Artificial Intelligence Competence Self-Efficacy and Attitudes Toward AI Among T.V.E Teachers as Basis for a Professional Development Plan
DOI:
https://doi.org/10.65339/ijggsd.V1.I1.39Keywords:
AI Competence Self-Efficacy, Artificial Intelligence, Attitudes Toward AI, Descriptive-Correlational Research, Professional Development Plan, Sustainable Development Goals, Teacher Professional Development, Technology and Vocational EducationAbstract
This study examined Artificial Intelligence (AI) Competence Self-Efficacy and Attitudes Toward AI among Technology and Vocational Education (TVE) teachers at Abellana National School as a basis for a proposed Professional Development Plan. Anchored in Social Cognitive Theory, the Technology Acceptance Model, and the Technological Pedagogical Content Knowledge (TPACK) Framework, the study employed a quantitative descriptive-correlational design involving 45 TVE teachers selected through total population sampling. Data were collected using the Teacher Artificial Intelligence Competence Self-Efficacy Scale (TAICS) and the Teachers’ Attitudes Toward Artificial Intelligence in Education (TATAIE) Scale. Descriptive statistics and Pearson Product-Moment Correlation were employed for data analysis. Findings revealed high AI Competence Self-Efficacy across six dimensions, with AI Pedagogy obtaining the highest mean (4.01) and AI Assessment the lowest (3.69). Respondents demonstrated favorable Attitudes Toward AI, particularly its Usefulness for Teachers (M = 4.21), while Usefulness for Learners received the lowest rating (M = 3.62). A strong positive and statistically significant relationship was established between AI Competence Self-Efficacy and Attitudes Toward AI (r = 0.691, p < .001), indicating that greater perceived competence was associated with more favorable attitudes. The study recommended a Professional Development Plan prioritizing AI-supported assessment, learner-centered applications, ethical AI integration, and continuous professional engagement. The research aligns with Sustainable Development Goal 4 (Quality Education), SDG 9 (Industry, Innovation and Infrastructure), and SDG 8 (Decent Work and Economic Growth) through teacher development, technological innovation, and vocational education. Its potential sustainability impact involves strengthening institutional teaching capacity, promoting responsible AI adoption, and supporting continuous improvement in skills-oriented education.
References
Ahmadi Fatalaki, J., Saeedi, Z., Sheikhzadeh Marand, E., & Nasiri Firuz, A. R. (2025). Artificial intelligence in education and EFL teachers’ attitudes: Scale development and validation. Innovation in Language Learning and Teaching. Advance online publication. https://doi.org/10.1080/17501229.2025.2494823 DOI: https://doi.org/10.2139/ssrn.4687460
Amir-Rudin, A. H., Abdul Hamid, A. H., Rosmail, I. I., Abu Seman, S. A., & Abd Rashid, N. (2025). Validating the acceptance of artificial intelligence (AI) in higher education institutions using the Technology Acceptance Model (TAM). Environment-Behaviour Proceedings Journal, 10(SI26), 137–144. https://doi.org/10.21834/e-bpj.v10iSI26.6810 DOI: https://doi.org/10.21834/e-bpj.v10iSI26.6810
Avidov-Ungar, O., Hadad, S., Shamir-Inbal, T., & Blau, I. (2023). Formal and informal professional development during different COVID-19 periods: The role of teachers’ career stages. Professional Development in Education, 51(4), 666–682. https://doi.org/10.1080/19415257.2023.2174163 DOI: https://doi.org/10.1080/19415257.2023.2174163
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
Bower, M., Torrington, J., Lai, J. W. M., Petocz, P., & Alfano, M. (2024). How should we change teaching and assessment in response to increasingly powerful generative artificial intelligence? Outcomes of the ChatGPT teacher survey. Education and Information Technologies, 29(12), 15403–15439. https://doi.org/10.1007/s10639-023-12405-0 DOI: https://doi.org/10.1007/s10639-023-12405-0
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, 138, Article 107468. https://doi.org/10.1016/j.chb.2022.107468 DOI: https://doi.org/10.1016/j.chb.2022.107468
Chiu, T. K. F., Ahmad, Z., & Çoban, M. (2025). Development and validation of teacher artificial intelligence (AI) competence self-efficacy (TAICS) scale. Education and Information Technologies, 30(5), 6667–6685. https://doi.org/10.1007/s10639-024-13094-z DOI: https://doi.org/10.1007/s10639-024-13094-z
Chiu, T. K. F., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6, Article 100171. https://doi.org/10.1016/j.caeo.2024.100171 DOI: https://doi.org/10.1016/j.caeo.2024.100171
Consoli, T., Désiron, J., & Cattaneo, A. (2023). What is “technology integration” and how is it measured in K–12 education? A systematic review of survey instruments from 2010 to 2021. Computers & Education, 197, Article 104742. https://doi.org/10.1016/j.compedu.2023.104742 DOI: https://doi.org/10.1016/j.compedu.2023.104742
Darko, E. N. K. O., Qi, Z., & Wang, Z. (2026). Teachers’ competence, self-efficacy, and their attitudes toward generative AI in education: A correlational study. Frontiers in Psychology, 17, Article 1756148. https://doi.org/10.3389/fpsyg.2026.1756148 DOI: https://doi.org/10.3389/fpsyg.2026.1756148
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 DOI: https://doi.org/10.2307/249008
Demapendan, J. P. C. (2024). Exploring the education mismatch: A phenomenological study of Technology and Livelihood Education (TLE) teachers assigned outside specialization. Indonesian Journal of Education Research, 5(6), 243–253. https://doi.org/10.37251/ijoer.v5i6.1189 DOI: https://doi.org/10.37251/ijoer.v5i6.1189
Department of Education. (2017). National adoption and implementation of the Philippine Professional Standards for Teachers (DepEd Order No. 42, s. 2017). https://www.deped.gov.ph/2017/08/11/do-42-s-2017-national-adoption-and-implementation-of-the-philippine-professional-standards-for-teachers-2/
Department of Education. (2022). Adoption of the Basic Education Development Plan 2030 (DepEd Order No. 024, s. 2022). https://www.deped.gov.ph/wp-content/uploads/2022/05/DO_s2022_024.pdf
Ding, A.-C. E., Shi, L., Yang, H., & Choi, I. (2024). Enhancing teacher AI literacy and integration through different types of cases in teacher professional development. Computers and Education Open, 6, Article 100178. https://doi.org/10.1016/j.caeo.2024.100178 DOI: https://doi.org/10.1016/j.caeo.2024.100178
Erol, M., Canbeldek Erol, M., Erol, A., & Gök Çolak, F. (2025). Exploring the relationship between teachers’ AI attitudes, AI self-efficacy, and AI technological pedagogical content knowledge. European Journal of Education, 60(4), Article e70332. https://doi.org/10.1111/ejed.70332 DOI: https://doi.org/10.1111/ejed.70332
Galindo-Domínguez, H., Delgado, N., Campo, L., & Losada, D. (2024). Relationship between teachers’ digital competence and attitudes towards artificial intelligence in education. International Journal of Educational Research, 126, Article 102381. https://doi.org/10.1016/j.ijer.2024.102381 DOI: https://doi.org/10.1016/j.ijer.2024.102381
Hao, J., von Davier, A. A., Yaneva, V., Lottridge, S., von Davier, M., & Harris, D. J. (2024). Transforming assessment: The impacts and implications of large language models and generative AI. Educational Measurement: Issues and Practice, 43(2), 16–29. https://doi.org/10.1111/emip.12602 DOI: https://doi.org/10.1111/emip.12602
Kong, S. C., Yang, Y., & Hou, C. (2024). Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model. Computers and Education: Artificial Intelligence, 7, Article 100328. https://doi.org/10.1016/j.caeai.2024.100328 DOI: https://doi.org/10.1016/j.caeai.2024.100328
Lucas, M., Zhang, Y., Bem-haja, P., & Vicente, P. N. (2024). The interplay between teachers’ trust in artificial intelligence and digital competence. Education and Information Technologies, 29, 22991–23010. https://doi.org/10.1007/s10639-024-12772-2 DOI: https://doi.org/10.1007/s10639-024-12772-2
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research
Miksza, P., Shaw, J. T., Richerme, L. K., Hash, P. M., Hodges, D. A., & Parker, E. C. (2023). Quantitative descriptive and correlational research. In Music education research: An introduction (pp. 241–262). Oxford University Press. https://doi.org/10.1093/oso/9780197639757.003.0012 DOI: https://doi.org/10.1093/oso/9780197639757.003.0012
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x DOI: https://doi.org/10.1177/016146810610800610
Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71, 137–161. https://doi.org/10.1007/s11423-023-10203-6 DOI: https://doi.org/10.1007/s11423-023-10203-6
Ng, D. T. K., Tan, C. W., & Leung, J. K. L. (2024). Empowering student self-regulated learning and science education through ChatGPT: A pioneering pilot study. British Journal of Educational Technology, 55(4), 1328–1353. https://doi.org/10.1111/bjet.13454 DOI: https://doi.org/10.1111/bjet.13454
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w DOI: https://doi.org/10.1007/s10639-022-11316-w
Reinilä, E., Saajanaho, M., Fadjukoff, P., Törmäkangas, T., & Kokko, K. (2023). The development of generativity in middle adulthood and the beginning of late adulthood: A longitudinal study from age 42 to 61. Journal of Adult Development, 30, 291–304. https://doi.org/10.1007/s10804-022-09436-1 DOI: https://doi.org/10.1007/s10804-022-09436-1
Republic Act No. 10533. (2013). Enhanced Basic Education Act of 2013. https://lawphil.net/statutes/repacts/ra2013/ra_10533_2013.html
Uygun, D. (2024). Teachers’ perspectives on artificial intelligence in education. Advances in Mobile Learning Educational Research, 4(1), 931–939. https://doi.org/10.25082/AMLER.2024.01.005 DOI: https://doi.org/10.25082/AMLER.2024.01.005
Velli, K., & Zafiropoulos, K. (2024). Factors that affect the acceptance of educational AI tools by Greek teachers—A structural equation modelling study. European Journal of Investigation in Health, Psychology and Education, 14(9), 2560–2579. https://doi.org/10.3390/ejihpe14090169 DOI: https://doi.org/10.3390/ejihpe14090169
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 DOI: https://doi.org/10.2307/30036540
Yang, Y. F., Tseng, C. C., & Lai, S. C. (2024). Enhancing teachers’ self-efficacy beliefs in AI-based technology integration into English speaking teaching through a professional development program. Teaching and Teacher Education, 144, Article 104582. https://doi.org/10.1016/j.tate.2024.104582 DOI: https://doi.org/10.1016/j.tate.2024.104582
Yim, I. H. Y., & Wegerif, R. (2024). Teachers’ perceptions, attitudes, and acceptance of artificial intelligence (AI) educational learning tools: An exploratory study on AI literacy for young students. Future in Educational Research, 2(4), 318–345. https://doi.org/10.1002/fer3.65 DOI: https://doi.org/10.1002/fer3.65
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