HomePsychology and Education: A Multidisciplinary Journalvol. 39 no. 4 (2025)

Impacts of Artificial Intelligence Technology on Student Learning of Selected College Students in Gumaca, Quezon

Kniffzerc Tarray | Maria Celerina Oreta | Melchor Espiritu | Rosemarie Alfarero

Discipline: Artificial Intelligence

 

Abstract:

This research aimed to describe the impacts of artificial intelligence (AI) technology, specifically ChatGPT, Google Meet, and Google Classroom, on student learning experiences in selected colleges and universities in Gumaca, Quezon. It sought to identify the demographic profile of respondents, their usage of AI technologies, the perceived impacts of AI on various aspects of student learning such as access and equity, personalized learning experience, enhance learning outcome, teacher-student interactions, and practical application, and whether these impacts vary based on demographic profiles. This applied descriptive survey method utilizing researcher-made questionnaires that were validated by experts to gather necessary data. Purposive sampling selected 100 students already integrating AI technology into their studies. Data were analyzed using percentage, frequency, and weighted mean along with One-Way ANOVA to determine significant differences. Findings revealed that the respondents are 19-20 years old (32%), females (67%), and second year college students (32%). All the students use ChatGPT (100%) but not everyone use Google Meet (75%) and Google Classroom (73%). Additionally, the respondents agree on the positive impact of using AI in student learning in terms of access and equity (mean=3.91), personalized learning experience (mean=4.02), enhance learning outcome (mean=4.00), teacher-student interactions (mean=3.87), and practical applications (4.14) but has to improve using AI for 21st century skills development, student progress monitoring, objective grade computation, efficiency in assignment submissions, and providing lifelong learning opportunities. Lastly, ANOVA results showed that there is no significant difference on the perceived impacts of artificial intelligence in students learning when respondents are group according to profiles as to age, sex, and college level. The study recommends for parents, teachers, students, and future researchers to be conscious on the positive application of AI with due guidance, exploration of trends and AI for different context, and consideration of ethical considerations in its utilization.



References:

  1. Aaronson, S. A. (2020). Data Governance, AI, and Trade: Asia as a Case Study (No. 2020-6).
  2. Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431-440. https://link.springer.com/article/10.1007/s43681-021-00096-7
  3. Albina, A. C., & Sumagaysay, L. P. (2020). Employability tracer study of Information Technology Education graduates from a state university in the Philippines. Social Sciences & Humanities Open, 2(1), 100055.
  4. Al-Maroof, R. S., Salloum, S. A., Hassanien, A. E., & Shaalan, K. (2023). Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic. Interactive Learning Environments, 31(3), 1293-1308.
  5. Asirit, L. B. L., & Hua, J. H. (2023). Converging perspectives: Assessing AI readiness and utilization in Philippine higher education. Polaris Global Journal of Scholarly Research and Trends, 2(3), 1-50. https://pgjsrt.com/pgjsrt/index.php/qaj/article/view/152
  6. Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62. https://dergipark.org.tr/en/pub/jai/issue/77844/1337500
  7. Balaquiao, E. C. (2024). Optimizing Students’ Performance through Artificial Intelligence (AI) Technology: A Gamified Approach to Smart Learning Environment. Journal of Pedagogy and Education Science, 3(02), 104-114.
  8. Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17, 1-12. https://link.springer.com/article/10.1186/s41239-020-00218-x
  9. Bhutoria, A. (2022). Personalized education and artificial intelligence in the United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 3, 100068.
  10. Chandra, M., Kumar, K., Thakur, P., Chattopadhyaya, S., Alam, F., & Kumar, S. (2022). Digital technologies, healthcare and COVID-19: Insights from developing and emerging nations. Health and Technology, 12(2), 547-568.
  11. Cheddadi, S., & Bouache, M. (2021). Improving equity and access to higher education using artificial intelligence. In 2021 16th International Conference on Computer Science & Education (ICCSE) (pp. 241-246). IEEE. https://ieeexplore.ieee.org/abstract/document/9569548/
  12. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8, 75264-75278. https://ieeexplore.ieee.org/abstract/document/9069875/
  13. Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100002. https://www.sciencedirect.com/science/article/pii/S2666920X20300023
  14. Chiu, T. K., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2023). Teacher support and student motivation to learn with Artificial Intelligence (AI) based chatbot. Interactive Learning Environments, 1-17.
  15. Dai, Y., Chai, C. S., Lin, P. Y., Jong, M. S. Y., Guo, Y., & Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12(16), 6597.
  16. Escamos, J. A. C., Nueca, R. C., Tordecilla, J. M. C., Baguisa, L., Noveno, S. M. D., Rondina, H. L. A., ... & Reyes, J. M. (2023). Acceptability of Artificial Intelligence Applications in Preparing English Lesson of Professional Teachers in Tuntungin-Putho Integrated National High School Los Banos Laguna. International Journal of Modern Developments in Engineering and Science, 2(12), 24-27. https://journal.ijmdes.com/ijmdes/article/view/191
  17. 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).
  18. Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277-304.
  19. Himang, C. M., Villa Jr, S., Mayorga, N. E., Nolon, N. F., Pajaron Jr, G., & Himang, E. M. (2020). Understanding the Dynamics of ChatGPT Adoption Among Undergraduate Students: Dataset from a Philippine State University. Pajelleno and Himang, Engezbent Mirasol, Understanding the Dynamics of ChatGPT Adoption Among Undergraduate Students: Dataset from a Philippine State University.
  20. Holstein, K., & Doroudi, S. (2022). Equity and artificial intelligence in education. In The ethics of artificial intelligence in education (pp. 151-173). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9780429329067-9/equity-artificial-intelligence-education-kenneth-holstein-shayan-doroudi
  21. Hu, X., He, W., Chiu, T. K., & Zhao, L. (2023). Using a teacher scheme for educational dialogue analysis to investigate student–student interaction patterns for optimal group activities in an artificial intelligence course. Education and Information Technologies, 28(7), 8789-8813. https://link.springer.com/article/10.1007/s10639-022-11556-w
  22. International AIED Society (2010). About the society. http://iaied.org/about/ checked 31/01/11
  23. Jabar, M., Chiong-Javier, E., & Pradubmook Sherer, P. (2024). Qualitative ethical technology assessment of artificial intelligence (AI) and the internet of things (IoT) among filipino Gen Z members: implications for ethics education in higher learning institutions. Asia Pacific Journal of Education, 1-15. https://www.tandfonline.com/doi/abs/10.1080/02188791.2024.2303048
  24. Jackaria, P. M., Hajan, B. H., & Mastul, A. R. H. (2024). A comparative analysis of the rating of college students’ essays by ChatGPT versus human raters. International Journal of Learning, Teaching and Educational Research, 23(2), 478-492.
  25. Kim, J. (2023). Leading teachers’ perspective on teacher-AI collaboration in education. Education and Information Technologies, 1-32. https://link.springer.com/article/10.1007/s10639-023-12109-5
  26. Kim, J., Goodwin, A. L., & Hsieh, B. (2021). The racialized experiences of Asian American teachers in the US: Applications of Asian critical race theory to resist marginalization. Routledge.
  27. Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298-311.
  28. Luengo-Oroz, M., Bullock, J., Pham, K. H., Lam, C. S. N., & Luccioni, A. (2021). From artificial intelligence bias to inequality in the time of COVID-19. IEEE Technology and Society Magazine, 40(1), 71-79. https://ieeexplore.ieee.org/abstract/document/9379034/
  29. Maaliw III, R. R. (2020). Adaptive Virtual Learning Environment based on Learning Styles for Personalizing E-learning System: Design and Implementation. Online Submission, 8(6), 3398-3406. https://eric.ed.gov/?id=ED610591
  30. Melchor, P. J. M., Lomibao, L. S., & Parcutilo, J. O. (2023). Exploring the Potential of AI Integration in Mathematics Education for Generation Alpha—Approaches, Challenges, and Readiness of Philippine Tertiary Classrooms: A Literature Review. Journal of Innovations in Teaching and Learning, 3(1), 39-44.
  31. Mina, P. N. R., Solon, I. M., Sanchez, F. R., Delante, T. K., Villegas, J. K., Basay, F. J., ... & Mutya, R. (2023). Leveraging Education through Artificial Intelligence Virtual Assistance: A Case Study of Visually Impaired Learners. International Journal of Educational Innovation and Research, 2(1), 10-22. https://ejournal.unma.ac.id/index.php/ijeir/article/view/3001
  32. Mistretta, S. (2023). The Singularity Is Emerging: Large Language Models and the Impact of Artificial Intelligence on Education. In Reimagining Education-The Role of E-Learning, Creativity, and Technology in the Post-Pandemic Era. IntechOpen. https://www.intechopen.com/chapters/1148527
  33. Obilor, E. I. (2023). Convenience and purposive sampling techniques: Are they the same. International Journal of Innovative Social & Science Education Research, 11(1), 1-7.
  34. Okolo, C. T. (2023). Addressing global inequity in AI development. Handbook of Critical Studies of Artificial Intelligence, 378.
  35. Pal, D., & Vanijja, V. (2020). Perceived usability evaluation of Microsoft Teams as an online learning platform during COVID-19 using system usability scale and technology acceptance model in India. Children and youth services review, 119, 105535.
  36. Pedroso, J. E. P., Tubola, L. F. A., Mamon, E. J. M., & Sencida, M. (2021). Google Meet: an online platform for class discussion. Journal homepage: www. ijrpr. com ISSN, 2582, 7421. https://www.researchgate.net/profile/John-Erwin-Pedroso/publication/361586691_Google_Meet_An_Online_Platform_for_Class_Discussion/links/62baf15af9dee438e8c88448/Google-Meet-An-Online-Platform-for-Class-Discussion.pdf
  37. Pratama, M. P., Sampelolo, R., & Lura, H. (2023). Revolutionizing education: harnessing the power of artificial intelligence for personalized learning. Klasikal: Journal of Education, Language Teaching and Science, 5(2), 350-357. http://www.journalfkipuniversitasbosowa.org/index.php/klasikal/article/view/877
  38. Prestoza, M. J. R., & Banatao, J. C. M. (2024). Exploring the Efficacy of AI Passion-Driven Pedagogy in Enhancing Student Engagement and Learning Outcomes: A Case Study in Philippines. Asian Journal of Assessment in Teaching and Learning, 14(1), 45-54. https://ojs.upsi.edu.my/index.php/AJATeL/article/view/9324
  39. Romero-Ivanova, C., Shaughnessy, M., Otto, L., Taylor, E., & Watson, E. (2020). Digital practices & applications in a COVID-19 culture. Higher Education Studies, 10(3), 80-87. https://eric.ed.gov/?id=EJ1264741
  40. Salas‐Pilco, S. Z. (2020). The impact of AI and robotics on physical, social‐emotional and intellectual learning outcomes: An integrated analytical framework. British Journal of Educational Technology, 51(5), 1808-1825. https://bera-journals.onlinelibrary.wiley.com/doi/abs/10.1111/bjet.12984
  41. Sidel, J. L., Bleibaum, R. N., & Tao, K. C. (2018). Quantitative descriptive analysis. Descriptive analysis in sensory evaluation, 287-318.
  42. Spurk, D., Hirschi, A., Wang, M., Valero, D., & Kauffeld, S. (2020). Latent profile analysis: A review and “how to” guide of its application within vocational behavior research. Journal of vocational behavior, 120, 103445.
  43. Timmons, K., Cooper, A., Bozek, E., & Braund, H. (2021). The impacts of COVID-19 on early childhood education: Capturing the unique challenges associated with remote teaching and learning in K-2. Early Childhood Education Journal, 49(5), 887-901. https://link.springer.com/article/10.1007/s10643-021-01207-z
  44. Wahono, B., Lin, P. L., & Chang, C. Y. (2020). Evidence of STEM enactment effectiveness in Asian student learning outcomes. International Journal of STEM Education, 7(1), 36.
  45. Williamson, B., & Hogan, A. (2020). Commercialisation and privatisation in/of education in the context of Covid-19.
  46. Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., ... & Li, Y. (2021). A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity, 2021, 1-18. https://www.hindawi.com/journals/complexity/2021/8812542/