Can AI aid teachers to motivate inventiveness of learners in ICT, Math, Science, and TechVoc Areas
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Abstract
Anchored in Constructivist Learning Theory and reinforced by Amabile’s Componential Theory of Creativity, this study examines the manner in which artificial intelligence (AI) facilitates educators in fostering inventive capacities among learners within Information and Communication Technology (ICT), Mathematics, and Science. While extant scholarly discourse underscores AI’s capability to augment personalized instruction, instantaneous feedback, and adaptive learning experiences, only a limited body of literature has scrutinized the extent to which AI shapes learners’ creativity and innovative competencies in STEM-oriented instructional environments, particularly when interpreted through the perspectives of both educators and learners. This scholarly lacuna becomes increasingly evident within basic education settings, where AI implementation remains in its formative stages and exhibits inconsistent levels of comprehension and application. Employing a qualitative descriptive research design, the inquiry elicits perspectives from purposively selected ICT, Mathematics, and Science teachers (n = 10–12) who have incorporated AI-enabled instructional resources, including simulations, adaptive learning platforms, and code assistance applications, together with students (n = 6–8 per discipline) who have participated in AI-supported pedagogical experiences. Data were elicited through semi-structured interviews structured in accordance with the research objectives, emphasizing the identification of teachers’ AI integration practices, learners’ viewpoints, and the motivational as well as inventive outcomes associated with AI-enhanced instructional contexts. The study is anticipated to demonstrate that educators employ a diverse repertoire of AI platforms to cultivate creativity, exploratory learning, and analytical problem resolution, while simultaneously encountering impediments associated with technological accessibility, professional competency development, and congruence with curricular learning outcomes. Learners are likewise expected to regard AI-powered instructional tools as immersive and motivational, particularly because of functionalities such as real-time feedback, individualized learning activities, and interactive simulations that invigorate inventive thinking. Nevertheless, potential constraints, including cognitive burden, excessive reliance on AI, and inadequate instructional facilitation, may likewise emerge. Collectively, the anticipated findings are expected to furnish a more nuanced comprehension of how AI may be pedagogically harnessed to nurture creativity and innovation across ICT, Mathematics, and Science classrooms while simultaneously informing educational policy formulation, teacher professional development initiatives, and the refinement of AI-supported instructional approaches aligned with constructivist and creativity-oriented pedagogical principles
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References
Adalia, H. G., Chavez, J. V., Hayudini, M. A. A., et al. (2025). Relevance of grammar among Gen Z college students using social learning perspectives. Forum for Linguistic Studies, 7(3), 432–450. https://doi.org/10.30564/fls.v7i3.8401
Al Darayseh, M. (2024). Exploring the Impact of Artificial Intelligence in Teaching and Learning of Science: A Systematic Review of Empirical Research. Research in Science Education.
Amabile, T. M. (2012). Componential Theory of Creativity (No. 12-096). Harvard Business School.
Amabile, T. M., & Pratt, M. (2016). The Dynamic Componential Model of Creativity and Innovation in Organizations. Research in Organizational Behavior, 36, 157–183.
Barrientos, A. A. (2025). Encouraging environmental sensitivity and earth science performance: Perceptions from first-year teacher education students. Journal of Environmental & Earth Sciences, 7(4), 180–192. https://doi.org/10.30564/jees.v7i4.8039
Bondoc, R. S., Jr. (2024). ICT-driven instructional and assessment strategies for physical education in the new normal. Environment and Social Psychology, 9(4), 2155. https://doi.org/10.54517/esp.v9i4.2155
Calzada, K. P. D. (2024). Anti-dependency teaching strategy for innovation in the age of AI among technology-based students. Environment and Social Psychology, 9(8), 3026. https://doi.org/10.59429/esp.v9i8.3026
Calzada, K. P. D., & Espartero, M. M., & Prado, R. T. D. (2024). Analyzing the level of interest of high school students in solving mathematical problems in modular and face-to-face learning. Environment and Social Psychology, 9(4), 2167. https://doi.org/10.54517/esp.v9i4.2167
Carpio, L. B., Caburnay, A. L. S., Nolledo, S. M., et al. (2024). Technology-based teaching among nursing instructors: Confidence and apprehension in using simulation equipment for training. Environment and Social Psychology, 9(8), 2591. https://doi.org/10.59429/esp.v9i8.2591
Castro, F. L. T., Ventura, B. L. O., Estajal, R. S., et al. (2024). Teachers handling multiple subject areas: Difficulties and adaptive attributes in the delivery of instructions. Environment and Social Psychology, 9(9), 2520. https://doi.org/10.59429/esp.v9i9.2520
Ceneciro, C. C. (2025). Characterizing workshops promoting motivated engagement and retention beyond the sessions: Experiential narratives from education, language, and social science instructors. Forum for Linguistic Studies, 7(4), 51–65. https://doi.org/10.30564/fls.v7i4.8373
Chan, T., Lee, J., & Lee, M. (2021). Student perceptions of AI-assisted learning in STEM classrooms. Journal of Educational Technology Research, 29(4), 455–472.
Chavez, J. V. (2023). Assessing online academic integrity and humanized teaching in Zamboanga Peninsula Polytechnic State University. Journal of Multidisciplinary in Social Sciences, 19(1), 9–17.
Chavez, J. V., & Prado, R. T. D. (2023). Discourse analysis on online gender-based humor: Markers of normalization, tolerance, and lens of inequality. Forum for Linguistic Studies, 5(1), 55–71. https://doi.org/10.18063/fls.v5i1.1530
Chavez, J. V., Lamorinas, D. D., & Ceneciro, C. C. (2023). Message patterns of online gender-based humor, discriminatory practices, biases, stereotyping, and disempowering tools through discourse analysis. Forum for Linguistic Studies, 5(2), 1535. http://doi.org/10.59400.fls.v5i2.1535
Chavez, J. V., Cuilan, J. T., Mannan, S. S., et al. (2024). Discourse analysis on the ethical dilemmas on the use of AI in academic settings from ICT, Science, and Language instructors. Forum for Linguistic Studies, 6(5), 349–363. https://doi.org/10.30564/fls.v6i5.6765
Chen, C., & Tsai, C. (2021). Artificial intelligence in science education: A review of applications and impacts. Science Education Review, 52(3), 367–389.
Chiu, M., et al. (2023). Artificial Intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review. Computers & Education: Artificial Intelligence.
Crompton, H., & Burke, D. (2021). Artificial intelligence in education: Ethical considerations and teacher perspectives. Computers & Education, 168, 104212.
Cuilan, J. T., Chavez, J. V., Soliva, K. J. G., et al. (2024). Verbal and non-verbal communication patterns of persuasive selling among live online sellers. Environment and Social Psychology, 9(8), 2519. https://doi.org/10.59429/esp.v9i8.2519
Dela Calzada, K. P., Tacbobo, C. M. P., Lualhati, M. E. C., et al. (2025). Alternating environmental teaching through AI: Potential benefits and limitations. Journal of Environmental & Earth Sciences, 7(4), 138–151. https://doi.org/10.30564/jees.v7i4.8340
Dede, C., Grotzer, T., & Kamar, E. (2020). AI-enhanced learning environments for scientific inquiry. Journal of Science Education and Technology, 29(5), 589–602.
Espartero, M. M., Calzada, K. P. D., & Prado, R. T. D. (2024). Analyzing the level of interest of high school students in solving mathematical problems in the modular and face-to-face learning. Environment and Social Psychology, 9(4), 2167. https://doi.org/10.54517/esp.v9i4.2167
Garcia, C. S., Lastam, J. M. P., Chavez, J. V., et al. (2025). Discourse analysis on learners halting their education due to early marriage. Environment and Social Psychology, 10(1), 2558. https://doi.org/10.59429/esp.v10i1.2558
Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Inoferio, H. V., Espartero, M. M., Asiri, M. S., et al. (2024). Coping with math anxiety and lack of confidence through AI-assisted learning. Environment and Social Psychology, 9(5), 2228. https://doi.org/10.54517/esp.v9i5.2228
Kaur, P., & Natarajan, B. (2021). Enhancing creative problem-solving in math through AI-supported learning. International Journal of STEM Education, 8(12), 1–14.
Kim, S., Li, Y., & Xu, H. (2023). Using AI code assistants to support novice programmers: Opportunities and challenges. Journal of Computing Education, 31(2), 211–230.
Li, Y., & Xu, H. (2023). Intelligent programming tools and student creativity: A systematic review. Computers in Human Behavior, 141, 107620.
Mundo, M. A. D., Reyes, E. F. D., & Gervacio, E. M. (2024). Discourse analysis on experience-based position of science, mathematics, and Tech-Voc educators on generative AI and academic integrity. Environment and Social Psychology, 9(8), 3028. https://doi.org/10.59429/esp.v9i8.3028
Nirmala, M., & Ravichandran, Y. D. (2025). Artificial Intelligence in School-Level Mathematics Education: A Comprehensive Review. International Journal of Research and Scientific Innovation.
Pane, J. F., Steiner, E. D., & Baird, M. D. (2021). Adaptive learning technologies in mathematics instruction. Educational Evaluation and Policy Analysis, 43(3), 407–432.
Ra, E., Kim, S. J., Seo, E.-Y., & So, G. (2025). Designing LMS and Instructional Strategies for Integrating Generative-Conversational AI. arXiv.
Ruslim, M. I., & Khalid, F. (2024). The Use of Artificial Intelligence in Differentiated Instruction Classrooms. International Journal of Academic Research in Business and Social Sciences, 14(8), 680–695.
Tarun, B., Du, H., Kannan, D., & Gehringer, E. F. (2025). Human-in-the-Loop Systems for Adaptive Learning Using Generative AI. arXiv.
Verdeflor, R. N. (2024). A phenomenological study of mathematics in the world: Discriminate learning experience, impressions, and actual encounters of students. Environment and Social Psychology, 9(9), 2814. https://doi.org/10.59429/esp.v9i9.2814
Verdeflor, R. N. (2024). Choosing science and mathematics programs in college: Practical and psychological arbiters in career-pathing. Environment and Social Psychology, 9(9), 2777. https://doi.org/10.59429/esp.v9i9.2777.
.