Grammar and engagement in focus: Evaluating Gemini AI’s impact on an educational environment
The present paper aims to investigate the role of Gemini AI in improving learners' grammatical knowledge and measuring their level of engagement within the learning process using this AI tool. So, 75 Omani pre-intermediate EFL learners were equally divided into three groups, with 25 students in each group, including a control group (traditional in-class training), an experimental A group (traditional in-class and Gemini AI within the classroom), and an experimental B group (traditional in-class training and Gemini AI outside of the classroom). To collect the data, researcher-made grammar tests were designed, piloted, validated, and their reliability was established. Additionally, to measure the engagement level of students, a questionnaire was adapted from [ 1 ]. The study's findings on grammar tests showed the progress of all groups from pretest to posttest. Initially, experimental group A performed significantly better than the other two groups, and experimental group B outperformed the control group. The analysis of the delayed posttest showed consistent results in which the experimental group A significantly performed better than the other two groups. Although the other groups did not show significant enhancements, experimental group B outperformed the control group. The thorough analysis of the engagement questionnaire also revealed similar results in which the experimental group A's engagement level was significantly higher than that of the other groups, followed by better performance of experimental group B, and then the control group. The study's findings are helpful for teachers, students, and institutions.
Authors
- Ali Al Ghaithi (ORCID: https://orcid.org/0000-0002-9653-508X)
- Behnam Behforouz (ORCID: https://orcid.org/0000-0002-0078-2757)
- Miew Luan Ng
Institutions
- INTI International University (MY)
- Shinawatra University (TH)
- University Prep (US)
- Sohar University (OM)
Publication Details
- Journal
- Computers and Education Open
- Published
- 2025-10-18
- DOI
- https://doi.org/10.1016/j.caeo.2025.100302
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00