Development and Evaluation of AI-Integrated Digital Learning Modules for Food and Beverage Operations Under the Strengthened Senior High School Tech-Pro Curriculum
DOI:
https://doi.org/10.5281/zenodo.23415509Keywords:
artificial intelligence, Food and Beverage Operations, digital learning modules, competency-based instruction, Tech-Pro curriculum, instructional validationAbstract
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.
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Airaj, M. (2024). Ethical artificial intelligence for teaching-learning in higher education. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12545-x
Alam, M., Islam, M., Babiker, M., Siddiqui, M., Amin, M., & Olah, J. (2026). AI-assisted learning tools and student learning outcomes: A cognitive load theory perspective. Computers in Human Behavior Reports, 21. https://doi.org/10.1016/j.chbr.2026.100986
Alyusfitri, R., Gistituati, N., Yerizon, Fauzan, A., & Yarman. (2024). The effectiveness and relationship of student responses toward learning outcomes using interactive multimedia-based e-modules in elementary schools. International Electronic Journal of Elementary Education, 16(5), 573–584. https://iejee.com/index.php/IEJEE/article/view/2203
Balco, G.B. and Uy, S. (2023). Development and validation of a localized module in science, technology, engineering and mathematics competencies for food and beverage services level II trainees. Education Resources Information Center. https://eric.ed.gov/?id=ED628531
Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2022.107468
Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66, 616–630. https://doi.org/10.1007/s11528-022-00715-y
Delita, F., Berutu, N., & Nofrion. (2022). Online learning: The effects of using e-modules on self-efficacy, motivation and learning outcomes. Turkish Online Journal of Distance Education, 23(4). https://files.eric.ed.gov/fulltext/EJ1365038.pdf
Delos Santos, D., Dela Cruz, C., Estacio, I., & Parrocha, A. (2026). Evaluating the strengthened senior high school curriculum: Teachers’ and students’ perspectives as a catalyst for action. International Journal of Research Publication and Reviews, 7(3), 1448-1463. https://ijrpr.com/uploads/V7ISSUE3/IJRPR60603.pdf
Fortuna, A., Prasetya, F., Samala, A., Rawas, S., Kaya, S., Raihan, M., Andriani, W., Safitri, D., Nabawi, R. (2025). Artificial intelligence in personalized learning: A global systematic review of current advancements and shaping future opportunities. Social Sciences & Humanities Open, 12. https://doi.org/10.1016/j.ssaho.2025.102114
Hidalgo, J.A., Fabro, R.B., Castro, M., Dela Cruz, K., Perez, K.M., Quiaoit, J.M., Juan, S.M. (2025). Teaching strategies and their effects on competency assessment performance of senior high school students. International Journal of Research and Innovation in Social Science. https://dx.doi.org/10.47772/IJRISS.2025.908000355
Ibrahim, A. (2016). Definition, purpose and procedure of developmental research: An analytical review. Asian Research Journal of Arts & Social Sciences, 1(6), 1-6. https://doi.org/10.9734/ARJASS/2016/30478
Kholifah, N., Nurtanto, M., Sutrisno, V., Majid, N., Subakti, H., Daryano, R., & Achmadi, A. (2025). Unlocking workforce readiness through digital employability skills in vocational education Graduates: A PLS-SEM analysis based on human capital Theory. Social Sciences & Humanities Open, 11. https://doi.org/10.1016/j.ssaho.2025.101625
Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice Hall.
Kozan, K., Hur, J., Kim, I., & Barrett, A. (2025). Instructional designers’ integration of generative artificial intelligence into their professional practice. Education Sciences, 15(9), 1133. https://doi.org/10.3390/educsci15091133
Lasala, N. (2023). Development and validation of E-SelfIMo: E-learning self-directed interactive module in earth science. Recoletos Multidisciplinary Research Journal, 11(1), 85-101. https://philarchive.org/rec/LASDAV
Metreveli, A., Chen, X., Hedman, A., & Sergeeva, A. (2025). “Who will be left behind?”: A Swedish case of learning AI in vocational education. International Journal of Educational Research. https://doi.org/10.1016/j.ijer.2025.102697
Pickles, M., Duffy, P., & Stewart, J. (2025). Addressing global labour challenges: An integrative model for sustainable hospitality workplaces, informed by resource-based view theory and the kaleidoscope career model. International Journal of Hospitality Management, 131. https://doi.org/10.1016/j.ijhm.2025.104258
Rosales, V., Zabala, V., Leuterio, J.V., Achondo, I., Dumadag, C., Rosales, G.M., Paclipan, C.M., & Capao, M. (2025). Ensuring quality in TVET: An analysis of training and assessment. The Second Congressional Commission on Education. https://edcom2.gov.ph/media/2026/01/21_ROSALES-Ensuring-Quality-in-TVET2.pdf
Ruiz-Rojas, L., Acosta-Vargas, P., De-Moreta-Llovet, J., & Gonzalez-Rodriguez, M. (2023). Empowering education with generative artificial intelligence tools: Approach with an instructional design matrix. Sustainability, 15(15), 11524. https://doi.org/10.3390/su151511524
Shakeel, S., Al Mamun, M., & Haolader, M. (2022). Instructional design with ADDIE and rapid prototyping for blended learning: Validation and its acceptance in the context of TVET Bangladesh. Education and Information Technologies, 28, 7601-7630. https://doi.org/10.1007/s10639-022-11471-0
Tang, L., & Sheng Su, Y. (2024). Ethical implications and principles of using artificial intelligence models in the classroom: A systematic literature review. International Journal of Interactive Multimedia and Artificial Intelligence, 8(5), 25-36. https://doi.org/10.9781/ijimai.2024.02.010
Tarigan, W. P. L., Sipahutar, H., & Harahap, F. (2023). The impact of an interactive digital learning module on students’ academic performance and memory retention. Computers and Children, 2(2), em004. https://doi.org/10.29333/cac/13654
Tee, P., Wong, L., Dada, M., Song, B., & Ng, C. (2024). Demand for digital skills, skill gaps and graduate employability: Evidence from employers in Malaysia. F1000 Research. https://doi.org/10.12688/f1000research.148514.1
Uzorka, A. & Odebiyi, O. (2025). Impact of digital learning tools on student engagement and achievement. Journal of Digital Learning and Distance Education, 4(1). https://doi.org/10.56778/jdlde.v4i1.511
Weng, X., Ye, H., Dai, Y., & Ng, O. (2024). Integrating artificial intelligence and computational thinking in educational contexts: A systematic review of instructional design and student learning outcomes. Journal of Educational Computing Research, 62(6). https://doi.org/10.1177/07356331241248686
Yan, Y., Liu, H., & Chau, T. (2025). A systematic review of AI ethics in education: Challenges, policy gaps, and future directions. Journal of Global Information Management, 33(1). https://doi.org/10.4018/JGIM.386381
Yoshida, Y. (2025). Work readiness of senior high school students in the technical-vocational-livelihood track. International Journal of Pedagogy and Learning Community, 2(1), 1-12. https://doi.org/10.24036/2ytjvj68
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