ChatGPT in STEM Classrooms: Students’ Perceptions of Interest, Academic Proficiency, and Learning Independence
Purpose of the study: To provide K-12 evidence from a low- and middle-income context, this study examines how basic education STEM students use ChatGPT and how it relates to their interest, academic proficiency, and learning independence. Methodology: Design: descriptive cross-sectional survey in one large public school. Participants: 186 Grade 11–12 STEM students. Instrument: 17-item researcher-developed questionnaire, administered online during class. Tool: ChatGPT (OpenAI). Analysis: item-level frequencies and percentages; reliability and validity checks treated as supportive for a heterogeneous instrument. Main Findings: ChatGPT use was episodic and concentrated in Research and English. Students reported greater engagement, clearer understanding, and shorter assignment time. Independence gains were modest; textbook reliance declined while tutoring reliance was largely stable. Governance practices were common, including verification and paraphrase-synthesis or inspirational use. Older students emphasized efficiency and integration; younger students reported larger conceptual gains. Novelty/Originality of this study: This study contributes classroom-proximate, item-level evidence from Philippine basic education, an underrepresented K-12 setting. It characterizes selective, front-end deployment and widespread verification, offering rubric-ready handles for responsible use. It identifies grade-linked orchestration differences and connectivity-aware implications that can guide targeted scaffolds to translate efficiency into competence and independent learning.
Authors
- Aaron A. Funa (ORCID: https://orcid.org/0000-0002-6648-8825)
- Renz Alvin E. Gabay (ORCID: https://orcid.org/0000-0002-8108-4589)
- Dave Diaz
Institutions
- Sorsogon State University (PH)
Publication Details
- Journal
- Journal of Basic Education Research
- Published
- 2025-09-28
- DOI
- https://doi.org/10.37251/jber.v6i3.2096
- Citations
- 1
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 1.69