PENGARUH PEMANFAATAN AI DAN LINGKUNGAN BELAJAR TERHADAP PRESTASI AKADEMIK
DOI:
https://doi.org/10.51878/academia.v6i4.15275Keywords:
Pemanfaatan AI, Lingkungan Belajar, Prestasi AkademikAbstract
This study is motivated by the increasing use of Artificial Intelligence (AI) in learning and the critical role of the learning environment in shaping students’ academic performance. However, a gap persists between the potential of AI utilization and the quality of learning environments in achieving optimal academic outcomes. This study aims to examine the empirical effects of AI utilization and learning environment on students’ academic performance. A quantitative approach with a survey design was employed, involving 117 active student respondents from the Primary School Teacher Education for Madrasah (PGMI) Study Program at IAIN Curup. Data were collected using a structured questionnaire based on a Likert scale and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The analysis procedures included outer model evaluation to assess validity and reliability, and inner model evaluation to test the relationships between variables and the proposed hypotheses. The findings indicate that AI utilization has a positive and significant effect on academic performance ( ; ), while the learning environment also has a positive and more substantial effect ( ; ). The R-square value of 0.735 demonstrates that both variables explain 73.5% of the variance in academic performance. This study concludes that the integration of effective AI utilization and a supportive learning environment is a key determinant in enhancing students’ academic achievement.
ABSTRAK
Penelitian ini dilatarbelakangi oleh meningkatnya pemanfaatan Artificial Intelligence (AI) dalam pembelajaran serta pentingnya learning environment (lingkungan belajar) dalam menentukan keberhasilan akademik mahasiswa. Namun, masih terdapat kesenjangan antara potensi penggunaan AI dan kualitas lingkungan belajar terhadap pencapaian prestasi akademik. Penelitian ini bertujuan untuk menganalisis pengaruh pemanfaatan AI dan lingkungan belajar terhadap prestasi akademik mahasiswa secara empiris. Penelitian menggunakan pendekatan kuantitatif dengan desain survei terhadap 117 responden mahasiswa aktif Program Studi Pendidikan Guru Madrasah Ibtidaiyah (PGMI) IAIN Curup. Data dikumpulkan melalui kuesioner berbasis skala Likert dan dianalisis menggunakan Structural Equation Modeling berbasis Partial Least Squares (PLS-SEM). Tahapan analisis meliputi evaluasi outer model untuk menguji validitas dan reliabilitas, serta evaluasi inner model untuk menguji hubungan antarvariabel dan hipotesis penelitian. Hasil penelitian menunjukkan bahwa pemanfaatan AI berpengaruh positif dan signifikan terhadap prestasi akademik ( ; ), dan lingkungan belajar juga berpengaruh positif dan signifikan dengan pengaruh yang lebih kuat ( ; ). Nilai R-square sebesar menunjukkan bahwa kedua variabel mampu menjelaskan 73,5% variasi prestasi akademik. Penelitian ini menyimpulkan bahwa integrasi pemanfaatan teknologi AI dan lingkungan belajar yang kondusif menjadi faktor kunci dalam meningkatkan prestasi akademik mahasiswa.
Downloads
References
Ahmad, F. B., Al-Nawaiseh, S. J., & Al-Nawaiseh, A. J. (2023). Receptivity level of faculty members in universities using digital learning tools: A UTAUT perspective. International Journal of Emerging Technologies in Learning (iJET), 18(13), 209–219. https://doi.org/10.3991/ijet.v18i13.39763
Al-Bukhrani, M. A., Alrefaee, Y., & Tawfik, M. (2025). Adoption of AI writing tools among academic researchers: A theory of reasoned action approach. PLoS ONE, 20(1), e0313837. https://doi.org/10.1371/journal.pone.0313837
Ali, I., Warraich, N. F., & Butt, K. (2024). Acceptance and use of Artificial Intelligence and AI-based applications in education: A meta-analysis and future direction. Information Development, 41(3), 859–874. https://doi.org/10.1177/02666669241257206
Bai, X., & Lin, Y. (2025). Exploring the determinants of AIGC usage intention based on the extended AIDUA model: A multi-group structural equation modeling analysis. Frontiers in Psychology, 16, 1589318. https://doi.org/10.3389/fpsyg.2025.1589318
Cappella, E., Frazier, S. L., Atkins, M. S., Schoenwald, S. K., & Glisson, C. (2008). Enhancing schools’ capacity to support children in poverty: An ecological model of school-based mental health services. Administration and Policy in Mental Health and Mental Health Services Research, 35(5), 395–409. https://doi.org/10.1007/s10488-008-0182-y
Chan, C. K. Y., & Zhou, W. (2023). An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI. Smart Learning Environments, 10(1), 1–18. https://doi.org/10.1186/s40561-023-00284-4
Cho, C. H., & Costa, E. (2024). Sustainability accounting education: Challenges and outlook. International Journal of Sustainability in Higher Education, 25(7), 1412–1425. https://doi.org/10.1108/ijshe-02-2024-0152
Dikilita?, K., & Noguera, I. (2023). Conceptual framework for flexible learning design: The context of flipped classroom. US-China Education Review, 13(2), 85–96. https://doi.org/10.31265/usps.267
Fang, X. (2025). Research of ethical adoption of college students’ learning applications of generative artificial intelligence. Journal of Computer Assisted Learning, 41(6), 1–12. https://doi.org/10.1111/jcal.70146
Hoven, D., & Palalas, A. (2011). (Re)Conceptualizing design approaches for mobile language learning. CALICO Journal, 28(3), 699–720. https://doi.org/10.11139/cj.28.3.699-720
Huang, B. (2025). Exploring the acceptance of large language models as an integrated reading tool: A UTAUT-based analysis. SAGE Open, 15(4), 1–15. https://doi.org/10.1177/21582440251392110
Huang, Y., Yu, L., & Chen, G. (2025). The impact of learning motivation on academic performance among low-income college students: The mediating roles of learning strategies and mental health. Frontiers in Psychology, 16, 1639375. https://doi.org/10.3389/fpsyg.2025.1639375
Lavidas, K., Voulgari, I., Papadakis, S., Athanassopoulos, S., Anastasiou, A., Filippidi, A., Komis, V., & Karacapilidis, N. (2024). Determinants of humanities and social sciences students’ intentions to use Artificial Intelligence applications for academic purposes. Information, 15(6), 314. https://doi.org/10.3390/info15060314
Lee, A. T., Ramasamy, R. K., & Subbarao, A. (2025). Understanding psychosocial barriers to healthcare technology adoption: A review of TAM Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology and UTAUT frameworks. Healthcare, 13(3), 250. https://doi.org/10.3390/healthcare13030250
Lombardi, D., Shipley, T. F., Bailey, J. M., Bretones, P. S., Prather, E. E., Ballen, C. J., Knight, J., Smith, M. K., Stowe, R. L., Cooper, M. M., Prince, M. J., Atit, K., Uttal, D. H., LaDue, N., McNeal, P., Ryker, K., John, K. S., Kraft, K. J. van der H., & Docktor, J. L. (2021). The curious construct of active learning. Psychological Science in the Public Interest, 22(1), 8–43. https://doi.org/10.1177/1529100620973974
Mili?evi?, N., Kalaš, B., Djoki?, N., Mal?i?, B., & Djokic, I. (2024). Students’ intention toward Artificial Intelligence in the context of digital transformation. Sustainability, 16(9), 3554. https://doi.org/10.3390/su16093554
Pan, H.-L. W. (2023). Advancing student learning power by operating classrooms as learning communities: Mediated effects of engagement activities and social relations. Sustainability, 15(3), 2461. https://doi.org/10.3390/su15032461
Pang, Y. (2022). The role of web-based flipped learning in EFL learners’ critical thinking and learner engagement. Frontiers in Psychology, 13, 1008257. https://doi.org/10.3389/fpsyg.2022.1008257
Rodríguez, M. del P. G., Vélez, S. C., García, M. D., & Carmona, J. (2023). Learning environments in compulsory secondary education (ESO): Validation of the physical, learning, teaching and motivational scales. Learning Environments Research, 27(1), 53–75. https://doi.org/10.1007/s10984-023-09464-y
Sattar, T., Ullah, M. I., & Ahmad, B. (2022). The role of stakeholders participation, goal directness and learning context in determining student academic performance: Student engagement as a mediator. Frontiers in Psychology, 13, 875174. https://doi.org/10.3389/fpsyg.2022.875174
Taufiq-Hail, G. A., Yusof, S. A. M., Rashid, A., El-Shekeil, I., & Lutfi, A. (2024). Exploring factors influencing Gen Z’s acceptance and adoption of AI and cloud-based applications and tools in academic attainment. Emerging Science Journal, 8(3), 815–836. https://doi.org/10.28991/esj-2024-08-03-02
Wang, Y., Wang, X., Ding, W., Chen, X., Zhu, L., Tan, M., Tao, M., & Ma, S. (2025). Metacognitive ability as a mediator between learning environment and Artificial Intelligence literacy among Chinese nursing students: A cross-sectional study. BMC Nursing, 24(1), 102. https://doi.org/10.21203/rs.3.rs-6654681/v1
Wu, R., Gao, L., Li, J., Huang, Q., & Pan, Y. (2024). Key factors influencing design learners’ behavioral intention in human-AI collaboration within the educational metaverse. Sustainability, 16(22), 9942. https://doi.org/10.3390/su16229942
Wu, W., Zhang, B., Li, S., & Liu, H. (2022). Exploring factors of the willingness to accept AI-assisted learning environments: An empirical investigation based on the UTAUT model and perceived risk theory. Frontiers in Psychology, 13, 870777. https://doi.org/10.3389/fpsyg.2022.870777
Zhang, C., Wang, F., Kang, Z., Hong, Y., Arbing, R., Chen, W., & Huang, F. F. (2025). Effect of symptom burden on demoralization in Chinese lung cancer patients: The mediating roles of family function, resilience, and coping behaviors. Psycho-Oncology, 34(2), e70102. https://doi.org/10.1002/pon.70102
Zhou, X., Zhang, J., & Chan, C. (2024). Unveiling students’ experiences and perceptions of Artificial Intelligence usage in higher education. Journal of University Teaching and Learning Practice, 21(6), 1–20. https://doi.org/10.53761/xzjprb23
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Yuyun Yumiarty
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
