Student Perceptions of Generative AI in Personalized Distance Learning: The Moderating Effects of Usage Frequency and Faculty Encouragement
DOI:
https://doi.org/10.69569/jip.2025.686Keywords:
Faculty encouragement, Ethical AI use, Educational technology, Generative AI, Moderating effect, Personalized distance learning, SDG 4, Student agencyAbstract
As generative artificial intelligence (GAI) tools such as ChatGPT, Grammarly, and Quillbot become increasingly embedded in digital education, understanding how students perceive their role in personalized distance learning, across both asynchronous and synchronous modes, remains crucial. Anchored in Sustainable Development Goal 4 (SDG 4), which promotes inclusive and equitable quality education and lifelong learning opportunities for all, this study investigates how GAI utilization relates to students’ perceptions of usefulness, motivational impact, and ethical implications. It also examines whether usage frequency and faculty encouragement moderate these relationships. Using a descriptive-correlational design with moderation analysis, data were collected from 327 undergraduate students at Tagoloan Community College through a validated questionnaire (CVI = 0.94). Overall, findings revealed that students perceived GAI as highly beneficial for learning and self-motivation, reflecting growing confidence in using AI responsibly. Results from General Linear Modeling indicated that GAI utilization was a significant predictor of students’ perceptions (F = 169.32, p < .001, η²ₚ = .345), suggesting that direct engagement with AI tools strongly shapes educational value and learning experiences. However, neither usage frequency (F = 0.99, p = .396) nor faculty encouragement (F = 0.75, p = .475) significantly moderated this relationship. Interestingly, despite limited faculty support (11.3%) and the predominant use of ChatGPT (96.9%), students demonstrated ethical awareness through responses that emphasized citation practices, verification of AI-generated outputs, and the avoidance of plagiarism, indicating reflective and responsible learning behaviors. These findings highlight the primacy of student agency over institutional influence in fostering meaningful AI engagement. The study recommends that educators and institutions implement structured, ethical, and student-centered integration of AI tools into curricula through digital literacy workshops, academic integrity guidelines, and scaffolded AI-supported learning tasks to enhance autonomy, critical engagement, and responsible technology use, aligned with the objectives of SDG 4.
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References
Abdel-Rahman, H. M., & Alsmadi, M. K. (2024). Exploring faculty perceptions and acceptability of AI in teaching. Education and Information Technologies, 29, 48. https://doi.org/10.1007/s44217-024-00128-4
Al-Emran, M., Al-Sharafi, M. A., Foroughi, B., Al-Qaysi, N., Mansoor, D., Beheshti, A., & Ali, N. A. (2025). Evaluating the influence of generative AI on students’ academic performance through the lenses of TPB and TTF using a hybrid SEM-ANN approach. Education and Information Technologies, 1–31. https://doi.org/10.1007/s10639-025-13485-w
Almassaad, A., Alajlan, H., & Alebaikan, R. (2024). Student perceptions of generative artificial intelligence: Investigating utilization, benefits, and challenges in higher education. Systems, 12(10), 385. https://doi.org/10.3390/systems12100385
Alotaibi, S. M. F. (2025). Determinants of generative artificial intelligence (GenAI) adoption among university students and its impact on academic performance: The mediating role of trust in technology. Interactive Learning Environments, 1–30. https://doi.org/10.1080/10494820.2025.2492785
Babbie, E. R. (2021). The practice of social research (15th ed.). Cengage Learning. https://shorturl.at/6GPXu
Bano, S., Zawacki-Richter, O., & Qayyum, A. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 49. https://doi.org/10.1186/s41239-023-00411-8
Bartlett, J. E., Kotrlik, J. W., & Higgins, C. C. (2001). Organizational research: Determining appropriate sample size in survey research. Information Technology, Learning, and Performance Journal, 19(1), 43–50. https://tinyurl.com/pr7wp8uj
Belshaw, D., & Perry, M. (2024). Generative AI and academic integrity in higher education: A systematic literature review. Information, 16(4), 296. https://doi.org/10.3390/info16040296
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications. https://tinyurl.com/2t8bkrh2
Dwivedi, Y. K., Hughes, D. L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., & Wamba, S. F. (2023). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2022.102642
Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 00149. https://doi.org/10.15406/bbij.2017.05.00149
Fakhri, M. M., Ahmar, A. S., Isma, A., Rosidah, R., & Fadhilatunisa, D. (2024). Exploring generative AI tools frequency: Impacts on attitude, satisfaction, and competency in achieving higher education learning goals. EduLine: Journal of Education and Learning Innovation, 4(1), 196–208. https://doi.org/10.35877/454ri.eduline2592
Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to design and evaluate research in education (10th ed.). McGraw-Hill Education. https://tinyurl.com/59ysms2s
García-Peñalvo, F. J., Moreno, P., & Conde, M. Á. (2025). Faculty acceptance and use of generative AI in educational contexts. Frontiers in Education, 10, 1427450. https://doi.org/10.3389/feduc.2025.1427450
Gilli, M., & Tzovla, E. (2025). Generative artificial intelligence in university education: Innovation or threat to academic values? IT Professional, 27(2), 17–25. https://doi.org/10.1109/MITP.2025.3249123
Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). The Guilford Press. https://tinyurl.com/yje4r2j3
Israel, M., & Hay, I. (2006). Research ethics for social scientists: Between ethical conduct and regulatory compliance. SAGE Publications. https://psycnet.apa.org/record/2006-10839-000
Ivanov, S., Soliman, M., Tuomi, A., Alkathiri, N. A., & Al-Alawi, A. N. (2024). Drivers of generative AI adoption in higher education through the lens of the theory of planned behaviour. Technology in Society, 77, 102521. https://doi.org/10.1016/j.techsoc.2024.102521
Jisc. (2025). Student perceptions of AI 2025. National Centre for AI. https://tinyurl.com/4e5nze28
Khateeb, A., & Al-Emran, M. (2025). Mastering knowledge: The impact of generative AI on student engagement and ethics in higher education. Studies in Higher Education. https://doi.org/10.1080/03075079.2025.2487570
Khlaif, Z. N., Ayyoub, A., Hamamra, B., Bensalem, E., Mitwally, M. A., Ayyoub, A., & Shadid, F. (2024). University teachers’ views on the adoption and integration of generative AI tools for student assessment in higher education. Education Sciences, 14(10), 1090. https://doi.org/10.3390/educsci14101090
Lin, Y., Zhang, T., & Liu, M. (2024). Student perceptions of generative artificial intelligence in academic support contexts. Systems, 12(10), 385. https://doi.org/10.3390/systems12100385
Martínez-Muñoz, G., Ortega-Tudela, J. M., & González, M. C. (2024). Transforming learning with generative AI: From student perceptions to the design of an educational solution. ResearchGate. https://tinyurl.com/2m5bnwdv
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/EWZM9535
Obenza, B. N., Salvahan, A., Rios, A. N., Solo, A., Alburo, R. A., & Gabila, R. J. (2024). University students' perception and use of ChatGPT: Generative artificial intelligence (AI) in higher education. International Journal of Human Computing Studies, 5(12), 5–18. https://doi.org/10.31149/ijhcs.v5i12.5033
Ogola, P. O., Okonji, M. I., & Dlamini, B. T. (2025). A comparative study of student perceptions on generative AI in Africa. Discover Artificial Intelligence, 5(1), 4. https://doi.org/10.1016/j.discia.2025.100004
Orb, A., Eisenhauer, L., & Wynaden, D. (2001). Ethics in qualitative research. Journal of Nursing Scholarship, 33(1), 93–96. https://doi.org/10.1111/j.1547-5069.2001.00093.x
Prinsloo, P., & Slade, S. (2024). Students' perceptions of generative AI-powered learning analytics: An important exploration. Journal of Learning Analytics, 11(1), 45–61. https://doi.org/10.18608/jla.2024.8609
Quiño, J. B. (2022). Students’ perception and satisfaction of Google classroom as instructional medium for teaching and learning. Canadian Journal of Educational and Social Studies, 2(2), 1-25. https://doi.org/10.53103/cjess.v2i2.22
Saihi, A., Ben-Daya, M., Hariga, M., & As'ad, R. (2024). A structural equation modeling analysis of generative AI chatbots adoption among students and educators in higher education. Computers and Education: Artificial Intelligence, 7, 100274. https://doi.org/10.1016/j.caeai.2024.100274
Salem, M. A., & Alturki, U. (2025). Reassessing academic integrity in the age of AI: A systematic review. Heliyon, 11(2), e02649. https://doi.org/10.1016/j.heliyon.2025.e02649
Sergeeva, O. V., Zheltukhina, M. R., Shoustikova, T., Tukhvatullina, L. R., Dobrokhotov, D. A., & Kondrashev, S. V. (2025). Understanding higher education students’ adoption of generative AI technologies: An empirical investigation using UTAUT2. Contemporary Educational Technology, 17(2), ep571. https://doi.org/10.30935/cedtech/16039
Sutherland, K., & Holmes, W. (2024). The rapid rise of generative AI and its implications for academic integrity. Discover Education, 2, 100007. https://doi.org/10.1016/j.discove.2024.100007
Sweeney, T., & Velasquez, D. (2024). Exploring faculty perceptions and concerns regarding artificial intelligence in education. Heliyon, 10(3), e12008894. https://doi.org/10.1016/j.heliyon.2024.e12008894
Wiles, R., Crow, G., Heath, S., & Charles, V. (2008). The management of confidentiality and anonymity in social research. International Journal of Social Research Methodology, 11(5), 417–428. https://doi.org/10.1080/13645570701622231
World Medical Association. (2013). World medical association declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA, 310(20), 2191–2194. https://doi.org/10.1001/jama.2013.281053
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0
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