How do College Students Express AI Fatigue? A Content and Keyword-in-Context Analysis of Academic AI Use
Keywords:
artificial intelligence; AI fatigue ; cognitive fatigue ; emotional fatigue ; motivational fatigue ; higher education ; content analysisAbstract
This study examined how Filipino college students express AI fatigue in academic contexts. Using a qualitative descriptive design, the study analyzed 1,000 open-ended online survey responses collected from public higher education institutions in the Philippines through purposive convenience sampling. Directed qualitative content analysis, complemented by keyword-in-context (KWIC) analysis, was used to identify and contextualize fatigue-related expressions in student narratives. Analysis revealed four categories: cognitive fatigue, emotional fatigue, motivational fatigue, and absence of fatigue expression. Cognitive fatigue emerged as the most frequently expressed dimension, characterized by mental tiredness, information overload, and difficulty sustaining focus. Motivational and emotional fatigue were also evident and frequently co-occurred with cognitive fatigue. A substantial proportion of responses described AI use without any fatigue-related strain. These findings indicate that AI fatigue is multidimensional and unevenly experienced. The findings suggest that the use of academic AI does not uniformly reduce effort but may shift cognitive demands toward evaluation and information processing. By grounding interpretation in students’ own language, the study contributes empirical evidence to discussions on sustainable AI integration in higher education, particularly within digitally intensive developing contexts.
https://doi.org/10.26803/ijlter.25.3.29
References
Acut, D. P., Gamusa, E. V., Pernaa, J., Yuenyong, C., Pantaleon, A. T., Espina, R. C., Sim, M. J. C., & Garcia, M. B. (2025). AI Shaming Among Teacher Education Students: A Reflection on Acceptance and Identity in the Age of Generative Tools. In M. B. Garcia, J. Rosak-Szyrocka, & A. Bozkurt (Eds.), Pitfalls of AI Integration in Education: Skill Obsolescence, Misuse, and Bias (pp. 95–118). IGI Global. https://doi.org/10.4018/979-8-3373-0122-8.ch005
Al Mashagbeh, M., Alsharqawi, M., Tudevdagva, U., & Khasawneh, H. J. (2025). Student engagement with artificial intelligence tools in academia: a survey of Jordanian universities. Frontiers in Education, Volume 10-2025. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2025.1550147
Almassaad, A., Alajlan, H., & Alebaikan, R. (2024). Student Perceptions of Generative Artificial Intelligence: Investigating Utilization, Benefits, and Challenges in Higher Education. In Systems 12(10), 385. https://doi.org/10.3390/systems12100385
An, R., Qian, G., Mumtaz, A., Alotaibi, K. A., & Wang, X. (2025). Digital fatigue and academic resilience among university students with grit and flexibility as mediators. Scientific Reports, 15(1), 45407. https://doi.org/10.1038/s41598-025-29313-7
Assarroudi, Abdolghader, Heshmati Nabavi, Fatemeh, Armat, Mohammad Reza, Ebadi, Abbas, & Vaismoradi, Mojtaba. (2018). Directed qualitative content analysis: the description and elaboration of its underpinning methods and data analysis process. Journal of Research in Nursing, 23(1), 42–55. https://doi.org/10.1177/1744987117741667
Bakker, A. B., & Mostert, K. (2024). Study Demands–Resources Theory: Understanding Student Well-Being in Higher Education. Educational Psychology Review, 36(3), 92. https://doi.org/10.1007/s10648-024-09940-8
Bengtsson, M. (2016). How to plan and perform a qualitative study using content analysis. NursingPlus Open, 2, 8–14. https://doi.org/10.1016/j.npls.2016.01.001
Biri, A. K. A., Arilin, A. T., Traigo, M. L. M., Jaji, G. T., Salasain, R. J., & Abdilla, A.-B. Y. (2025). AI Dilemma in Schoolwork: Student Anxiety and Fairness Perceptions in AI Schoolwork Accusations. Journal of Education and Practice, 16(11), 1–15. https://www.iiste.org/Journals/index.php/JEP/article/viewFile/63505/65637
Bobrytska, V. I., Krasylnykova, H. V., Protska, S. M., Skyrda, T. S., Krasylnykov, S. R., & Chkhalo, O. M. (2025). Digitalisation in Higher Education: A Systematic Review of its Impact on Health, Cognition, and Social Interaction. International Journal of Learning, Teaching and Educational Research, 24(4), 339–358. https://doi.org/10.26803/ijlter.24.5.18
Bray, M. V. du. (2023). Context Matters: Conducting Word-Based Analysis in Qualitative Research. In The Handbook of Teaching Qualitative and Mixed Research Methods (1st Editio). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9781003213277-58/context-matters-margaret-du-bray
Brockbank, R. B. (2025). Factors Influencing Cognitive Load: Exploring Affective Elements, Motivational Constructs, and Regulatory Strategies in Learning [All Graduate Theses and Dissertations, Fall 2023 to Present]. https://digitalcommons.usu.edu/etd2023/606/
Buenadicha-Mateos, M., Sánchez-Hernández, M. I., & González-López, Ó. R. (2022). Analysis of the Emotional Exhaustion Derived From Techno-Stress in the Next Generation of Qualified Employees. Frontiers in Psychology, 13, 792606. https://doi.org/10.3389/fpsyg.2022.792606
Carter, N., Bryant-Lukosius, D., DiCenso, A., Blythe, J., & Neville, A. J. (2014). The use of triangulation in qualitative research. Oncology Nursing Forum, 41(5), 545–547. https://doi.org/10.1188/14.ONF.545-547
Chandrasekar, A., Clark, S. E., Martin, S., Vanderslott, S., Flores, E. C., Aceituno, D., Barnett, P., Vindrola-Padros, C., & Vera San Juan, N. (2024). Making the most of big qualitative datasets: a living systematic review of analysis methods. Frontiers in Big Data, 7, 1455399. https://doi.org/10.3389/fdata.2024.1455399
Co, S. J. (2025). Artificial intelligence in Philippine education: A narrative review of applications, perceptions, and challenges. International Research Journal of Science, Technology, Education, and Management, 5(2), 25–38. https://doi.org/10.5281/zenodo.15902950
DataReportal. (2024). Digital 2024: The Philippines. https://datareportal.com/reports/digital-2024-philippines
Deep, P. Das, & Chen, Y. (2025). Student Burnout and Mental Health in Higher Education DuringCOVID-19: Online Learning Fatigue, Institutional Support, and theRole of Artificial Intelligence. Higher Education Studies, 15(2), 381–401. https://doi.org/10.5539/hes.v15n2p381
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. https://doi.org/10.1016/j.compedu.2024.105224
Digital Education Council. (2024). Digital Education Council Global AI Student Survey 2024. https://www.digitaleducationcouncil.com/post/digital-education-council-global-ai-student-survey-2024
Domingo, M. A. T. (2026). AI, Impostor Syndrome, and Emotional Resilience: Rethinking SEL and Student Well-Being in Inclusive AI-Enhanced ESL Classrooms. In G. Durak & Serkan Çankaya (Eds.), Shaping Inclusive Educational Policies and Practices With Generative AI (pp. 237–268). IGI Global. https://doi.org/10.4018/979-8-3373-3745-6.ch009
Dong, L., Tang, X., & Wang, X. (2025). Examining the effect of artificial intelligence in relation to students’ academic achievement: A meta-analysis. Computers and Education: Artificial Intelligence, 8, 100400. https://doi.org/10.1016/j.caeai.2025.100400
Doria, J. O. (2025). The Double-Edged Sword : A Review of Artificial Intelligence Integration in the Philippine Educational System. Southeast Asian Journal of Science and Technology, 9(1), 257–264. https://www.sajst.org/online/index.php/sajst/article/view/341/289
Doyle, L., McCabe, C., Keogh, B., Brady, A., & McCann, M. (2020). An overview of the qualitative descriptive design within nursing research. Journal of Research in Nursing : JRN, 25(5), 443–455. https://doi.org/10.1177/1744987119880234
Durgungoz, A., & Kharrufa, A. (2025). “ChatGPT is like a study buddy, a teacher and sometimes just a friend”: a longitudinal exploration of students’ interactions, perception and acceptance. Interactive Learning Environments, 1–20. https://doi.org/10.1080/10494820.2025.2509276
Erlingsson, C., & Brysiewicz, P. (2017). A hands-on guide to doing content analysis. African Journal of Emergency Medicine : Revue Africaine de La Medecine d’urgence, 7(3), 93–99. https://doi.org/10.1016/j.afjem.2017.08.001
Espartinez, A. S. (2024). Exploring student and teacher perceptions of ChatGPT use in higher education: A Q-Methodology study. Computers and Education: Artificial Intelligence, 7, 100264. https://doi.org/10.1016/j.caeai.2024.100264
Espartinez, A. S. (2025). Between Innovation and Tradition: A Narrative Inquiry of Students’ and Teachers’ Experiences with ChatGPT in Philippine Higher Education. In Social Sciences 14(6). https://doi.org/10.3390/socsci14060359
Essel, H. B., Vlachopoulos, D., Essuman, A. B., & Amankwa, J. O. (2024). ChatGPT effects on cognitive skills of undergraduate students: Receiving instant responses from AI-based conversational large language models (LLMs). Computers and Education: Artificial Intelligence, 6, 100198. https://doi.org/10.1016/j.caeai.2023.100198
Fife, W. (2020). The Importance of Counting for Qualitative Research. In W. Fife (Ed.), Counting as a Qualitative Method: Grappling with the Reliability Issue in Ethnographic Research, 121–128. Springer International Publishing. https://doi.org/10.1007/978-3-030-34803-8_8
Funa, A. A., & Gabay, R. A. E. (2025). Policy guidelines and recommendations on AI use in teaching and learning: A meta-synthesis study. Social Sciences & Humanities Open, 11, 101221. https://doi.org/10.1016/j.ssaho.2024.101221
Funa, A. A., & Gabay, R. A. E. (2026). Exploring perspectives toward generative artificial intelligence integration in science education: A cross-generational study. Social Sciences & Humanities Open, 13, 102358. https://doi.org/10.1016/j.ssaho.2025.102358
Gallagher, R. T. B. G. (2016). Using Microsoft Excel to code and thematically analyse qualitative data: a simple, cost-effective approach. All Ireland Journal of Teaching and Learning in Higher Education, 8(2), 2811–2814. https://ojs.aishe.org/index.php/aishe-j/article/view/281/467
Garcia, M. B., Goi, C. L., Shively, K., Maher, D., Rosak-Szyrocka, J., Happonen, A., Bozkurt, A., & Damaševi?ius, R. (2025). Understanding Student Engagement in AI-Powered Online Learning Platforms: A Narrative Review of Key Theories and Models. In A. R. Gierhart (Ed.), Cases on Enhancing P-16 Student Engagement With Digital Technologies, 1–30. IGI Global. https://doi.org/10.4018/979-8-3693-5633-3.ch001
Garcia, M. B., Metwally, A. H. S., Wang, T., Ofosu-Ampong, K., Alasgarova, R., Nerantzi, C., Nixon, J., & Bozkurt, A. (2026). Psychological and Developmental Repercussions of Pervasive AI Usage in Schools: A Review of Educational Benefits and Challenges. In J. J. Araujo & S. Snider (Eds.), Responsible AI Integration in Education, 87–118. IGI Global. https://doi.org/10.4018/979-8-3373-6007-2.ch003
Giray, L., Nemeño, J., Braganaza, J., Lucero, S. M., & Bacarra, R. (2024). A survey on digital device engagement, digital stress, and coping strategies among college students in the Philippines. International Journal of Adolescence and Youth, 29(1), 2371413. https://doi.org/10.1080/02673843.2024.2371413
Gonsalves, C. (2024). Generative AI’s Impact on Critical Thinking: Revisiting Bloom’s Taxonomy. Journal of Marketing Education, 02734753241305980. https://doi.org/10.1177/02734753241305980
Gonsalves, C. (2025). Addressing student non-compliance in AI use declarations: implications for academic integrity and assessment in higher education. Assessment & Evaluation in Higher Education, 50(4), 592–606. https://doi.org/10.1080/02602938.2024.2415654
Halliday, S., Lavis, T., Callaghan, P., & Chur-Hansen, A. (2025). Generative AI in higher education psychology programs: a scoping review exploring the opportunities for its use in assessment methods. Australian Psychologist, 1–12. https://doi.org/10.1080/00050067.2025.2580236
Hannah, David R, & Lautsch, Brenda A. (2010). Counting in Qualitative Research: Why to Conduct it, When to Avoid it, and When to Closet it. Journal of Management Inquiry, 20(1), 14–22. https://doi.org/10.1177/1056492610375988
Heidari, K. (2025). Cognitive fatigue induced by spaced and massed practice: effects on idiom accuracy across L2 proficiency levels. International Review of Applied Linguistics in Language Teaching, 1–27. https://doi.org/10.1515/iral-2025-0039
Hsieh, Hsiu-Fang, & Shannon, Sarah E. (2005). Three Approaches to Qualitative Content Analysis. Qualitative Health Research, 15(9), 1277–1288. https://doi.org/10.1177/1049732305276687
Huang, D., Hash, N., Cummings, J. J., & Prena, K. (2025). Academic cheating with generative AI: Exploring a moral extension of the theory of planned behavior. Computers and Education: Artificial Intelligence, 8, 100424. https://doi.org/10.1016/j.caeai.2025.100424
Hunter, D. J. (2018). Defining exploratory-descriptive qualitative (EDQ) research and considering its application to healthcare. Worldwide Nursing Conference. http://eprints.gla.ac.uk/180272/
Ibrahim, R. K., Khaled, M., Almansoori, M., Almazrouei, M., Ashraf, A., Alahmedi, S. H., & Hendy, A. (2025). Screen time and stress: understanding how digital burnout influences health among nursing students. BMC Nursing, 24(1), 990. https://doi.org/10.1186/s12912-025-03621-9
Inan, F. A., Sosi, E. T., Unal, D., Marzban, F., & Alleyne Bayne, G. (2025). The Impact of Coursework Demand and Learning Engagement on Mental Fatigue in Online College Students. International Journal of Environmental Research and Public Health, 22(12). https://doi.org/10.3390/ijerph22121860
Jaeger, S. R., & Rasmussen, M. A. (2021). Importance of data preparation when analysing written responses to open-ended questions: An empirical assessment and comparison with manual coding. Food Quality and Preference, 93, 104270. https://doi.org/10.1016/j.foodqual.2021.104270
Karim, E., Pavel, H. R., Nikanfar, S., Hebri, A., Roy, A., Nambiappan, H. R., Jaiswal, A., Wylie, G. R., & Makedon, F. (2024). Examining the Landscape of Cognitive Fatigue Detection: A Comprehensive Survey. In Technologies 12(3), 38. https://doi.org/10.3390/technologies12030038
Khalifa, M., & Albadawy, M. (2024). Using artificial intelligence in academic writing and research: An essential productivity tool. Computer Methods and Programs in Biomedicine Update, 5, 100145. https://doi.org/10.1016/j.cmpbup.2024.100145
Khan, S. M., & Suhluli, S. (2025). Generative AI and Cognitive Challenges in Research: Balancing Cognitive Load, Fatigue, and Human Resilience. In Technologies 13(13), 486. https://doi.org/10.3390/technologies13110486
Kirsanov, O., Kushwah, L., & Selvaretnam, G. (2025). Beyond Detection: How Students Use—and Hide—AI in Online Assessments and What Authentic Tasks Can Do About It. Journal of Academic Ethics, 24(1), 14. https://doi.org/10.1007/s10805-025-09691-3
Klimova, B., & Pikhart, M. (2025). Exploring the effects of artificial intelligence on student and academic well-being in higher education: a mini-review. Frontiers in Psychology, 16, 1498132. https://doi.org/10.3389/fpsyg.2025.1498132
Kok, A. (2022). Cognitive control, motivation and fatigue: A cognitive neuroscience perspective. Brain and Cognition, 160, 105880. https://doi.org/10.1016/j.bandc.2022.105880
Kunasegaran, K., Ismail, A. M. H., Ramasamy, S., Gnanou, J. V., Caszo, B. A., & Chen, P. L. (2023). Understanding mental fatigue and its detection: a comparative analysis of assessments and tools. PeerJ, 11, e15744. https://doi.org/10.7717/peerj.15744
Lane, A. M., Micklewright, D., & Meijen, C. (2025). Understanding Fatigue: A Psychological Framework for Health and Performance. Sci, 7(4), 162.https://doi.org/10.3390/sci7040162
Lee, D., Arnold, M., Srivastava, A., Plastow, K., Strelan, P., Ploeckl, F., Lekkas, D., & Palmer, E. (2024). The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives. Computers and Education: Artificial Intelligence, 6, 100221. https://doi.org/10.1016/j.caeai.2024.100221
Lee, H.-Y., & Wu, T.-T. (2025). Enhancing blended learning discussions with a Scaffolded Knowledge Integration–Based ChatGPT mobile instant messaging system. Computers & Education, 237, 105375. https://doi.org/10.1016/j.compedu.2025.105375
Li?an, D.-E. (2025). Psychological “effects” of digital technology: a meta-analysis. Frontiers in Psychology, 16, 1560516. https://doi.org/10.3389/fpsyg.2025.1560516
Liu, Z., Xie, Y., Sun, Z., Liu, D., Yin, H., & Shi, L. (2023). Factors associated with academic burnout and its prevalence among university students: a cross-sectional study. BMC Medical Education, 23(1), 317. https://doi.org/10.1186/s12909-023-04316-y
Ma, G., Tian, S., Song, Y., Chen, Y., Shi, H., & Li, J. (2025). When Technology Meets Anxiety:The Moderating Role of AI Usage in the Relationship Between Social Anxiety, Learning Adaptability, and Behavioral Problems Among Chinese Primary School Students. Psychology Research and Behavior Management, 18, 151–167. https://doi.org/10.2147/PRBM.S502337
Maloney, S., Axelsen, M., Stone, C., Galligan, L., Redmond, P., Brown, A., Turner, J., & Lawrence, J. (2023). Defining and exploring online engagement fatigue in a university context. Computers and Education Open, 4, 100139. https://doi.org/10.1016/j.caeo.2023.100139
Martín-Moncunill, D., & Alonso Martínez, D. (2025). Students’ Trust in AI and Their Verification Strategies: A Case Study at Camilo José Cela University. In Education Sciences, 15(10), 1307. https://doi.org/10.3390/educsci15101307
Mayring, P. (2015). Qualitative Content Analysis: Theoretical Background and Procedures. In A. Bikner-Ahsbahs, C. Knipping, & N. Presmeg (Eds.), Approaches to Qualitative Research in Mathematics Education: Examples of Methodology and Methods, 365-380. Springer Netherlands. https://doi.org/10.1007/978-94-017-9181-6_13
Mo, F., Huang, J., Yang, Y., Özen, Z., Maeda, Y., & Olenchak, F. R. (2025). Undergraduate Students’ Learning Outcomes with ChatGPT: A Meta-Analytic Study,. Computers and Education: Artificial Intelligence, 10, 100536. https://doi.org/10.1016/j.caeai.2025.100536
Mohamed, A. M. (2024). Exploring the potential of an AI-based Chatbot (ChatGPT) in enhancing English as a Foreign Language (EFL) teaching: perceptions of EFL Faculty Members. Education and Information Technologies, 29(3), 3195–3217. https://doi.org/10.1007/s10639-023-11917-z
Müller, T., & Apps, M. A. J. (2019). Motivational fatigue: A neurocognitive framework for the impact of effortful exertion on subsequent motivation. Neuropsychologia, 123, 141–151. https://doi.org/10.1016/j.neuropsychologia.2018.04.030
Neagu, S. N., & Vieriu, A. M. (2025). Digital and Psychological Well-Being Among Technical University Students: Exploring the Impact of Digital Engagement in Higher Education. In Education Sciences 15(9), 1192. https://doi.org/10.3390/educsci15091192
Patac, L. P., & Patac, A. V. (2025). Using ChatGPT for academic support: Managing cognitive load and enhancing learning efficiency – A phenomenological approach. Social Sciences & Humanities Open, 11, 101301. https://doi.org/10.1016/j.ssaho.2025.101301
Promsiri, T. (2025). AI and the psychology of educational disruption: Historical patterns and cognitive implications. Acta Psychologica, 260, 105637. https://doi.org/10.1016/j.actpsy.2025.105637
Qiang, J., He, X., Xia, Z., Huang, J., & Xu, C. (2024). The association between intolerance of uncertainty and academic burnout among university students: the role of self-regulatory fatigue and self-compassion. Frontiers in Public Health, 12, 1441465. https://doi.org/10.3389/fpubh.2024.1441465
Qu, Y., & Wang, J. (2025). The Impact of AI Guilt on Students’ Use of ChatGPT for Academic Tasks: Examining Disciplinary Differences. Journal of Academic Ethics. https://doi.org/10.1007/s10805-025-09643-x
Reed, H. C. (2022). E-Learning Fatigue and the Cognitive, Educational, and Emotional Impacts on Communication Sciences and Disorders Students During COVID-19. Perspectives of the ASHA Special Interest Groups, 7(6), 1885–1902. https://doi.org/10.1044/2022_PERSP-22-00049
Sechelski, A. N., & Onwuegbuzie, A. J. (2019). A Call for Enhancing Saturation at the Qualitative Data Analysis Stage via the Use of Multiple Qualitative Data Analysis Approaches. The Qualitative Report, 24(4), 795–821. https://doi.org/10.46743/2160-3715/2019.3554
Silvia, C., Kopczynski, K., & Alexander, A. (2025). Navigating Digital Fatigue in Educational Environments. Creative Education, 16(11), 1883–1896. https://doi.org/10.4236/ce.2025.1611115
Slotta, D. (2025). Internet usage in the Philippines - statistics & facts. https://www.statista.com/topics/5660/internet-economy-in-the-philippines
Song, J., & Liu, S. (2025). Dark personality traits are associated with academic misconduct, frustration, negative thinking, and generative AI use habits: the case of Sichuan art universities. BMC Psychology, 13(1), 633. https://doi.org/10.1186/s40359-025-02949-w
Stojanov, A., Liu, Q., & Koh, J. H. L. (2024). University students’ self-reported reliance on ChatGPT for learning: A latent profile analysis. Computers and Education: Artificial Intelligence, 6, 100243. https://doi.org/10.1016/j.caeai.2024.100243
Sustaningrum, R., & Haldaka, M. (2025). Student utilization and perceptions of AI technology for academic purposes: a mixed-method analysis. Cogent Education, 12(1), 2553835. https://doi.org/10.1080/2331186X.2025.2553835
Tafesse, Wondwesen, Aguilar, Mary Precy, Sayed, Sabaa, & Tariq, Urwa. (2024). Digital Overload, Coping Mechanisms, and Student Engagement: An Empirical Investigation Based on the S-O-R Framework. Sage Open, 14(1), 21582440241236090. https://doi.org/10.1177/21582440241236087
Tian, J., & Zhang, R. (2025). Learners’ AI dependence and critical thinking: The psychological mechanism of fatigue and the social buffering role of AI literacy. Acta Psychologica, 260, 105725. https://doi.org/10.1016/j.actpsy.2025.105725
Vieriu, A. M., & Petrea, G. (2025). The Impact of Artificial Intelligence (AI) on Students’ Academic Development. In Education Sciences 15(3), 343. https://doi.org/10.3390/educsci15030343
Wang, J., & Fan, W. (2025). The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis. Humanities and Social Sciences Communications, 12(1), 621. https://doi.org/10.1057/s41599-025-04787-y
Wang, X., Zhao, X., & Yu, C. (2025). The influence of information and social overload on academic performance: The role of social media fatigue, cognitive depletion, and self-control. Revista de Psicodidáctica (English Ed.), 30(2), 500164. https://doi.org/10.1016/j.psicoe.2025.500164
Yeung, L. K. C., Chow, D. P. L., Wong, P. H., & Lau, S. S. S. (2025). In ChatGPT they trust: a study of students’ perceptions and misuse of ChatGPT in higher education. AI and Ethics, 6(1), 11. https://doi.org/10.1007/s43681-025-00855-w
Zhang, S., Zhao, X., Zhou, T., & Kim, J. H. (2024). Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior. International Journal of Educational Technology in Higher Education, 21(1), 34. https://doi.org/10.1186/s41239-024-00467-0
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Emmanuel B. Parreño, John Paul P. Miranda, Rhiziel P. Manalese, Jeanky Salenga-Mendez, Jumel C. Miller, Argel S. Payumo
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published by IJLTER are licensed under a Creative Commons Attribution Non-Commercial No-Derivatives 4.0 International License (CCBY-NC-ND4.0).
