Lived Experiences of Science Educators on AI Use: A Descriptive Phenomenological Study
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Abstract
This study explores the lived experiences of science educators using artificial intelligence (AI), focusing on how these experiences shape their thinking processes. As AI becomes increasingly integrated into education, the study seeks to understand educators’ subjective, first-person experiences beyond measurable instructional outcomes. It specifically examines how educators describe their use of AI and how these experiences relate to cognitive processes in teaching. A descriptive phenomenological design guided by Giorgi’s (2009) method was employed to capture rich experiential accounts. Seven science educators from Baybay City, Leyte, were selected through purposive sampling based on active AI use. Data were gathered through semi-structured interviews conducted both synchronously and asynchronously. Analysis followed Giorgi’s systematic steps: bracketing, meaning unit identification, transformation, and synthesis to derive the essential structure of the phenomenon. Findings reveal AI as a multifaceted support system that enhances pedagogical practice, cognition, and professional growth. Five key constituents emerged: (1) AI as a pedagogical and learning support tool, (2) cognitive transformation in AI use, (3) emotional and experiential engagement, (4) critical thinking and ethical awareness, and (5) reconstruction of teacher identity. Educators reported improved efficiency in lesson preparation, enhanced organization, flexibility, and deeper analytical engagement. Emotional responses included increased confidence and support, alongside caution due to concerns about accuracy and overreliance. Participants also demonstrated active evaluation and adaptation of AI-generated outputs, reinforcing human decision-making in instruction. A notable shift in identity emerged, from content provider to facilitator and lifelong learner. AI functions not merely as a tool but as a transformative presence in science education. It reshapes instructional practices, cognitive engagement, and professional identity. While it enhances efficiency and supports higher-order thinking, its effective integration depends on educators’ critical, ethical, and reflective use. The study highlights the need to balance AI integration with the preservation of human cognition and professional responsibility in education.
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References
Alam, M. N., Islam, M. A., Babiker, M. O. A., Siddiqui, M. S., Amin, M. B., & Oláh, J. (2026). AI-assisted learning tools and student learning outcomes: A cognitive load theory perspective. Computers in Human Behavior Reports, 21, Article 100986. https://doi.org/10.1016/j.chbr.2026.100986
Alshammari, S. H., & Alrehaili, T. A. (2025). The effect of teaching, social, and cognitive presence on student engagement in online courses: a structural equation modelling approach. Acta Psychologica, 258, 105183. https://doi.org/10.1016/j.actpsy.2025.105183
Batsaikhan, B. (Zack), & Correia, A.-P. (2024). The effects of Generative Artificial Intelligence on Intelligent Tutoring Systems in higher education: A systematic review. Studies in Technology Enhanced Learning, 4(1). https://doi.org/10.21428/8c225f6e.33570bb1
Cecilio-Fernandes, D., & Sandars, J. (2025). The hallucination of learning with generative artificial intelligence. Medical Teacher, 47(12), 2057–2058. https://doi.org/10.1080/0142159x.2025.2531812
Du, Y., & Zhang, F. (2022). Examinees’ affective preference for online speaking assessment: Synchronous VS asynchronous. Chinese Language Teaching Methodology and Technology, 5(1), Article 3. https://engagedscholarship.csuohio.edu/cltmt/vol5/iss1/3
Egunjobi, J. P. (2025). The misuse of AI-Generated content in academic and religious settings. International Journal of Research and Scientific Innovation, XII(XV), 871–879. https://doi.org/10.51244/ijrsi.2025.121500077p
Fitria, T. N. (2023). The use of artificial intelligence in education (AIED): Can AI replace the teacher’s role? Epigram, 20(2), 165–187. https://doi.org/10.32722/epi.v20i2.5711
Gerlich, M. (2025). AI Tools in Society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006
Giorgi, A. (2009). The descriptive phenomenological method in psychology: A modified Husserlian approach. Pittsburgh, PA: Duquesne University Press.
Graus, M., Van De Broek, A., Hennissen, P., & Schils, T. (2022). Disentangling aspects of teacher identity learning from reflective blogs: The development of a category system. Teaching and Teacher Education, 111, 103624. https://doi.org/10.1016/j.tate.2021.103624
Guilherme, A. (2017). AI and education: the importance of teacher and student relations. AI & Society, 34(1), 47–54. https://doi.org/10.1007/s00146-017-0693-8
Harrasi, N. A., Subhi, N. A., Harthy, S. A., & Habsi, J. A. (2025). Learner perspectives on AI teacher effectiveness: The role of engagement, motivation, efficiency, and educational experience. Social Sciences & Humanities Open, 12, 101950. https://doi.org/10.1016/j.ssaho.2025.101950
Hasan, M. K. (2025). How AI quietly undermines the joy and effort of learning: a call for rebalancing education in the digital age. Annals of Medicine and Surgery, 87(8), 4693–4694. https://doi.org/10.1097/ms9.0000000000003456
Imran, M. a. U., Aziz, M. B., Tiwari, A., Saha, S., & Ghimire, A. (2024). Exploring the latest trends in AI Technologies: A study on current state, application and individual impacts. Journal of Computer and Communications, 12(08), 21–36. https://doi.org/10.4236/jcc.2024.128002
Kalyani, L. K. (2024). The role of Technology in Education: Enhancing learning outcomes and 21st century skills. International Journal of Scientific Research in Modern Science and Technology, 3(4), 05–10. https://doi.org/10.59828/ijsrmst.v3i4.199
Kamalov, F., Calonge, D. S., & Gurrib, I. (2023). New era of Artificial intelligence in Education: Towards a sustainable Multifaceted Revolution. Sustainability, 15(16), 12451. https://doi.org/10.3390/su151612451
Lee, S., & Song, K. (2024). Teachers’ and students’ perceptions of AI-generated concept explanations: Implications for integrating generative AI in computer science education. Computers and Education: Artificial Intelligence, 7, 100283. https://doi.org/10.1016/j.caeai.2024.100283
Moemeke, C. (2024). Artificial Intelligence and Machine Learning in enhancing Science Learning Experiences: Exploring Possibilities and Concerns. NIU Journal of Educational Research, 10(2), 59-72. doi: 10.58709/niujed.v10i2.2000
Mondal, H., & Mondal, S. (2024). Artificial Intelligence-Generated content needs a human oversight. Indian Journal of Dermatology, 69(3), 284. https://doi.org/10.4103/ijd.ijd_163_24
Motukeeva, A., Azhibaeva, A., Kazieva, G., Zainiev, R., & Tokonbekova, K. (2025). Analysis of the features shaping students’ thinking to enhance the educational process. Higher Education for the Future, 13(1), 25–49. https://doi.org/10.1177/23476311251372249
Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). Teachers’ trust in AI ‐powered educational technology and a professional development program to improve it. British Journal of Educational Technology, 53(4), 914–931. https://doi.org/10.1111/bjet.13232
Parviz, M. (2024). AI in education: Comparative perspectives from STEM and Non-STEM instructors. Computers and Education Open, 6, 100190. https://doi.org/10.1016/j.caeo.2024.100190
Pérez-Ortiz, M., Novak, E., Bulathwela, S., & Shawe-Taylor, J. (2021). An AI-based learning companion promoting lifelong learning opportunities for all (arXiv Preprint No. arXiv:2112.01242). arXiv. https://doi.org/10.48550/arXiv.2112.01242
Pishghadam, R., Golzar, J., & Miri, M. A. (2022). A new conceptual framework for teacher identity development. Frontiers in Psychology, 13, 876395. https://doi.org/10.3389/fpsyg.2022.876395
Ruiz-Martín, H., Blanco, F., & Ferrero, M. (2024). Which learning techniques supported by cognitive research do students use at secondary school? Prevalence and associations with students’ beliefs and achievement. Cognitive Research: Principles and Implications, 9(1), 44. https://doi.org/10.1186/s41235-024-00567-5
Schmidt, D. A., Alboloushi, B., Thomas, A., & Magalhaes, R. (2025). Integrating artificial intelligence in higher education: perceptions, challenges, and strategies for academic innovation. Computers and Education Open, 9, 100274. https://doi.org/10.1016/j.caeo.2025.100274
Taheri, R., Nazemi, N., Pennington, S. E., Clark, J. A., & Dadgostari, F. (2025). Factors influencing educators’ AI adoption: A grounded meta-analysis review. Computers and Education Artificial Intelligence, 9, 100464. https://doi.org/10.1016/j.caeai.2025.100464
Tan, X., Cheng, G., & Ling, M. H. (2024). Artificial intelligence in teaching and teacher professional development: A systematic review. Computers and Education: Artificial Intelligence, 8, 100355. https://doi.org/10.1016/j.caeai.2024.100355
Tang, Z., & Liao, J. (2025). Unlocking emotional resilience: Exploring the impact of AI-enhanced support systems on EFL teachers’ burnout and EFL students’ well-being in modern classrooms. Acta Psychologica, 260, 105672. https://doi.org/10.1016/j.actpsy.2025.105672
Tripathi, T., Sharma, S. R., Singh, V., Bhargava, P., & Raj, C. (2025). Teaching and learning with AI: a qualitative study on K-12 teachers’ use and engagement with artificial intelligence. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1651217
Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students’ academic development. Education Sciences, 15(3), 343. https://doi.org/10.3390/educsci15030343
Wang, D., Dong, X., & Zhong, J. (2025). Enhance College AI Course Learning Experience with Constructivism-Based Blog Assignments. Education Sciences, 15(2), 217. https://doi.org/10.3390/educsci15020217
Zajda, J. (2021). Constructivist learning theory and creating effective learning environments. In J. Zajda (Ed.), Globalisation and education reforms (pp. 35–50). Springer. https://doi.org/10.1007/978-3-030-71575-5_3
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review. Smart Learning Environments, 11(1). https://doi.org/10.1186/s40561-024-00316-7
Zhou, M., & Peng, S. (2025). The usage of AI in teaching and students’ creativity: the mediating role of learning engagement and the moderating role of AI literacy. Behavioral Sciences, 15(5), 587. https://doi.org/10.3390/bs15050587
