ChatGPT integration significantly boosts personalized learning outcomes: A Philippine study
Iris Jane G. Agbong-Coates
Abstract
This study investigated the impact of AI integration, specifically ChatGPT, on personalized learning involving 785 college students in the Philippines who took the online survey. Utilizing regression analysis and an Omnibus ANOVA test, the study examined the influence of AI Integration alongside demographic variables such as age, sex, educational level, and type of school on personalized learning. Results indicate that AI integration can explain a substantial portion of the variability in personalized learning outcomes (approximately 88.54%). Specifically, ChatGPT demonstrates a significant positive effect on personalized learning, suggesting that as ChatGPT integration increases, personalized learning experiences also increase. However, demographic variables such as age, sex, educational level, and type of school show minimal effects on personalized learning outcomes, except for a potential trend for higher scores in private universities and colleges compared to state universities and colleges. These findings underscore the pivotal role of AI technologies, like ChatGPT, in enhancing personalized learning experiences while highlighting the need for further exploration of contextual factors influencing educational outcomes. The implications extend beyond the study to offer insights for educational stakeholders and policymakers, emphasizing the potential benefits of AI-driven personalized learning initiatives. However, limitations such as sample characteristics, measurement bias, and technology accessibility should be addressed in future research endeavors to maximize the benefits of AI integration in education.
Keywords
AI integration, ChatGPT, flow experience, learning engagement, personalized learning, quality of learning
Author information & Contribution
Iris Jane G. Agbong-Coates. Mati Polytechnic College Inc. Email: i_coates@yahoo.com
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
This work was not supported by any funding.
Institutional Review Board Statement
Data and Materials Availability
AI Declaration
Notes
Acknowledgement
References
Abedi, R., Nili Ahmadabadi, M.R., Taghiyareh, F., Aliabadi, K,, & Pourroustaei Ardakani, S. (2021). The effects of personalized learning on achieving meaningful learning outcomes. Interdiscip J Virtual Learn Med Sci. 12(3), 177-187. https://doi.org/10.30476/IJVLMS.2021.89371.1072
Ahmed, Y. (2023). Utilization of ChatGPT in medical education: Applications and implications for curriculum enhancement. Journal of the Society for Medical Informatics of Bosnia & Herzegovina, 31(4), 300–305. https://doi.org/10.5455/aim.2023.31.300-305
Albdrani, R. N., & Al-Shargabi, A. A. (2023). Investigating the effectiveness of CHATGPT for providing personalized learning experience: A case study. International Journal of Advanced Computer Science and Applications, 14(11). https://doi.org/10.14569/ijacsa.2023.01411122
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://doi.org/10.58429/pgjsrt.v2n3a152
Ayeni, O. O., Mohd Al Hamad, N., Chisom, O., Osawaru, B., & Adewusi, O. E. (2024). AI in education: A review of personalized learning and educational technology. GSC Advanced Research and Reviews, 18(2), 261–271. https://doi.org/10.30574/gscarr.2024.18.2.0062
Bevans, R. (2023, June 22). Simple linear regression: An easy introduction & examples. Scribbr. https://www.scribbr.com/statistics/simple-linear-regression/
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. https://doi.org/10.1016/j.caeai.2022.100068
Bobbitt, Z. (2023, February 2). A simple explanation of internal consistency. Statology. https://www.statology.org/internal-consistency/
Cacicio, S. & Riggs, R. (2023). ChatGPT: Leveraging AI to support personalized teaching and learning. Adult Literacy & Learning Impact Network. http://doi.org/10.35847/SCacicio.RRiggs.5.2.70
Carr, B. (2023). Revolutionizing education: Unleashing the power of Chat GPT/AI to empower educators. Technology and the Curriculum Summer 2023. https://pressbooks.pub/techcurr2023/chapter/revolutionizing-education-unleashing-the-power-of-chat-gpt-ai-to-empower-educators/
Chawla, J. (2024, March 6). Generative AI for personalized education: Learning experiences. eNest Technologies. https://enestit.com/generative-ai-for-personalized-education-learning-experiences/
Claned, B. (2024, April 11). The role of AI in personalized learning. https://claned.com/the-role-of-ai-in-personalized-learning/
Crompton, H., & Burke, D. (2024). The educational affordances and challenges of ChatGPT: State of the field. TechTrends, 68, 380–392. https://doi.org/10.1007/s11528-024-00939-0
Csikszentmihalyi, M. (2009). Flow: The psychology of optimal experience (Ser. 1990). Harper and Row.
Das, A., & Malaviya, S. (2023). The impact of AI-driven personalization on learners’ performance. International Journal of Computer Sciences and Engineering, 11(08), 15–22. https://doi.org/10.26438/ijcse/v11i8.1522
Dawes, S. (2023, July 12). How AI can deliver personalised learning and transform academic assessment. Home. https://www.unisa.edu.au/connect/enterprise-magazine/articles/2023/how-ai-can-deliver-personalised-learning-and-transform-academic-assessment/
de Longeaux, G. (2021, June 4). What is the minimum sample size required to perform a meaningful linear regression?. Medium. https://towardsdatascience.com/what-is-the-minimum-sample-size-required-to-perform-a-meaningful-linear-regression-945c0edf1d0
Dekkers, R., Carey, L., & Langhorne, P. (2022). Setting inclusion and exclusion criteria. Making Literature Reviews Work: A Multidisciplinary Guide to Systematic Approaches, 201–233. https://doi.org/10.1007/978-3-030-90025-0_6
Dempere, J., Modugu, K., Hesham, A. & Ramasamy, L.K. (2023). The impact of ChatGPT on higher education. Front. Educ. 8:1206936. https://doi.org/10.3389/feduc.2023.1206936
Elangovan, N., & Sundaravel, E. (2021). Method of preparing a document for survey instrument validation by experts. MethodsX, 8, 101326. https://doi.org/10.1016/j.mex.2021.101326
Estrellado, C.P. & Millar, G.B. (2023). ChatGPT: Towards educational technology micro-level framework. International Journal of Science, Technology, Engineering and Mathematics, 3 (4), 101-127. https://doi.org/10.53378/353035
Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., & Gerjets, P. (2023). ChatGPT in education: Global reactions to AI Innovations. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-42227-6
Gligorea, I., Cioca, M., Oancea, R., Gorski, A.T., Gorski, H., & Tudorache, P. (2023). Adaptive learning using artificial intelligence in e-learning: A literature review. Educ. Sci., 13, 1216. https://doi.org/10.3390/educsci13121216
Gunawardena, M., Bishop, P. & Aviruppola, K. (2024). Personalized learning: The simple, the complicated, the complex and the chaotic. Teaching and Teacher Education, 139, 104429. https://doi.org/10.1016/j.tate.2023.104429
Gunnoo, C. G. avatar C. (2022, April 29). Personalised learning in higher education: Laying the foundations. THE Campus Learn, Share, Connect. https://www.timeshighereducation.com/campus/personalised-learning-higher-education-laying-foundations
Gustilo, L., Ong, E. & Lapinid, M.R. (2024). Algorithmically-driven writing and academic integrity: exploring educators’ practices, perceptions, and policies in AI era. Int J Educ Integr, 20, 3. https://doi.org/10.1007/s40979-024-00153-8
Hammer, C. S. (2011). The importance of participant demographics. American Journal of Speech-Language Pathology, 20(4), 261–261. https://doi.org/10.1044/1058-0360(2011/ed-04)
Hardy, M. (2023, August 31). Designing futures: Personalization in higher education. The EvoLLLution. https://evolllution.com/attracting-students/marketing_branding/designing-futures-personalization-in-higher-education
Hasanein, A.M., & Sobaih, A.E.E. (2023). Drivers and consequences of ChatGPT use in higher education: Key stakeholder perspectives. Eur. J. Investig. Health Psychol. Educ., 13, 2599-2614. https://doi.org/10.3390/ejihpe13110181
Igbokwe, I. C. (2023). Application of artificial intelligence (AI) in educational management. International Journal of Scientific and Research Publications, 13(3). https://doi.org/10.29322/ijsrp.13.03.2023.p13536
Islam, I., & Islam, M.N. (2024). Exploring the opportunities and challenges of ChatGPT in academia. Discov Educ, 3, 31. https://doi.org/10.1007/s44217-024-00114-w
Javaid, M., Haleem, A., Singh, R.P., Khan, S. & Khan, I.H. (2023). Unlocking the opportunities through ChatGPT tool towards ameliorating the education system. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(2), 100115. https://doi.org/10.1016/j.tbench.2023.100115
Jeyaraman, M., Ramasubramanian, S., Balaji, S., Jeyaraman, N., Nallakumarasamy, A., & Sharma, S. (2023). ChatGPT in action: Harnessing artificial intelligence potential and addressing ethical challenges in medicine, education, and scientific research. World Journal of Methodology, 13(4), 170–178. https://doi.org/10.5662/wjm.v13.i4.170
Jian, M. J. (2023). Personalized learning through AI. Advances in Engineering Innovation, 5(1), 16–19. https://doi.org/10.54254/2977-3903/5/2023039
Jo, H. (2024). From concerns to benefits: a comprehensive study of ChatGPT usage in education. Int J Educ Technol High Educ, 21, 35. https://doi.org/10.1186/s41239-024-00471-4
Kamalov, F., Santandreu Calonge, D. & Gurrib, I. (2023). New era of artificial intelligence in education: Towards a sustainable multifaceted revolution. Sustainability, 15, 12451. https://doi.org/10.3390/su151612451
Kanade, V. (2023, April 3). What is linear regression? – Spiceworks – Spiceworks. Spiceworks Inc. https://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-linear-regression/
Katiyar, N., Awasthi, V., Pratap, R., Mishra, K., Shukla, N., Singh, R. & Tiwari, M. (2024). Ai-driven personalized learning systems: Enhancing educational effectiveness. Educational Administration: Theory and Practice, 30(5), 11514–11524. https://doi.org/10.53555/kuey.v30i5.4961
Kiryakova, G. & Angelova, N. (2023). ChatGPT—A challenging tool for the university professors in their teaching practice. Educ. Sci. 2023, 13, 1056. https://doi.org/10.3390/educsci13101056
Labadze, L., Grigolia, M. & Machaidze, L. (2023). Role of AI chatbots in education: systematic literature review. Int J Educ Technol High Educ, 20, 56. https://doi.org/10.1186/s41239-023-00426-1
Lau, A. (2019, May 9). When online survey respondents only “select some that apply.” Pew Research Center Methods. https://www.pewresearch.org/methods/2019/05/09/when-online-survey-respondents-only-select-some-that-apply/
Liu, M., Ren, Y., Nyagoga, L.M., Stonier, F., Wu, Z., & Yu, L. (2023). Future of education in the era of generative artificial intelligence: Consensus among Chinese scholars on applications of ChatGPT in schools. Future in Research Education, 1(1), 72-101. https://doi.org/10.1002/fer3.10
Mai, D.T.T., Da, C.V. & Hanh, N.V. (2024), The use of ChatGPT in teaching and learning: A systematic review through SWOT analysis approach. Front. Educ. 9:1328769. https://doi.org/10.3389/feduc.2024.1328769
Major, L., Francis, G.A. & Tsapali, M. (2021). The effectiveness of technology-supported personalised learning in low- and middle-income countries: A meta-analysis. British Journal of Educational Technology, 52(5), 1935-1964. https://doi.org/10.1111/bjet.13116
Makhambetova, A., Zhiyenbayeva, N., & Ergesheva, E. (2021). Personalized learning strategy as a tool to improve academic performance and motivation of students. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 16(6), 1-17. http://doi.org/10.4018/IJWLTT.286743
Margarella, E. (2023, September 26). The potential of AI and CHATGPT: Empowering learning and communication in the Digital age. Faculty Focus | Higher Ed Teaching & Learning. https://www.facultyfocus.com/articles/teaching-with-technology-articles/the-potential-of-ai-and-chatgpt-empowering-learning-and-communication-in-the-digital-age/
Mcleod, S. (2023, July 31). Likert scale questionnaire: Meaning, examples & analysis. Simply Psychology. https://www.simplypsychology.org/likert-scale.html
Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D.E., Thierry-Aguilera, R. & Gerardou, F.S. (2023). Challenges and opportunities of generative AI for higher education as explained by ChatGPT. Educ. Sci., 13, 856. https://doi.org/10.3390/educsci13090856
Montenegro-Rueda, M., Fernández-Cerero, J., Fernández-Batanero, J. M., & López-Meneses, E. (2023). Impact of the implementation of CHATGPT in education: A systematic review. Computers, 12(8), 153. https://doi.org/10.3390/computers12080153
Mötteli, C., Grob, U., Pauli, C., Reusser, K. & Stebler, R. (2023). The influence of personalized learning on the development of learning enjoyment. International Journal of Educational Research Open, 5, 100271. https://doi.org/10.1016/j.ijedro.2023.100271
Naik, M. (2023). Personalized learning paths: Adapting education with AI-Driven curriculum. European Economic Letters. https://doi.org/10.52783/eel.v14i1.993
Nashwan, S., Sadallah, M. & Bouteraa, M. (2023). Use of ChatGPT in academia: Academic integrity hangs in the balance. Technology in Society, 75, 102370. https://doi.org/10.1016/j.techsoc.2023.102370
Onyenma C., Emeana B., & Nwakamma C.O. (2024). effects integrating personalized learning environment on students’ achievement in social studies in secondary schools in Imo State. International Journal of Education, Learning and Development, 12 (4), 1-15. https://doi.org/10.37745/ijeld.2013
Parikh, A. (2023, July 26). 3 ways personalized learning can benefit higher education. D2L. https://www.d2l.com/blog/3-ways-personalized-learning-can-benefit-higher-education/
Phillips, K. (2023). The effects of personalized learning on student achievement. Dissertations. Paper 225. https://digitalcommons.wku.edu/diss/225
RCoreTeam (2021). R: A Language and environment for statistical computing. (Version 4.1) [Computer software]. https://cran.r-project.org
Reber, R., Canning, E. A., & Harackiewicz, J. M. (2018). Personalized education to increase interest. current directions in psychological science, 27(6), 449–454. https://doi.org/10.1177/0963721418793140
Rejeb, A., Rejeb, K., Appolloni, A., Treiblmaier, H. & Iranmanesh, M. (2024). Exploring the impact of ChatGPT on education: A web mining and machine learning approach. The International Journal of Management Education, 22(1), 100932. https://doi.org/10.1016/j.ijme.2024.100932
Rheinberg, F., Vollmeyer, R., & Engeser, S. (2003). Flow short scale. PsycTESTS Dataset. https://doi.org/10.1037/t47787-000
Rouhiainen, L. (2019, October 14). How AI and data could personalize higher education. Harvard Business Review. https://hbr.org/2019/10/how-ai-and-data-could-personalize-higher-education
Sabzalieva, E., & Valentini, A. (2023, December). ChatGPT and artificial intelligence in higher education: quick start guide. Unesdoc.unesco.org. https://unesdoc.unesco.org/ark:/48223/pf0000385146
Schmid, R., Pauli, C., Stebler, R., Reusser, K., & Petko, D. (2022). Implementation of technology-supported personalized learning—its impact on instructional quality. The Journal of Educational Research, 115(3), 187–198. https://doi.org/10.1080/00220671.2022.2089086
Statisticslaerd. (2018). Linear regression analysis using SPSS statistics. Linear Regression Analysis in SPSS Statistics – Procedure, assumptions and reporting the output. https://statistics.laerd.com/spss-tutorials/linear-regression-using-spss-statistics.php
Sutradhar, A., Adhikari, A., Sutradhar, S. M., & Sen, S. (2023). Use of correlation analysis in educational research. International Research Journal of Education and Technology, 05(05). https://www.irjweb.com/Use%20of%20Correlation%20Analysis%20in%20Educational%20Research.pdf
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory, 1st ed. Springer New York.
Taani, O., & Alabidi, S. (2024). ChatGPT in education: benefits and challenges of ChatGPT for mathematics and science teaching practices. International Journal of Mathematical Education in Science and Technology, 1–30. https://doi.org/10.1080/0020739X.2024.2357341
Thejamoviproject (2022). jamovi. (Version 2.3) [Computer Software]. https://www.jamovi.org.
Thomas, J. (2023). Evaluation of personalized learning. Education Theses and Dissertations. Paper 7. http://hdl.handle.net/10950/4262
Trevino, M.L. (2020). The impact of personalized learning strategies on educational growth and achievement of low socioeconomic elementary students. Doctoral dissertation. https://scholarcommons.sc.edu/etd/5958
Tulsiani, R. (2024, January 16). Chatgpt and the future of personalized learning in higher education. eLearning Industry. https://elearningindustry.com/chatgpt-and-the-future-of-personalized-learning-in-higher-education
Wang, X., & Cheng, Z. (2020). Cross-sectional studies. Chest, 158(1). https://doi.org/10.1016/j.chest.2020.03.012
Williams, K. (2023, October 30). Ethical surveys: A guide to responsible research. SurveySparrow. https://surveysparrow.com/blog/ethical-surveys/
Wu, Y., Zheng, Y., Feng, B., Yang, Y., Kang, K. & Zhao, A. (2024). Embracing ChatGPT for medical education: Exploring its impact on doctors and medical students. JMIR Med Educ. 10:e52483. https://doi.org/10.2196/52483
Yu, H. (2024). The application and challenges of ChatGPT in educational transformation: New demands for teachers’ roles. Heliyon, 10(2), e24289. https://doi.org/10.1016/j.heliyon.2024.e24289
Yuyun, I., & Suherdi, D. (2023). Components and strategies for personalized learning in higher education: A systematic review. Proceedings of the 20th AsiaTEFL-68th TEFLIN-5th iNELTAL Conference (ASIATEFL 2022), 271–290. https://doi.org/10.2991/978-2-38476-054-1_23
Zeb, A., Ullah, R. & Karim, R. (2024). Exploring the role of ChatGPT in higher education: opportunities, challenges and ethical considerations. International Journal of Information and Learning Technology, 41(1), 99-111. https://doi.org/10.1108/IJILT-04-2023-0046
Zhai, X. (2023). ChatGPT and AI: The Game Changer for Education. AI4STEM Education Center, 2(2). https://doi.org/10.13140/RG.2.2.31107.37923
Zhang, P. & Tur, G. (2023). A systematic review of ChatGPT use in K-12 education. European Journal of Education Research, Development and Policy, 59(2), e12599. https://doi.org/10.1111/ejed.12599
Zhong, L. (2022). A systematic review of personalized learning in higher education: Learning content structure, learning materials sequence, and Learning Readiness Support. Interactive Learning Environments, 31(10), 7053–7073. https://doi.org/10.1080/10494820.2022.2061006
Cite this article:
Agbong-Coates, I.J.G. (2024). ChatGPT integration significantly boosts personalized learning outcomes: A Philippine study. International Journal of Educational Management and Development Studies, 5 (2), 165-186. https://doi.org/10.53378/353067
License:
This work is licensed under a Creative Commons Attribution (CC BY 4.0) International License.
Find this article in:
Most read articles
- Social media usage and the academic performance of Filipino junior high school students
- A narrative exploration of romantic experiences and ideal relationship standards among Filipino Gen Z
- Exploring the factors influencing commuters’ satisfaction and the use of public utility buses in Quezon City, Philippines
- Tiktok made me book it: The impact of Tiktok on tourism destination selection of generation Z and millennials in Manila
- Students’ exposure to social media and their radical involvement on the societal issues in the Philippines
- ChatGPT integration significantly boosts personalized learning outcomes: A Philippine study
- Senior high school students’ awareness and literacy on computer software applications
- Self-perception of ABM students towards their academic, social and emotional college preparedness
- Enhancing financial literacy among Pantawid Pamilyang Pilipino Program beneficiaries
- Emotional intelligence and leadership efficacy of university student leaders