Students’ Lived Experiences on the Use of Artificial Intelligence Tools in Learning Social Studies
DOI:
https://doi.org/10.65339/ijsis.V1.I3.183Keywords:
Academic Integrity, Artificial Intelligence, Learner Autonomy, Phenomenology, Self-Regulated Learning, Social Studies, Technology AcceptanceAbstract
This study explored the lived experiences of Grade 12 Humanities and Social Sciences (HUMSS) students in using Artificial Intelligence (AI) tools for learning Social Studies in Malapatan III District, Schools Division of Sarangani, Philippines. Anchored in Moustakas’ Transcendental Phenomenology, Davis’ Technology Acceptance Model, and Zimmerman’s Self-Regulated Learning Theory, the study employed a qualitative phenomenological research design. Nine Grade 12 HUMSS students from three public secondary schools participated in in-depth interviews, while three Social Studies teachers participated in a focus group discussion for data triangulation. Participants were selected through purposive sampling, and data were gathered using a validated semi-structured interview guide and analyzed thematically. The findings identified twelve emerging themes encompassing students’ lived experiences, the contexts shaping AI-supported learning, and their perspectives on future AI integration. Participants perceived AI as a valuable learning resource that improved conceptual understanding, academic productivity, confidence, classroom engagement, and emotional well-being. However, excessive dependence, reduced independent thinking, information accuracy, and academic integrity emerged as significant concerns. Students emphasized the importance of responsible AI use, critical evaluation, and continued teacher guidance. The study recommends strengthening AI literacy, ethical practices, and teacher capacity building through the proposed Responsible Artificial Intelligence Integration and Learning Empowerment Program (RAILEP). The findings primarily support Sustainable Development Goal 4 (Quality Education) by promoting responsible, inclusive, and learner-centered technology integration. The study contributes to educational and institutional sustainability by informing school-based guidelines and instructional practices that balance technological innovation with academic integrity, learner autonomy, and meaningful human interaction.
References
Abah, J., Terungwa, U., & Chinaka, T. (2025). Conceptualizing the influence of artificial intelligence on students’ academic integrity. Journal of Mathematics Instruction, Social Research and Opinion. https://doi.org/10.58421/misro.v4i2.383 DOI: https://doi.org/10.58421/misro.v4i2.383
Abdulmumin, A. I. (2025). The educator’s guide to AI: What you can’t afford not to know. Al-Wazan: Journal of Arabic Education. https://doi.org/10.58223/al-wazan.v3i1.337 DOI: https://doi.org/10.58223/al-wazan.v3i1.337
Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI Ethics, 2, 431–440. https://doi.org/10.1007/s43681-021-00096-7 DOI: https://doi.org/10.1007/s43681-021-00096-7
Alasgarova, R., & Rzayev, J. (2025). The implications of artificial intelligence for teacher agency and teacher-student relationships through the Technology Acceptance Model. International Journal of Technology in Education and Science. https://doi.org/10.46328/ijtes.645 DOI: https://doi.org/10.46328/ijtes.645
Ali, M., Khan, T., Khattak, M. N., & Sener, I. (2024). Synergizing AI and business: Maximizing innovation, creativity, decision precision, and operational efficiency in high-tech enterprises. Journal of Open Innovation: Technology, Market, and Complexity. https://doi.org/10.1016/j.joitmc.2024.100352 DOI: https://doi.org/10.1016/j.joitmc.2024.100352
Alm, A. (2024). Exploring autonomy in the AI wilderness: Learner challenges and choices. Education Sciences. https://doi.org/10.3390/educsci14121369 DOI: https://doi.org/10.3390/educsci14121369
Almalki et al. (2025). Integration of artificial intelligence in nursing education: A cross-national exploration. BMC Nursing, 24. https://doi.org/10.1186/s12912-025-03689-3 DOI: https://doi.org/10.1186/s12912-025-03689-3
Almeida, F., Silva, A., Lopes, S., & Braz, I. (2025). Understanding recruiters’ acceptance of artificial intelligence: Insights from the Technology Acceptance Model. Applied Sciences. https://doi.org/10.3390/app15020746 DOI: https://doi.org/10.3390/app15020746
Almufarreh, A. (2024). Determinants of students’ satisfaction with AI tools in education: A PLS-SEM-ANN approach. Sustainability. https://doi.org/10.3390/su16135354 DOI: https://doi.org/10.3390/su16135354
Alshamy, A., Al-Harthi, A., & Abdullah, S. (2025). Perceptions of generative AI tools in higher education: Insights from students and academics at Sultan Qaboos University. Education Sciences. https://doi.org/10.3390/educsci15040501 DOI: https://doi.org/10.3390/educsci15040501
Aly, A., Setiadi, S., & Zuriyati, Z. (2025). Exploring AI's ethical impact on teacher-student dynamics in English language learning. Journal La Edusci. https://doi.org/10.37899/journallaedusci.v6i1.1703 DOI: https://doi.org/10.37899/journallaedusci.v6i1.1703
Anghel, G. A., Zanfir, C., Matei, F.-L., Voicu, C., & Neacșa, R. (2025). The integration of artificial intelligence in academic learning practices: A comprehensive approach. Education Sciences. https://doi.org/10.3390/educsci15050616 DOI: https://doi.org/10.3390/educsci15050616
Arar, K., Özen, H., Polat, G., & Turan, S. (2025). Artificial intelligence, generative artificial intelligence, and research integrity: A hybrid systemic review. Smart Learning Environments, 12. https://doi.org/10.1186/s40561-025-00403-3 DOI: https://doi.org/10.1186/s40561-025-00403-3
Azman, Ö., & Tümkaya, S. (2025). Navigating the ethical landscape of AI integration in education: Balancing innovation and responsibility. https://doi.org/10.12688/f1000research.160011.1 DOI: https://doi.org/10.12688/f1000research.160011.1
Bazine, I. (2025). Exploring the development of the Technology Acceptance Model (TAM): A chronological overview. International Journal of Research and Scientific Innovation. https://doi.org/10.51244/ijrsi.2025.120600138 DOI: https://doi.org/10.51244/IJRSI.2025.120600138
Black, R. W., & Tomlinson, B. (2025). University students describe how they adopt AI for writing and research in a general education course. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-92937-2 DOI: https://doi.org/10.1038/s41598-025-92937-2
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. DOI: https://doi.org/10.1191/1478088706qp063oa
Bugnos, J., Protacio, A., Billanes, E., Caman, K., Diego, S., Formacion, C., Galamay, N., & Molato, M. (2022). Traditionalist to progressivist: Lived experiences of generation X teachers in the new normal. Globus Journal of Progressive Education, 12(1), 90–94. https://globusedujournal.in/wp-content/uploads/2022/05/GE-121-JJ22-12-Jezelle-D.-Bugnos.pdf
Burgos, J., Córdova, P., & Sanjinés, A. (2023). Assessing student-perceived impact of using artificial intelligence tools: Construction of a synthetic index of application in higher education. Cogent Education, 11. https://doi.org/10.1080/2331186x.2023.2287917 DOI: https://doi.org/10.1080/2331186X.2023.2287917
Butt, S., Fida, F., Abbas, A., & Batool, S. (2025). Artificial intelligence and the future of education: Opportunities and challenges. ACADEMIA International Journal for Social Sciences. https://doi.org/10.63056/acad.004.03.0598 DOI: https://doi.org/10.63056/ACAD.004.03.0598
Casal-Otero, L., Catalá, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K-12: A systematic literature review. International Journal of STEM Education, 10, 1–17. https://doi.org/10.1186/s40594-023-00418-7 DOI: https://doi.org/10.1186/s40594-023-00418-7
Chang, W., & Sun, J. (2024). Evaluating AI's impact on self-regulated language learning: A systematic review. System. https://doi.org/10.1016/j.system.2024.1034 DOI: https://doi.org/10.1016/j.system.2024.103484
Costa, M. (2025). Artificial intelligence and data literacy in rural schools’ teaching practices: Knowledge, use, and challenges. Education Sciences. https://doi.org/10.3390/educsci15030352 DOI: https://doi.org/10.3390/educsci15030352
Cotton, D., Cotton, P., & Shipway, J. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61, 228–239. https://doi.org/10.1080/14703297.2023.2190148 DOI: https://doi.org/10.1080/14703297.2023.2190148
Del Rosario Navas Bonilla, C., Carrasco, L., Pupiales, J., & Noriega, D. (2025). The future of education: A systematic literature review of self-directed learning with AI. Future Internet, 17, 366. https://doi.org/10.3390/fi17080366 DOI: https://doi.org/10.3390/fi17080366
Dimitriadou, E., & Lanitis, A. (2023). A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms. Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00231-3 DOI: https://doi.org/10.1186/s40561-023-00231-3
Duan, H., & Zhao, W. (2024). The effects of educational artificial intelligence-powered applications on teachers' perceived autonomy, professional development for online teaching, and digital burnout. International Review of Research in Open and Distributed Learning, 25(3), 57–76. DOI: https://doi.org/10.19173/irrodl.v25i3.7659
Eager, B., & Brunton, R. (2023). Prompting higher education towards AI-augmented teaching and learning practice. Journal of University Teaching and Learning Practice. https://doi.org/10.53761/1.20.5.02 DOI: https://doi.org/10.53761/1.20.5.02
Eaton, S. (2023). Postplagiarism: Transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology. International Journal for Educational Integrity, 19, 1–10. https://doi.org/10.1007/s40979-023-00144-1 DOI: https://doi.org/10.1007/s40979-023-00144-1
Ekaterina, K., Ana, M., & Maia, Z. (2025). Academic integrity within the medical curriculum in the age of generative artificial intelligence. Health Science Reports, 8. https://doi.org/10.1002/hsr2.70489 DOI: https://doi.org/10.1002/hsr2.70489
Estrellado, C. J., & Miranda, J. C. (2023). Artificial intelligence in the Philippine educational context: Circumspection and future inquiries. International Journal of Scientific and Research Publications, 13(5), 16–22. DOI: https://doi.org/10.29322/IJSRP.13.05.2023.p13704
Fabrique, K. S., et al. (2024). Senior high school students perceptions and awareness on the ethical implications of artificial intelligence. Medwin Publishers. https://medwinpublishers.com/OAJDA/senior-high-school-students-perceptions-and-awareness-on-the-ethical-implications-of-artificial-intelligence.pdf
Falebita, O., & Kok, P. J. (2024). Artificial intelligence tools usage: A structural equation modeling of undergraduates’ technological readiness, self-efficacy and attitudes. Journal for STEM Education Research, 8, 257–282. https://doi.org/10.1007/s41979-024-00132-1 DOI: https://doi.org/10.1007/s41979-024-00132-1
Famoso et al. (2023). The experiences of senior high school students using artificial intelligence-based educational applications and tools.
Faza, A., & Lestari, I. (2025). Self-regulated learning in the digital age: A systematic review of strategies, technologies, benefits, and challenges. The International Review of Research in Open and Distributed Learning. https://doi.org/10.19173/irrodl.v26i2.8119 DOI: https://doi.org/10.19173/irrodl.v26i2.8119
Francis, N. J., Jones, S., & Smith, D. P. (2025). Generative AI in higher education: Balancing innovation and integrity. British Journal of Biomedical Science, 81, 14048. https://shura.shu.ac.uk/34687/1/bjbs-81-14048.pdf DOI: https://doi.org/10.3389/bjbs.2024.14048
Gabayan, V., Liggayu, B., & Segundo, C. (2025, October 30). The generative shift: A systematic review on generative AI use and policies in the Philippines. TechRxiv. https://doi.org/10.36227/techrxiv DOI: https://doi.org/10.36227/techrxiv.176185661.14388203/v1
Gabila, R. (2024). University students' perception and use of ChatGPT: Generative artificial intelligence (AI) in higher education. International Journal of Human-Computer Studies. https://doi.org/10.31149/ijhcs.v5i12.5033
Gecker, J. (2025, September 12). The rise of AI tools forces schools to reconsider what counts as cheating. Associated Press. https://apnews.com/article/ai-cheating-school-chatgpt-4f89a552e9093ce2180471b4d4736675
Giray, L., De Silos, P., Adornado, A., Buelo, R., Galas, E., Reyes-Chua, E., Santiago, C., & Ulanday, M. (2024). Use and impact of artificial intelligence in Philippine higher education: Reflections from instructors and administrators. Internet Reference Services Quarterly, 28, 315–338. https://doi.org/10.1080/10875301.2024.2352746 DOI: https://doi.org/10.1080/10875301.2024.2352746
Grassini, S. (2023). The future of assessment in higher education? Education, 3-13. DOI: https://doi.org/10.3390/educsci13070692
Gyonyoru, K. I. K., & Katona, J. (2024). Student perceptions of AI-enhanced adaptive learning systems: A pilot survey. 2024 IEEE 7th International Conference and Workshop Óbuda on Electrical and Power Engineering (CANDO-EPE) (pp. 93–98). https://doi.org/10.1109/cando-epe65072.2024.10772884 DOI: https://doi.org/10.1109/CANDO-EPE65072.2024.10772884
Hassan, A., & Ahmad, I. (2025). Artificial intelligence: Its influence on academic performance of Grade 11 senior high school students. ResearchGate. https://www.researchgate.net/publication/395259802_Artificial_Intelligence_Its_Influence_on_Academic_Performance_of_Grade_11_Senior_High_School_Students
Holechek, S., & Sreenivas, V. (2024). Abstract 1557 generative AI in undergraduate academia: Enhancing learning experiences and navigating ethical terrains. Journal of Biological Chemistry. https://doi.org/10.1016/j.jbc.2024.105921 DOI: https://doi.org/10.1016/j.jbc.2024.105921
Hu, B. (2023). Analysis of the impact of artificial intelligence on our lives. The Frontiers of Society, Science and Technology, 3(7), 45–50. https://doi.org/10.25236/fsst.2021.030709 DOI: https://doi.org/10.25236/FSST.2021.030709
Ikram, M., Mohamed Hanefar, S. B. S., Saleem, S. M. S. U., & Zulfiqar, F. Z. (2026). Artificial intelligence in education: A systematic review of personalized learning trends and future directions. Frontiers in Education, 11. https://doi.org/10.3389/feduc.2026.1782626 DOI: https://doi.org/10.3389/feduc.2026.1782626
Ismaila Mounkoro. (2025). Integrating AI in classroom management: Improving educational efficiency and teacher workflows. Journal of Information Systems Engineering and Management, 10(45s), 823–834. https://doi.org/10.52783/jisem.v10i45s.9033 DOI: https://doi.org/10.52783/jisem.v10i45s.9033
Jin, S., Im, K., Yoo, M., Roll, I., & Seo, K. (2023). Supporting students’ self-regulated learning in online learning using artificial intelligence applications. International Journal of Educational Technology in Higher Education, 20, 1–21. https://doi.org/10.1186/s41239-023-00406-5 DOI: https://doi.org/10.1186/s41239-023-00406-5
Joseph Ymalay, R., & Ymalay, J. (2023). The experiences of senior high school students using artificial intelligence-based educational applications and tools.
Kersna, L., Laak, K., Lepp, L., & Pedaste, M. (2025). Supporting self-regulated learning in primary education: Using written learning guides in the lessons. Education Sciences. https://doi.org/10.3390/educsci15010060 DOI: https://doi.org/10.3389/feduc.2025.1594556
Khatri, B., & Karki, P. (2023). Artificial intelligence (AI) in higher education: Growing academic integrity and ethical concerns. Nepalese Journal of Development and Rural Studies. https://doi.org/10.3126/njdrs.v20i01.64134 DOI: https://doi.org/10.3126/njdrs.v20i01.64134
Kong, P. (2025). Exploring the role of Chinese EFL teachers' interpersonal behaviours in their students' acceptance and adoption of AI tools through the lens of Technology Acceptance Model (TAM). European Journal of Education. https://doi.org/10.1111/ejed.70275 DOI: https://doi.org/10.1111/ejed.70275
Kumar, R. (2025). Understanding PSE students’ reactions to the postplagiarism concept: A quantitative analysis. International Journal for Educational Integrity, 21. https://doi.org/10.1007/s40979-025-00182-x DOI: https://doi.org/10.1007/s40979-025-00182-x
Laflamme, A., & Bruneault, F. (2025). Redefining academic integrity in the age of generative artificial intelligence: The essential contribution of artificial intelligence ethics. Journal of Scholarly Publishing, 56, 481–509. https://doi.org/10.3138/jsp-2024-1125 DOI: https://doi.org/10.3138/jsp-2024-1125
Lavidas, K., Voulgari, I., Papadakis, S., Athanassopoulos, S., Anastasiou, A., Filippidi, A., Komis, V., & Karacapilidis, N. I. (2024). Determinants of humanities and social sciences students' intentions to use artificial intelligence applications for academic purposes. Inf., 15, 314. https://doi.org/10.3390/info15060314 DOI: https://doi.org/10.3390/info15060314
Lawasi, M. C., Rohman, V. A., & Shoreamanis, M. (2024). The use of AI in improving student's critical thinking skills. Proceedings Series on Social Sciences & Humanities. https://doi.org/10.30595/pssh.v18i.1279 DOI: https://doi.org/10.30595/pssh.v18i.1279
Liu, M., Sun, C., Li, S., & Jiang, Z. (2025). The impact of artificial intelligence (AI) on students' academic development. Education Sciences, 15(3), 343. https://www.mdpi.com/2227-7102/15/3/343
Lo, L. (2023). An initial interpretation of the U.S. Department of Education's AI report: Implications and recommendations for academic libraries. The Journal of Academic Librarianship. https://doi.org/10.1016/j.acalib.2023.102761 DOI: https://doi.org/10.1016/j.acalib.2023.102761
Luo, J. (2024). A critical review of GenAI policies in higher education assessment: A call to reconsider the “originality” of students’ work. Assessment & Evaluation in Higher Education, 49, 651–664. https://doi.org/10.1080/02602938.2024.2309963 DOI: https://doi.org/10.1080/02602938.2024.2309963
Marcos, F. (2023). Message – Office of the President of the Republic of the Philippines. Transactions of the National Academy of Science and Technology. https://doi.org/10.57043/transnastphl.2023.3308 DOI: https://doi.org/10.57043/transnastphl.2023.3308
Mollo, P. P. (2025). Student teachers’ perceptions of artificial intelligence chatbots for classroom practices: An interpretative phenomenological analysis. Interdisciplinary Journal of Education Research, 7(1), a05. https://doi.org/10.38140/ijer-2025.vol7.1.05 DOI: https://doi.org/10.38140/ijer-2025.vol7.1.05
Moustakas, C. (1994). Phenomenological research methods. SAGE Publications. DOI: https://doi.org/10.4135/9781412995658
Moya et al. (2024). Academic integrity and artificial intelligence in higher education (HE) contexts: A rapid scoping review. Canadian Perspectives on Academic Integrity. https://doi.org/10.55016/ojs/cpai.v7i3.78123 DOI: https://doi.org/10.55016/ojs/cpai.v7i3.78123
Muñoz, A. V. (2025). Integrating artificial intelligence across the Philippine educational continuum: Opportunities, challenges, and regulatory frameworks to foster responsible AI use from primary to graduate levels for sustainable development.
Ng, D., Tan, C., & Leung, J. (2024). Empowering student self-regulated learning and science education through ChatGPT: A pioneering pilot study. Br. J. Educ. Technol., 55, 1328–1353. https://doi.org/10.1111/bjet.13454 DOI: https://doi.org/10.1111/bjet.13454
Nopas, D. (2025). Algorithmic learning or learner autonomy? Rethinking AI’s role in digital education. Qualitative Research Journal. https://doi.org/10.1108/qrj-11-2024-0282 DOI: https://doi.org/10.1108/QRJ-11-2024-0282
Obenza, B., et al. (2024). University students' perception and use of ChatGPT: Generative artificial intelligence (AI) in higher education. International Journal of Human-Computer Studies. https://doi.org/10.31149/ijhcs.v5i12.5033 DOI: https://doi.org/10.31149/ijhcs.v5i12.5033
Octaberlina et al. (2024). Exploring the impact of AI threats on originality and critical thinking in academic writing. Edelweiss Applied Science and Technology. https://doi.org/10.55214/25768484.v8i6.3878 DOI: https://doi.org/10.55214/25768484.v8i6.3878
Opesemowo, O. (2024). Artificial intelligence in education, bridging community gap: A phenomenological approach. International Journal of New Education, 14. https://doi.org/10.24310/ijne.14.2024.20505 DOI: https://doi.org/10.24310/ijne.14.2024.20505
Peta, S. (2023). Journey of artificial intelligence frontier: A comprehensive overview. Global Journal of Computer Science and Technology. https://doi.org/10.34257/gjcstdvol23is2pg9 DOI: https://doi.org/10.34257/GJCSTDVOL23IS2PG9
Protacio, A. V. (2021). Culturally-diverse students lived experiences in English as language of instruction. Globus Journal of Progressive Education, 11(2), 150–155. https://www.globusedujournal.in/wp-content/uploads/2021/11/GE-JD21-Adrian-V.-Protacio.pdf
Qadir, J. (2023). Engineering education in the era of ChatGPT: Promise and pitfalls of generative AI for education. In 2023 IEEE Global Engineering Education Conference (EDUCON) (pp. 1–9). Kuwait City, Kuwait. DOI: https://doi.org/10.1109/EDUCON54358.2023.10125121
Rani, J. (2025). Artificial intelligence in education: Exploring opportunities, challenges, and ethical implications in a rapidly evolving landscape. Shodh Manjusha: An International Multidisciplinary Journal. https://doi.org/ DOI: https://doi.org/10.70388/sm250179
Reyes, R. A. (2025). Evaluating the influence of AI on motivation levels of senior high school students in a private educational institution H12-01 Group C. https://www.researchgate.net/publication/387754474
Rezaie, F. (2024). Examining the impact of artificial intelligence on psychological well-being and stress reduction: Analyzing the effectiveness of modern technology in improving mental health. Journal of Cognition, Behavior, Learning. https://doi.org/10.61838/jcbl.1.1.13 DOI: https://doi.org/10.61838/jcbl.1.1.13
Roga et al. (2025). The perceived awareness, attitudes, benefits, and threats of AI tools among senior high school students. Psychology and Education: A Multidisciplinary Journal. https://doi.org/10.70838/pemj.350207 DOI: https://doi.org/10.70838/pemj.350207
Ronanki, R., & Nguyen, A. (2023). Artificial intelligence for the real world. International Research Journal of Modernization in Engineering Technology and Science. https://doi.org/10.56726/irjmets42512 DOI: https://doi.org/10.56726/IRJMETS42512
Slimi, Z., & Carballido, B. V. (2023). Navigating the ethical challenges of artificial intelligence in higher education: An analysis of seven global AI ethics policies. TEM Journal, 12(2), 590–602. DOI: https://doi.org/10.18421/TEM122-02
Sonza, S. J., Protacio, A., Padojinog, W. M., Peñafiel, N., Javier, B. A., Dolojo, R., Magtulis, C., & Barcelona, C. (2022). Facilitators and barriers of students’ modular distance learning in English: A phenomenological inquiry. Globus Journal of Progressive Education, 12(1), 76–79. https://globusedujournal.in/wp-content/uploads/2022/04/GE-121-JJ22-9-Adrian-V.-Protacio-1.pdf
Sousa, A. D. (2024). Artificial intelligence in positive mental health: A narrative review. Frontiers in Digital Health, 6. https://doi.org/10.3389/fdgth.2024.1280235 DOI: https://doi.org/10.3389/fdgth.2024.1280235
Sugiarso, B., Nurjamin, A., Judijanto, L., Firdausiyah, L., & Hidayat, A. (2024). The effect of artificial intelligence in improving student learning achievement in high school. World Psychology. https://doi.org/10.55849/wp.v3i1.569 DOI: https://doi.org/10.55849/wp.v3i1.569
Sumilong, M. J. (2025, September). Instructional affect and learner motivation in generative AI-restrictive and permissive classrooms. Frontiers in Education, 10, Article 1626802. https://doi.org/10.3389/feduc.2025.1626802 DOI: https://doi.org/10.3389/feduc.2025.1626802
Sutrisno, D., Inawati, I., & Hermanto, H. (2024). Enhancing student learning autonomously: Exploring the global impact of artificial intelligence. English Language Teaching Educational Journal. https://doi.org/10.12928/eltej.v6i2.9100 DOI: https://doi.org/10.12928/eltej.v6i2.9100
Tacogue, S., Protacio, A., Alocada, J. J., Gevero, G. A., Diama, B. G., Denoy, D., Lumasag, J. R., & Oronce, R., Jr. (2022). Learning in isolation: Exploring the lived experiences of students in self-directed learning in English. Globus Journal of Progressive Education, 12(1), 81–84. https://globusedujournal.in/wp-content/uploads/2022/04/GE-121-JJ22-Stefi-Marie-S.-Tacogue.pdf
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 DOI: https://doi.org/10.1016/j.actpsy.2025.105672
Tapalova, O., Zhiyenbayeva, N., & Gura, D. (2022). Artificial intelligence in education: AIEd for personalised learning pathways. Electronic Journal of e-Learning. https://doi.org/10.34190/ejel.20.5.2597 DOI: https://doi.org/10.34190/ejel.20.5.2597
Taşkın, M. (2025). Artificial intelligence in personalized education: Enhancing learning outcomes through adaptive technologies and data-driven insights. Human Computer Interaction. https://doi.org/10.62802/ygye0506 DOI: https://doi.org/10.62802/ygye0506
University of the Philippines Principles for Responsible and Trustworthy Artificial Intelligence Philippines. (2023). University of the. https://up.edu.ph/up-principles for-responsible-artificial-intelligence
U.S. Department of Education. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. U.S. Department of Education, Office of Educational Technology. https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf
Van Den Berg, S., & Papadopoulos, P. (2024). Summative assessment with artificial intelligence: Qualitative analysis and comparison of technology acceptance in student and teacher populations. Innovations in Education and Teaching International, 62, 1529–1544. https://doi.org/10.1080/14703297.2024.2436613 DOI: https://doi.org/10.1080/14703297.2024.2436613
Vargas-Murillo, A., Morales, D., & Martínez-Béjar, R. (2023). Explainable artificial intelligence (XAI) in education: A systematic literature review. Sustainability, 15(18), 13589.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926 DOI: https://doi.org/10.1287/mnsc.46.2.186.11926
Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students’ academic development. Education Sciences, 15(3). https://doi.org/10.3390/educsci15030343 DOI: https://doi.org/10.3390/educsci15030343
Villaver, R. G., & Cabigas, M. E. A. (2025). Learning engagement with AI tools and academic performance. International Journal of Multidisciplinary Research and Analysis. https://doi.org/10.47191/ijmra/v8-i07-55 DOI: https://doi.org/10.47191/ijmra/v8-i07-55
Xu, W., & Fan, O. (2021). A systematic review of AI role in the educational system based on a proposed conceptual framework. Education and Information Technologies, 27, 4195–4223. https://doi.org/10.1007/s10639-021-10774 DOI: https://doi.org/10.1007/s10639-021-10774-y
Yan, L., Alwabel, A., & Mohamad, U. (2025). AI-powered education: Transforming teacher-student interactions and advancing sustainable learning practices. European Journal of Education. https://doi.org/10.1111/ejed.70351 DOI: https://doi.org/10.1111/ejed.70351
Yeruva, A. R. (2023). Providing a personalized healthcare service to the patients using AIOPs monitoring. Eduvest-Journal of Universal Studies, 3(2), 327–334. DOI: https://doi.org/10.59188/eduvest.v3i2.742
Yoko Mochizuki, Eric Bruillard, & Audrey Bryan. (2025, May 21). The ethics of AI or techno-solutionism? UNESCO’s policy guidance on AI in education. British Journal of Sociology of Education. https://doi.org/10.1080/01425692.2025.2502808 DOI: https://doi.org/10.1080/01425692.2025.2502808
Younas, M., El-Dakhs, D., & Jiang, Y. (2025). A comprehensive systematic review of AI-driven approaches to self-directed learning. IEEE Access, 13, 38387–38403. https://doi.org/10.1109/access.2025.3546319 DOI: https://doi.org/10.1109/ACCESS.2025.3546319
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. DOI: https://doi.org/10.1207/s15430421tip4102_2
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