Students' Perceptions on Artificial Intelligence as Academic Support Among English Education Students at Batanghari University: A Case Study of ChatGPT
Abstract
This research aims to analyze students' perceptions of AI-based learning among English Education students at Batanghari University. In an era where digital tools play an increasing role in education, understanding how students perceive artificial intelligence (AI) in the learning process becomes essential. This study adopts five key constructs to measure perceptions—engagement, interaction, satisfaction, academic performance, and behavioral intention—based on a validated framework by Khairuddin et al. (2024).The study employs a quantitative approach through a survey of 37 respondents from even-numbered semesters (2, 4, and 6). The questionnaire was distributed online and the data were analyzed using SPSS version 25. Statistical analysis included validity and reliability tests, descriptive statistics, normality and homogeneity tests, and hypothesis testing through One-Way ANOVA. The results revealed that students generally held a moderately positive perception toward AI-based learning. Among the five constructs, interaction received the highest average score, while behavioral intention had the lowest. Furthermore, the ANOVA test indicated a significant difference in students' perceptions across semester levels, confirming that academic experience influences how students view AI in learning. These findings suggest the importance of tailoring AI integration strategies based on students' academic maturity to maximize the effectiveness of AI tools in education.
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Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Al-Fraihat, D., Joy, M., & Sinclair, J. (2020). Evaluating E-Learning Systems Success: An Empirical Study. Computers in Human Behavior, 102, 67-86. https://doi.org/10.1016/j.chb.2019.08.004
Ambrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., & Norman, M. K. (2010). How learning works. John Wiley & Sons.
Anderson, K. (2015). Nursing management for the 21st Century. In S.S. Mason & M.L Sawyer (Eds.), High-impact nursing management (pp. 42-58). State University of New York Press.
Anderson, T. (2003). Modes of Interaction in Distance Education: Recent Developments and Research Questions. In M. G. Moore (Ed.), Handbook of Distance Education (pp. 129-144). Routledge.
Bloom, B. S. (1976). Human characteristics and school learning. McGraw-Hill.-
Chadwick, A. (2013). The hybrid media system: Politics and power. Oxford University Press.
Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510
Cohen, L., Manion, L., & Morrison, K. (2018). Research Methods in Education (8th ed.). Routledge.
Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Pearson.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
Dede, C. (2014). The role of digital technologies in deeper learning. Students at the Center: Deeper Learning Research Series. Jobs for the Future.
Dixson, M. D. (2015). Measuring student engagement in the online course: The Online Student Engagement scale (OSE). Online Learning, 19(4), 143-157.
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School Engagement: Potential of the Concept, State of the Evidence. Review of Educational Research, 74(1), 59-109. https://doi.org/10.3102/00346543074001059
Fryer, L. K., Coniam, D., Carpenter, R., & Lăpușneanu, D. (2020). Bots for language learning now: Current and future directions. Language Learning & Technology, 24(3), 1-13.
Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the behavioral sciences 10th. Statistic for The Behavioral Science.
Gunawan, W., Prasetyo, Y. T., & others. (2022). Indonesian students’ motivation in online learning during COVID-19 pandemic: A survey study. Education Sciences, 12(3), 111. https://doi.org/10.3390/educsci12030111
Heffernan, N. T., & Heffernan, C. L. (2014). The ASSISTments ecosystem: Building a platform that brings scientists and teachers together for principled improvement of educational resources. In K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (2nd ed., pp. 220-241). Cambridge University Press.
Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90, 36-53. https://doi.org/10.1016/j.compedu.2015.09.005
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Khairuddin, Z., Shahabani, N. S., Ahmad, S. N., Ahmad, A. R., & Zamri, N. A. (2024). Students’ Perceptions on The Artificial Intelligence (AI) Tools as Academic Support. Malaysian Journal of Social Sciences and Humanities (MJSSH), 9(11), e003087. https://doi.org/10.47405/mjssh.v9i11.3087 (Catatan: Nomor halaman diulang pada sitasi asli, telah disesuaikan)
Kousta, S.-T., Vigliocco, G., Vinson, D. P., Andrews, M., & Del Campo, E. (2014). The representation of abstract words: Why emotion matters. Journal of Experimental Psychology: General, 143(1), 110–122. https://doi.org/10.1037/a0033936
Kuh, G. D., Kinzie, J., Buckley, J. A., Bridges, B. K., & Hayek, J. C. (2014). Ensuring quality & taking high-impact practices to scale. Association of American Colleges and Universities.
Leavy, P. (2017). Research design: Quantitative, qualitative, mixed methods, arts-based, and community-based participatory research approaches. Guilford Press.
Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506-516.-
Luckin, R. (2017). Towards artificial intelligence-based assessment systems. Nature Human Behaviour, 1(3), 0028.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An argument for AI in Education. Pearson.
Martin, F., & Bolliger, D. U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning, 22(1), 205-222.
Moore, M. G. (1989). Three types of interaction. The American Journal of Distance Education, 3(2), 1-7.
Neuman, W. L. (2014). Social research methods: Qualitative and quantitative approaches (7th ed.). Pearson.
Norman, D. A. (2013). The design of everyday things: Revised and expanded edition. Basic Books.
Pallant, J. (2013). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (5th ed.). Allen & Unwin.
Pane, J. F., Steiner, E. D., Baird, M. D., & Hamilton, L. S. (2014). Continued Progress: Promising Evidence on Personalized Learning. RAND Corporation.
Plass, J. L., Homer, B. D., & Kinzer, C. K. (2015). Foundations of Game-Based Learning. Educational Psychologist, 50(4), 258-283.
Prinz, J. J. (2012). The conscious brain: How attention engenders experience. Oxford University Press.
Rasheed, R. A., Kamsin, A., & Abdullah, N. A. (2020). Challenges in the online component of blended learning: A systematic review. Computers & Education, 144, 103701.
Robbins, S. P., & Judge, T. A. (2010). Organizational Behavior. Pearson.
Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A., & Goldberg, L. R. (2007). The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspectives on Psychological Science, 2(4), 313-345.
Schwarz, N. (2012). Feelings-as-information theory. In P. A. M. Van Lange, A. W. Kruglanski, & E. T. Higgins (Eds.), Handbook of theories of social psychology (Vol. 1, pp. 289-308). Sage Publications.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill-building approach (7th ed.). Wiley.
Setyosari, P. (2010). Metode penelitian pendidikan dan pengembangan. Kencana.-
Sharples, M. (2016). Innovating pedagogy 2016: Open University innovation report 5. The Open University.
Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, 46(5), 30-32.
Statistics Indonesia (Badan Pusat Statistik). (2022). Penggunaan dan Pemanfaatan Teknologi Informasi dan Komunikasi pada Usaha/Perusahaan (Hasil Survei Penggunaan dan Pemanfaatan TIK pada Usaha/Perusahaan (SPPTIK) 2022). Badan Pusat Statistik.
Sweller, J. (2011). Cognitive load theory. In J. Mestre & B. Ross (Eds.), The psychology of learning and motivation: Cognition in education (Vol. 55, pp. 37-76). Elsevier Academic Press.
Teo, T., & Zhou, M. (2014). Explaining the intention to use technology among university students: A structural equation modeling approach. Journal of Computing in Higher Education, 26(2), 124-142.-
Unumeri, G. O. (2009). Perception and conflict. National Open University of Nigeria.
Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273-315.
Weerasinghe, I. M. S., & Fernando, R. L. S. (2017). Students’ satisfaction in higher education. American Journal of Educational Research, 5(5), 533-539.
Yu, Z. (2021). The effects of gender, educational level, and personality on online learning outcomes during the COVID-19 pandemic. International Journal of Educational Technology in Higher Education, 18, 14. https://doi.org/10.1186/s41239-021-00252-3
DOI: http://dx.doi.org/10.33087/jelt.v9i2.204
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