Using AI-Assisted Learning to Improve Grade 10 Students’ Research Writing Skills
This study aimed to determine the effectiveness of AI-Assisted Learning in improving the research writing skills of Grade 10 students in a public secondary school in Ozamiz City during the school year 2025–2026. The study employed a classroom-based action research design using a single-group pretest–posttest approach. The respondents consisted of thirty-three (33) Grade 10 students selected through purposive sampling. Data were gathered using a researcher-designed Research Writing Assessment Tool consisting of 30 items that measured students' ability to identify technical terms, formulate technical and operational definitions, classify operational definitions, apply correct grammar, and compose research-based paragraphs. English teachers and curriculum specialists validated the instrument, and reliability testing yielded a Cronbach's alpha of 0.70 or higher. Data were analyzed using frequency counts, percentages, means, standard deviations, and paired t-tests through JAMOVI. Findings revealed that the students' research writing skills improved from satisfactory before the implementation of the AI-assisted learning strategy to very satisfactory after the intervention. Results further showed that most students achieved Outstanding and Very Satisfactory performance levels after implementing the strategy. Statistical analysis revealed a highly significant difference in students' research writing skills before and after implementing AI-assisted Learning. The study concluded that AI-assisted Learning is an effective instructional strategy in improving Grade 10 students' research writing skills, particularly in grammar, coherence, organization of ideas, and overall academic writing performance.
Authors
- Genelyn R. Baluyos (ORCID: https://orcid.org/0000-0001-8875-906X)
- LUZ MAY D. FUERZAS
- Lilibeth Y. Abamonga
- Teresita G. Cabaong
Institutions
- Misamis University (PH)
Publication Details
- Journal
- Iconic Research and Engineering Journals
- Published
- 2026-09-16
- DOI
- https://doi.org/10.64388/irev10i3-1723135
- Primary Topic
- Technology-Enhanced Education Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00