The Readiness of Integrating AI (Artificial Intelligence) in Teaching Science: Perspectives of Private Higher Educational Institution Science Teachers in Bayombong, Nueva Vizcaya

Successful integration of Artificial Intelligence (AI) in science education depends largely on teachers’ readiness to effectively utilize emerging technologies in their instructional practices. Artificial Intelligence (AI) is the term used for systems that can learn from data, do tasks, and act like humans when it comes to reasoning, problem-solving, and making decisions. In this context, teachers' readiness includes teachers' perceived competence, resource accessibility, professional development and training, attitudes toward AI integration, and perceived barriers. This study sought to assess the preparedness of science educators at a private higher education institution in Bayombong, Nueva Vizcaya, for the integration of artificial intelligence in science instruction. Specifically, it examined their demographic profile, level of readiness across key dimensions, perceived barriers and challenges, differences when grouped by profile variables, and the relationship between readiness and perceived barriers. A quantitative descriptive-correlational research design was utilized, employing an adapted and validated questionnaire based on the Technology Acceptance Model (TAM). The study was conducted at a private higher education institution in Bayombong, Nueva Vizcaya, with the participation of 32 science educators. The data were analyzed using mean, standard deviation, t-test, ANOVA, Pearson correlation, and regression analysis. The results showed that teachers were somewhat ready, had mostly positive attitudes toward AI, and thought they were competent enough. Resource availability and professional development were somewhat accessible; however, significant obstacles, including time limitations, data privacy issues, and ambiguous institutional policies, were recognized. There were no significant differences when the groups were grouped by sex, but age and years of teaching experience had a significant effect on readiness and perceived barriers. There was also a strong negative correlation between readiness and perceived barriers. The study concludes that teachers are willing to use AI, but to make it work better in science education, they need more training, resources, and policy changes.

Perceived Competence, Resource Accessibility, Professional Development, Technology Acceptance Model

1. AlKanaan, H. M. N. (2022). Awareness Regarding the Implication of Artificial Intelligence in Science Education among Pre-Service Science Teachers. International Journal of Instruction, 15(3), 895–912. https://doi.org/10.29333/iji.2022.15348a [Google Scholar] [Crossref]

2. Alshorman, S. (2024b). THE READINESS TO USE AI IN TEACHING SCIENCE: SCIENCE TEACHERS’ PERSPECTIVE. Journal of Baltic Science Education, 23(3), 432–448. https://doi.org/10.33225/jbse/24.23.432 [Google Scholar] [Crossref]

3. Aravantinos, S., Lavidas, K., Komis, V., Karalis, T., & Papadakis, S. (2026). Artificial Intelligence in K-12 Education: A Systematic Review of Teachers’ Professional development Needs for AI integration. Computers, 15(1), 49. https://doi.org/10.3390/computers15010049 [Google Scholar] [Crossref]

4. Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education Artificial Intelligence, 3, 100099. https://doi.org/10.1016/j.caeai.2022.100099 [Google Scholar] [Crossref]

5. Ayaowei, I. T., Emmanuel, B. C. E. –., Nwailu, C., & Elijah, T. (2024). Availability, accessibility and use of information resources in the library by students. International Journal of Information Dissemination, 5(2), 87–99. https://doi.org/10.70118/ijkd.0202405020.8 [Google Scholar] [Crossref]

6. Barsoum, S. S., Elnagar, M. S., & Awad, B. M. (2022). The Effectiveness of Using a Cognitive Style-based Chatbot in Developing Science Concepts and Critical Thinking Skills among Preparatory School Pupils. European Scientific Journal ESJ, 18(22), 52. https://doi.org/10.19044/esj.2022.v18n22p52 [Google Scholar] [Crossref]

7. Chiu, T. K., & Chai, C. (2020). Sustainable Curriculum Planning for Artificial Intelligence Education: A Self-Determination Theory Perspective. Sustainability, 12(14), 5568. https://doi.org/10.3390/su12145568 [Google Scholar] [Crossref]

8. Čipková, E., & Karolčík, Š. (2018). Assessing of scientific inquiry skills achieved by future biology teachers. Chemia, Dydaktyka, Ekologia, Metrologia, 23(1–2), 71–80. https://doi.org/10.1515/cdem-2018-0004 [Google Scholar] [Crossref]

9. Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00392-8 [Google Scholar] [Crossref]

10. Folasade, K. L. (2019). Availability and accessibility of information resources in an academic library: Pathway to Sustainable development. Indian Journal of Information Sources and Services, 9(3), 8–11. https://doi.org/10.51983/ijiss.2019.9.3.640 [Google Scholar] [Crossref]

11. Ibm. (2024, October 25). What is artificial Intelligence (AI)? IBM United States. https://www.ibm.com/topics/artificial-intelligence#:~:text=What%20is%20AI%3F,and%20respond%20to%20human%20language. [Google Scholar] [Crossref]

12. Joshi, K. (2024). Artificial General Intelligence (AGI): A Comprehensive review. Journal of the Epidemiology Foundation of India, 2(3), 93–96. https://doi.org/10.56450/jefi.2024.v2i03.004 [Google Scholar] [Crossref]

13. Kaplan, A., & Haenlein, M. (2018). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004 [Google Scholar] [Crossref]

14. Lindner, A., Romeike, R., Jasute, E., & Pozdniakov, S. (2019). Teachers’ perspectives on artificial intelligence. In 12th International conference on informatics in schools, “Situation, evaluation and perspectives”, ISSEP. [Google Scholar] [Crossref]

15. Lin, X., Chen, L., Chan, K. K., Peng, S., Chen, X., Xie, S., Liu, J., & Hu, Q. (2022). Teachers’ perceptions of teaching Sustainable Artificial Intelligence: A Design frame perspective. Sustainability, 14(13), 7811. https://doi.org/10.3390/su14137811 [Google Scholar] [Crossref]

16. Lucas, M., Bem-Haja, P., Zhang, Y., Llorente-Cejudo, C., & Palacios-Rodríguez, A. (2025). A comparative analysis of pre-service teachers’ readiness for AI integration. Computers and Education Artificial Intelligence, 8, 100396. https://doi.org/10.1016/j.caeai.2025.100396 [Google Scholar] [Crossref]

17. McLean, S., Read, G. J. M., Thompson, J., Baber, C., Stanton, N. A., & Salmon, P. M. (2021). The risks associated with Artificial General Intelligence: A systematic review. Journal of Experimental & Theoretical Artificial Intelligence, 35(5), 649–663. https://doi.org/10.1080/0952813x.2021.1964003 [Google Scholar] [Crossref]

18. Moore, P. V. (2018). Jerry Kaplan Artificial intelligence: What everyone needs to know. Organization Studies, 40(3), 466–470. https://doi.org/10.1177/0170840618792173 [Google Scholar] [Crossref]

19. Roshan, S., Iqbal, S. Z., & Qing, Z. (2024b). Teacher training and professional development for implementing AI-Based educational tools. Journal of Asian Development Studies, 13(2), 1972–1987. https://doi.org/10.62345/jads.2024.13.2.154 [Google Scholar] [Crossref]

20. Russell, S. J., & Norvig, P. (2016). Artificial intelligence : a modern approach. https://thuvienso.hoasen.edu.vn/handle/123456789/8967 [Google Scholar] [Crossref]

21. Sallam, M., Salim, N. A., Al-Tammemi, A. B., Barakat, M., Fayyad, D., Hallit, S., Harapan, H., Hallit, R., & Mahafzah, A. (2023). ChatGPT Output regarding Compulsory vaccination and COVID-19 Vaccine Conspiracy: A descriptive study at the outset of a paradigm shift in online search for information. Cureus. https://doi.org/10.7759/cureus.35029 [Google Scholar] [Crossref]

22. Shardey, E., Nabi, F., & Vortia, W. (2025). Teacher Readiness for AI and digital tools in K-12 Classrooms: A Review of Professional Development Trends and Gaps. International Journal for Multidisciplinary Research, 7(5). https://doi.org/10.36948/ijfmr.2025.v07i05.55119 [Google Scholar] [Crossref]

23. Somabut, A., Tuamsuk, K., Lowatcharin, G., Traiyarach, S., & Kwangmuang, P. (2025). Preparing for the AI era: Science teachers’ readiness and professional development needs for generative AI integration in secondary education. Social Sciences & Humanities Open, 12, 102259. https://doi.org/10.1016/j.ssaho.2025.102259 [Google Scholar] [Crossref]

24. Su, K. (2022). IMPLEMENTATION OF INNOVATIVE ARTIFICIAL INTELLIGENCE COGNITIONS WITH PROBLEM-BASED LEARNING GUIDED TASKS TO ENHANCE STUDENTS’ PERFORMANCE IN SCIENCE. Journal of Baltic Science Education, 21(2), 245–257. https://doi.org/10.33225/jbse/22.21.245 [Google Scholar] [Crossref]

25. Tai, M. C. (2020). The impact of artificial intelligence on human society and bioethics. Tzu Chi Medical Journal, 32(4), 339. https://doi.org/10.4103/tcmj.tcmj_71_20 [Google Scholar] [Crossref]

26. Tshukudu, E., Waite, J., Rizvi, S., & Sentance, S. (2022). Teachers’ motivations to learn about ML and AI. In Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education, 2(ITiCSE ’22), 609. https://doi.org/10.1145/3502717.3532148 [Google Scholar] [Crossref]

27. Vorotnykova, I. (2023). PROFESSIONAL DEVELOPMENT OF SCIENCE AND MATHEMATICS TEACHERS USING ARTIFICIAL INTELLIGENCE. OPEN EDUCATIONAL E-ENVIRONMENT OF MODERN UNIVERSITY, 15, 18–34. https://doi.org/10.28925/2414-0325.2023.152 [Google Scholar] [Crossref]

28. Wang, X., Li, L., Tan, S. C., Yang, L., & Lei, J. (2023). Preparing for AI-enhanced education: Conceptualizing and empirically examining teachers’ AI readiness. Computers in Human Behavior, 146, 107798. https://doi.org/10.1016/j.chb.2023.107798 [Google Scholar] [Crossref]

• Exploring the Moderating Role of Demographic Variables in the Relationship Between Scientific Curiosity and Creativity Among Secondary School Students
• Development of a CODE-Based Teaching Guide on the Central Dogma in Biochemistry: A Study in the Philippines
• Scaffolding Genetics Learning in Resource-Constrained Classrooms: Effects of Inquiry-Based Worksheets on Student Achievement
• Chronological Versus Biological Age: The Role of Diet and Healthy Lifestyle in Modulating Epigenetic Aging
• Exploring Relationship Between Reaction Time and Academic Achievement in Science Subjects of Middle Stage Students