ChatGPT in Action: Unraveling its Impact on Student Motivation in English Language Learning
(1) Akademi Perikanan Kamasan Biak-Papua
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Akgun, S., & Greenhow, C. (2021). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. Al Ethnics, 2, 431–440. https://doi.org/https://doi.org/10.1007/s43681-021-00096-7
Baker, R. S. (2016). Stupid tutoring systems, and intelligent humans. International Journal of Artificial Intelligence in Education, 26(2), 600–614.
Biswas, S. (2023). Role of ChatGPT in education. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4369981
Chamorro-Atalaya, O., Durán-Herrera, V., Suarez-Bazalar, R., Nieves-
Barreto, C., Tarazona-Padilla, J., Rojas-Carbajal, M., Cruz-Telada, Y., Caller-Luna, J., Alarcón-Anco, R., & Arévalo-Tuesta, J. A. (2023). Inclusion of Metaverses in the Development of the Flipped Classroom in the University environment: Bibliometric Analysis of Indexed Scientific Production in SCOPUS. International Journal of Learning, Teaching and Educational Research, 22(10). https://doi.org/https://doi.org/10.26803/ijlter.22.10.14
Chang, C., Hwang, G., & Gau, M. (2022). Promoting students’ learning achievement and self-efficacy: A mobile chatbot approach for nursing training. British Journal of Educational Technology, 53(1), 171–188. https://doi.org/https://doi.org/10.1111/bjet.13158
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. https://doi.org/https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/https://doi.org/10.1207/S15327965PLI1104_01
Donnermann, M., Lein, M., Messingschlager, T., Riedmann, A., Schaper, P., Steinhaeusser, S., & Lugrin, B. (2021). Social robots and gamification for technology supported learning: An empirical study on engagement and motivation. Computers in Human Behavior, 121, 106792. https://doi.org/https://doi.org/10.1016/j.chb.2021.106792
Firat, M. (2023). How ChatGPT Can Transform Autodidactic Experiences and Open Education? https://doi.org/DOI:10.31219/osf.io/9ge8m
Ford, M. E. (1992). Motivating humans: Goals, emotions, and personal agency beliefs. Newbury Park. SAGE Publications.
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
Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023). Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, 104684. https://doi.org/https://doi.org/10.1016/j.compedu.2022.104684
Jayaputri, H. E. (2022). Applying the English Simple Code to Improve Indonesian Students ’ Communicative Speaking Ability and Their Motivation. 7(1), 47–68.
Labadze, L., Grigolia, M., & Machaidze, L. (2023). No Title. International Journal of Educational Technology in Higher Education, 20–56. https://doi.org/https://doi.org/10.1186/s41239-023-00426-1
Li, H., Gobert, J., & Dickler, R. (2019). Evaluating the Transfer of Scaffolded Inquiry: What Sticks and Does it last? In International Conference on Artificial Intelligence in Education, 163–168.
Linn, M. C., Gerard, L., Ryoo, K., McElhaney, K., Liu, O. L., & Rafferty, A. N. (2014). Computer-guided inquiry to improve science learning. Science, 344(6180), 155–156.
Linn, M. C., Donnelly-Hermosillo, D., & Gerard, L. (2023). Synergies Between Learning Technologies and Learning Sciences: Promoting Equitable Secondary School Teaching. In In Handbook of research on science education (First Edit, pp. 447–498).
Meuter, M. L., Bitner, M. J., Ostrom, A. L., & Brown, S. W. (2005). Choosing Among Alternative Service Delivery Modes: An Investigation of Customer Trial of Self-Service Technologies. Journal of Marketing, 69(2), 61–83. https://doi.org/https://doi.org/10.1509/jmkg.69.2.61.60759
Pellegrino, J. W., & Quellmalz, A. . (2010). Perspectives on the integration of technology and assessment. Journal of Research on Technology in Education, 43(2), 119–134.
Rai, A., Constatinides, P., & Sarker, S. (2019). Editor’s comments: Next Generation Digital Platforms: Toward Human Al-Hybirds. MIS Quartely, 43(1).
Subiyantoro, S., Degeng, I. N. S., Kuswandi, D., & Ulfa, S. (2023). Eksplorasi Dampak Chatbot Bertenaga AI (ChatGPT) Pada Pendidikan: Studi Kualitatif Tentang Manfaat dan Kerugian. Jurnal Pekomnas, 8(2), 157–168. https://doi.org/doi: 10.56873/jpkm.v8i2.5205
Teo, T. S. H., Lim, V. K. G., & Lai, R. Y. C. (1999). Intrinsic and extrinsic motivation in Internet usage. Omega, 27(1), 25–37. https://doi.org/https://doi.org/10.1016/S0305-0483(98)00028-0
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15. https://doi.org/10.1186/s40561-023-00237-x
Vallerand, R. J., Pelletier, L. G., Blais, M. R., Briere, N. M., Senecal, C., & Vallieres, E. F. (1992). The Academic Motivation Scale: A Measure of Intrinsic, Extrinsic, and Amotivation in Education. Educational and Psychological Measurement, 52(4), 1003–1017. https://doi.org/10.1177/0013164492052004025
Van Lehn, K., Banerjee, C., Milner, F., & Wetzel, J. (2020). Teaching algebraic model construction: A tutoring system, lessons learned and an evaluation. International Journal of Artificial Intelligence in Education, 30(3), 459–480. https://doi.org/https://doi.org/10.1007/s40593-020-00205-3
Wang, Y.-M., Wei, C.-L., Lin, H.-H., Wang, S.-C., & Wang, Y.-S. (2022). What drives students’ AI learning behavior: a perspective of AI anxiety. Interactive Learning Environments, 1–17. https://doi.org/10.1080/10494820.2022.2153147
Wang, Y., Bundgaard-Nielsen, R. L., Baker, B. J., & Maxwell, O. (2023). Same Vowels but Different Contrasts: Mandarin Listeners’ Perception of English /ei/-/i?/ in Unfamiliar Phonotactic Contexts. Journal of Phonetics, 97(101221). https://doi.org/10.1016/j.wocn.2023.101221
Yilmaz, R., Yilmaz, K., & Gizem, F. (2023). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, 100147. https://doi.org/https://doi.org/10.1016/j.caeai.2023.100147
Zhai, X. (2021). Advancing automatic guidance in virtual science inquiry: From ease of use to personalization. Educational Technology Research and Development, 69(1255–258).
Zhai, X., Haudek, K. ., Shi, L., Nehm, R., & Urban-Lurain, M. (2020). From substitution to redefinition: A framework of machine learning-based science assessment. Journal of Research in Science Teaching, 57(9), 1430–1459.
Zheng, L., Niu, J., Zhong, L., & Gyasi, J. F. (2023). The effectiveness of artificial intelligence on learning achievement and learning perception: A meta-analysis. Interactive Learning Environments, 31(9), 5650–5664. https://doi.org/10.1080/10494820.2021.2015693
Zoltan Dornyei. (2020). Innovations and Challenges in Language Learning Motivation. Routledge.
DOI: http://dx.doi.org/10.30998/scope.v8i2.22375
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