Can Generative Artificial Intelligence be a Good Teaching Assistant?—An Empirical Analysis Based on Generative AI‐Assisted Teaching.
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 criti...
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 3; pp. 1 - 21 |
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| Autores principales: | , , , , |
| Formato: | clinical trial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185452169&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185452169 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Jun2025 vid: 41 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 185452169 185452169 185452169 10.1111/jcal.70027 185452169 ppf: 1 ppct: 20 formats: tig: atl: Can Generative Artificial Intelligence be a Good Teaching Assistant?—An Empirical Analysis Based on Generative AI‐Assisted Teaching. aug: au: Tang, Qianwen Deng, Wenbo Huang, Yidan Wang, Shuaijie Zhang, Hao affil: School of Journalism & Communication, Yangzhou University, Yangzhou, China sug: subj: Artificial Intelligence, Generative Assistive Technology Teaching Methods Utilization Learning Environment Student Supervision Evaluation Teachers Outcomes of Education Funding Source Human China Female Male Adolescence Clinical Trials Empirical Research Quasi-Experimental Studies Hypothesis Information Science Integrated Curriculum Schools, Middle Students, Middle School Questionnaires Descriptive Statistics One-Way Analysis of Variance Post Hoc Analysis Comparative Studies Faculty-Student Relations Experiential Learning Student Satisfaction Adolescent: 13-18 years Female Male ab: 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. pubtype: Academic Journal doctype: clinical trial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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