Development and Evaluation of AI-Integrated Digital Learning Modules for Food and Beverage Operations Under the Strengthened Senior High School Tech-Pro Curriculum
This study developed and evaluated artificial intelligence (AI)-integrated digital learning modules for Food and Beverage Operations under the Strengthened Senior High School Tech-Pro Curriculum. Using a developmental-descriptive research design with a one-group pretest–posttest quasi-experimental component, it involved 84 Grade 11 learners and five TLE/TVL teachers from five public secondary schools in Agusan del Sur, together with three expert validators. The modules were examined in terms of instructional features, competency alignment, validity, and acceptability. Expert evaluation questionnaires, teacher and student acceptability questionnaires, and achievement tests supplied the data, which were analyzed using means, standard deviations, mean percentage scores, independent-samples t-tests, and a paired-samples t-test. The six instructional feature dimensions were rated highly evident (means = 4.60–4.80), and the five validity dimensions were assessed as highly valid (means = 4.67–4.80). Teachers and students considered the modules highly acceptable, with no statistically significant differences in their ratings across the five dimensions (all p > .05). The reported mean percentage score rose from 69.4 on the pretest to 78.0 on the posttest. The paired comparison of achievement-test scores was statistically significant (t = −11.303, p < .001). The findings support the modules’ suitability as supplementary, competency-aligned learning resources, while the one-group design limits causal interpretation. Continued refinement, responsible AI-use guidance, and testing in additional instructional settings are recommended.
Authors
- Randy C. Mangubat (ORCID: https://orcid.org/0000-0002-9396-5308)
- Maria Luiza V. Tantoy
Institutions
- Cebu Technological University (PH)
Publication Details
- Journal
- International Journal of Education, Research, and Innovation Perspectives
- Published
- 2026-10-16
- DOI
- https://doi.org/10.5281/zenodo.23415508
- Primary Topic
- Artificial Intelligence in Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00