artificial generativa
Pérez, A. A. J. (2025) · Revista Multidisciplinaria Voces de América y el Caribe · doi:10.69821/remuvac.v2i2.230 · Cited by 1 · see citing works
Xiao, W., & Lu, X. (2026) · Journal of Educational Computing Research · doi:10.1177/07356331261424733 · Cited by 1 · see citing works
“This personalization is shown to boost motivation, engagement, knowledge retention (Wang & and Huang, 2025) and satisfaction (Tang et al., 2025).”
Confucian Self-Cultivation and the Ethics of Learning with Large Language Models
Lam, C. C. (2026) · Studies in Philosophy and Education · doi:10.1007/s11217-026-10045-8 · Cited by 1 · see citing works
Jiang, G. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1815271 · Cited by 2 · see citing works
Zhang, D., Fu, G., Cao, K., Wang, X. Q., & Li, H. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1825053 · Cited by 1 · see citing works
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Dabbagh, N. H., & Fake, H. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100626 · Cited by 2 · see citing works
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“This type of approach is seen in AI-driven learning and human interpretation, which promote critical thinking rather than passive acceptance [17].”
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Wen, J. (2026) · Journal of Cases on Information Technology · doi:10.4018/jcit.400705
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Guansing, J., Leoveras, M. E. D. C., & Natividad, L. R. (2026) · Multidisciplinary Reviews · doi:10.31893/multirev.2026525
“The participants noted that AI enhances understanding, serves as an alternative source of instruction, and complements teacher guidance (Pratama et al., 2023; Tang et al., 2025).”
Features for designing AI-supported learning environments for older people
Kırmacı, Ö., & Flórez‐Revuelta, F. (2026) · Studies in the Education of Adults · doi:10.1080/02660830.2026.2669407
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“Teachers should lead students to appropriately use GenAI, thereby avoiding potential risks and making full use of its advantages (Tang et al., 2025).”
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“Only three studies (Tang et al., 2025; Gökkurt Yilmaz et al., 2025; Chen, 2025) used experimental or quasi-experimental designs.”
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A scoping review of artificial intelligence literacy in secondary education
Ghosh, R., Gardia, A., & Singha, J. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01408-8 · Cited by 1 · see citing works
“Several studies emphasised that generative AI platforms can enhance the quality of students’ questioning and decision-making processes when educators guide learners to engage with these tools intentionally and reflectively [63].”
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Self-citationFrom black box to pedagogical partner: Students’ sense-making of LLM-based automated assessment
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Teachers’ adoption of GenAI: a model integrating ethics and creativity
Onbaşılı, Ü. İ., Batı, K., Yetişir, M. İ., & Doğan, C. D. (2026) · Educational Studies · doi:10.1080/03055698.2026.2710334
Designing and Developing Rights-Based Architectures for GenAI in Special and Inclusive Classrooms
Alam, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9169-4.ch004
Research on the Competence of Rural Teachers in Human-Machine Collaborative Teaching
Jiang, Y., Li, W., & Yu, J. (2026) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste69562.2026.11649595
Cognitive, Metacognitive, and Self-Regulated Practices for Pre-Service Teachers in the AI Era
Fenty, N. S., & Park, J. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-3840-6.ch008
Teacher Readiness‐By‐Design for AI ‐Rich Interactive Learning
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Zhao, Q., & Li, S. (2026) · Behavioral Sciences · doi:10.3390/bs16091626
“This finding is consistent with prior studies reporting generally positive student experiences with GenAI-supported learning (e.g., El Fathi et al., 2025; Sun et al., 2024; Tang et al., 2025).”
Raju, G. M., Balart, T., & Shryock, K. J. (2026) · Education Sciences · doi:10.3390/educsci16101635
“…student overreliance, and calls for clearer institutional guidance, much of this work remains cross-sectional or focused on general attitudes toward adoption (Almisad & Aleidan, 2025; Buele & Llerena-Aguirre, 2025; de Silva et al., 2026; Han et al., 2025; Henderson et al., 2026; Tang et al., 2025).”
Integrating generative AI with multimodal interaction to inquire into scientific phenomena
Park, J., Soh, G. X. J., Lim, S. Y., & Lee, P. P. F. (2026) · Learning Research and Practice · doi:10.1080/23735082.2026.2742916
He, X., & Qiu, X. (2026) · Education for Chemical Engineers · doi:10.1016/j.ece.2026.100542
Ledezma, C., Nathalia, V., & Díez-Palomar, J. (2026) · Research Square · doi:10.21203/rs.3.rs-11391444/v1
Fan, X., Cheng, A., & Wang, H. (2026) · International Conference on Intelligent Education and Computer Technology (IECT) · doi:10.1145/3845354.3845389
- Authors
- Tang, Q., Deng, W., Huang, Y., Wang, S., & Zhang, H.
- Affiliations
- Yangzhou University (CN)
- East China Normal University (CN)
- Corresponding author
- Zhang, Hao
- Venue
- Journal of Computer Assisted Learning
- Publisher
- Wiley
- Published
- 2025-05-05
- Volume
- 41
- Issue
- 3
- ISSN
- 0266-4909 (ISSN-L)
- 1365-2729
- OpenAlex
- W4410134888
- Cited by
- 55
- References
- 115
- Citations by year
- 2026: 42 · 2025: 13
- FWCI
- 57.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Computer Assisted Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- generative AI, teacher supervision, AI in teaching, computer-assisted instruction, learning outcomes, knowledge mastery, student engagement, learning satisfaction, junior high school students, misinformation risk, overreliance on AI
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