The DOI matches its Crossref record; the references it cites are not checked yet.
PERSPECTIVES OF ARALING PANLIPUNAN MILLENNIAL TEACHERS IN UTILIZING ARTIFICIAL INTELLIGENCE FOR ACADEMIC INSTRUCTION
Open accessAbstract
The findings revealed that AI serves as a valuable instructional companion, supporting lesson planning, assessment development, and the preparation of instructional materials. Participants perceived AI as an effective tool for enhancing technology-integrated teaching, promoting interactive learning experiences, simplifying content delivery, and improving instructional efficiency. Despite these benefits, participants identified challenges related to information accuracy, internet connectivity, limited AI literacy, and students' potential overreliance on AI-generated outputs. The findings further suggest that AI is reshaping teachers' professional roles by positioning them as facilitators of learning while reinforcing the irreplaceable importance of empathy, values formation, ethical judgment, and critical thinking in education. The study highlights the need for schools, educational leaders, and policymakers to strengthen teachers' AI competencies through professional development, establish ethical guidelines for responsible AI utilization, and improve digital infrastructure to support human-centered AI integration. Future research may employ quantitative methods to examine AI utilization patterns, conduct Exploratory Factor Analysis (EFA) to develop standardized evaluation instruments, or compare AI integration across different subject areas to identify effective instructional practices.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
The citation lists haven’t been fetched yet
Research works printed but not linked: 2
- Coester, M., & Pohlmann, J. T. (2025). (Your trustworthiness reference from the thesis.)
- Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. Recommendation
Metadata
- Authors
- FERNANDEZ, J. M. R.
- Corresponding author
- FERNANDEZ, JC MAILA R.
- Venue
- International Journal of Research Publications
- Published
- 2026-07-16
- Volume
- 201
- Issue
- 1
- Pages
- 187-190
- ISSN
- 2708-3578 (ISSN-L)
- OpenAlex
- W7168501473
- Cited by
- 0
- References
- 0
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- International Journal of Research Publications (publishedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Educational Technology and Pedagogy
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence in education, millennial teachers, teacher perceptions, AI literacy, technology-integrated teaching, teacher role, AI competencies, teacher professional development, responsible AI use, digital infrastructure, critical thinking, human-centered AI
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review