GPT Chat: Useful or Not in Supporting Learning in Higher Education
Abstract
Background. Chat GPT is a natural language model developed by OpenAI, based on the GPT (Generative Pretrained Transformer) architecture. It is renowned for its ability to generate text that closely resembles human writing, including in chat and conversational interactions. In the growing digital era, artificial intelligence technology is increasingly playing an important role in various fields, including education.
Purpose. This study aims to identify the benefits of using Chat GPT in learning in higher education and how its use can improve the quality of learning, accelerate the assessment process, increase student engagement, improve teaching efficiency, and facilitate student understanding.
Method. The research method used is quantitative by using google form which will produce data in the form of numbers. By using google form, a questionnaire will be made and distributed to students in higher education.
Results. The results show that the use of Chat GPT has significant benefits in learning in higher education. The use of Chat GPT can improve the quality of learning, accelerate the assessment process, increase student engagement, and improve teaching efficiency.
Conclusion. The conclusion from this study is that the use of Chat GPT in a college setting can be beneficial in supporting learning in an innovative and effective way. However, the limitation of this study is that the researcher was only able to conduct a study of a few students in higher education. Therefore, the researcher hopes that future research can be conducted with a wider scope. The researcher also recommends that future research can be a reference material in conducting research related to the utilisation of Chat GPT in supporting learning in higher education.
Full text article
References
Abbass, H. (2021). Editorial: What is Artificial Intelligence? IEEE Transactions on Artificial Intelligence, 2(2), 94–95. https://doi.org/10.1109/TAI.2021.3096243
Al-Fraihat, D., Joy, M., Masa’deh, R., & 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
Bailey, M. (2018). On misogynoir: Citation, erasure, and plagiarism. Feminist Media Studies, 18(4), 762–768. https://doi.org/10.1080/14680777.2018.1447395
Biber, D., Gray, B., Staples, S., & Egbert, J. (2020). Investigating grammatical complexity in L2 English writing research: Linguistic description versus predictive measurement. Journal of English for Academic Purposes, 46, 100869. https://doi.org/10.1016/j.jeap.2020.100869
Blank, J., & Deb, K. (2020). Pymoo: Multi-Objective Optimization in Python. IEEE Access, 8, 89497–89509. https://doi.org/10.1109/ACCESS.2020.2990567
Carless, D., & Boud, D. (2018). The development of student feedback literacy: Enabling uptake of feedback. Assessment & Evaluation in Higher Education, 43(8), 1315–1325. https://doi.org/10.1080/02602938.2018.1463354
Chumachenko, D., Meniailov, I., Bazilevych, K., Kuznetsova, Y., & Chumachenko, T. (2019). Development of an intelligent agent-based model of the epidemic process of syphilis. 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT), 42–45. https://doi.org/10.1109/STC-CSIT.2019.8929749
Correll, N., Bekris, K. E., Berenson, D., Brock, O., Causo, A., Hauser, K., Okada, K., Rodriguez, A., Romano, J. M., & Wurman, P. R. (2018). Analysis and Observations From the First Amazon Picking Challenge. IEEE Transactions on Automation Science and Engineering, 15(1), 172–188. https://doi.org/10.1109/TASE.2016.2600527
DeGregorio, G. A., Singer, J., Cahill, K. N., & Laidlaw, T. (2019). A 1-Day, 90-Minute Aspirin Challenge and Desensitization Protocol in Aspirin-Exacerbated Respiratory Disease. The Journal of Allergy and Clinical Immunology: In Practice, 7(4), 1174–1180. https://doi.org/10.1016/j.jaip.2018.10.032
Donnelly, J. P., Chen, S. C., Kauffman, C. A., Steinbach, W. J., Baddley, J. W., Verweij, P. E., Clancy, C. J., Wingard, J. R., Lockhart, S. R., Groll, A. H., Sorrell, T. C., Bassetti, M., Akan, H., Alexander, B. D., Andes, D., Azoulay, E., Bialek, R., Bradsher, R. W., Bretagne, S., … Pappas, P. G. (2020). Revision and Update of the Consensus Definitions of Invasive Fungal Disease From the European Organization for Research and Treatment of Cancer and the Mycoses Study Group Education and Research Consortium. Clinical Infectious Diseases, 71(6), 1367–1376. https://doi.org/10.1093/cid/ciz1008
Ebisuya, M., & Briscoe, J. (2018). What does time mean in development? Development, 145(12), dev164368. khttps://doi.org/10.1242/dev.164368
Enders, M., Havemann, F., Ruland, F., Bernard?Verdier, M., Catford, J. A., Gómez?Aparicio, L., Haider, S., Heger, T., Kueffer, C., Kühn, I., Meyerson, L. A., Musseau, C., Novoa, A., Ricciardi, A., Sagouis, A., Schittko, C., Strayer, D. L., Vilà, M., Essl, F., … Jeschke, J. M. (2020). A conceptual map of invasion biology: Integrating hypotheses into a consensus network. Global Ecology and Biogeography, 29(6), 978–991. https://doi.org/10.1111/geb.13082
Gharby, S., Ravi, H. K., Guillaume, D., Abert Vian, M., Chemat, F., & Charrouf, Z. (2020). 2-methyloxolane as alternative solvent for lipid extraction and its effect on the cactus ( Opuntia ficus-indica L.) seed oil fractions. OCL, 27, 27. https://doi.org/10.1051/ocl/2020021
Guo, C., Lu, Y., Dou, Y., & Wang, F.-Y. (2023). Can ChatGPT Boost Artistic Creation: The Need of Imaginative Intelligence for Parallel Art. IEEE/CAA Journal of Automatica Sinica, 10(4), 835–838. https://doi.org/10.1109/JAS.2023.123555
Himmi, N., & Hatwin, L. B. A. (2018). PENGEMBANGAN MODUL SISTEM PERTIDAKSAMAAN DUA VARIABEL BERBASIS GEOGEBRA TERHADAP KEMAMPUAN VISUAL THINKING MATEMATIS SISWA KELAS X. PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika, 7(1). https://doi.org/10.33373/pythagoras.v7i1.1208
?lhan, E., & K?ymaz, ?. O. (2020). A generalization of truncated M-fractional derivative and applications to fractional differential equations. Applied Mathematics and Nonlinear Sciences, 5(1), 171–188. https://doi.org/10.2478/amns.2020.1.00016
Kingsbury, L., & Hong, W. (2020). A Multi-Brain Framework for Social Interaction. Trends in Neurosciences, 43(9), 651–666. https://doi.org/10.1016/j.tins.2020.06.008
Nagoudi, E. M. B., Khorsi, A., Cherroun, H., & Schwab, D. (2018). 2L-APD: A Two-Level Plagiarism Detection System for Arabic Documents. Cybernetics and Information Technologies, 18(1), 124–138. https://doi.org/10.2478/cait-2018-0011
Nayak, P. K., Yang, L., Brehm, W., & Adelhelm, P. (2018). From Lithium-Ion to Sodium-Ion Batteries: Advantages, Challenges, and Surprises. Angewandte Chemie International Edition, 57(1), 102–120. https://doi.org/10.1002/anie.201703772
Neese, F. (2018). Software update: The ORCA program system, version 4.0. WIREs Computational Molecular Science, 8(1). https://doi.org/10.1002/wcms.1327
Nur Suryawan, C. seto, & Premitasari, M. (2021). Optimalisasi Metode LPC-16 dan HMM-Forward Pada Sistem Asisten Virtual: Optimalissasi Metode Linear Predictive Coding - 16 dan Hidden Markov Model Forward pada Sistem Asisten Virtual. Jurnal Ilmiah Teknologi Infomasi Terapan, 7(2), 101–107. https://doi.org/10.33197/jitter.vol7.iss2.2021.531
Qiu, T., Chen, N., Li, K., Atiquzzaman, M., & Zhao, W. (2018). How Can Heterogeneous Internet of Things Build Our Future: A Survey. IEEE Communications Surveys & Tutorials, 20(3), 2011–2027. https://doi.org/10.1109/COMST.2018.2803740
Reichert, F., & Torney-Purta, J. (2019). A cross-national comparison of teachers’ beliefs about the aims of civic education in 12 countries: A person-centered analysis. Teaching and Teacher Education, 77, 112–125. https://doi.org/10.1016/j.tate.2018.09.005
Saniuk, S., Grabowska, S., & Gajdzik, B. (2020). Personalization of Products in the Industry 4.0 Concept and Its Impact on Achieving a Higher Level of Sustainable Consumption. Energies, 13(22), 5895. https://doi.org/10.3390/en13225895
Sanmamed, M. F., & Chen, L. (2018). A Paradigm Shift in Cancer Immunotherapy: From Enhancement to Normalization. Cell, 175(2), 313–326. https://doi.org/10.1016/j.cell.2018.09.035
Sarid, A. (2018). A theory of education. Cambridge Journal of Education, 48(4), 479–494. https://doi.org/10.1080/0305764X.2017.1356267
Sono, T., Satake, S., Kanda, T., & Imai, M. (2019). Walking partner robot chatting about scenery. Advanced Robotics, 33(15–16), 742–755. https://doi.org/10.1080/01691864.2019.1610062
Vimmerstedt, L. J., Akar, S., Augustine, C. R., Beiter, P. C., Cole, W. J., Feldman, D. J., Kurup, P., Lantz, E. J., Margolis, R. M., Stehly, T. J., Turchi, C. S., & Oladosu, D. (2019). 2019 Annual Technology Baseline (NREL/PR-6A20-74273, 1566062; hlm. NREL/PR-6A20-74273, 1566062). https://doi.org/10.2172/1566062
Wang, Z., Wang, S., Wang, X., & Niu, Y. (2018). Cross-correlation function based two-sensor auditory localization unit for chat robots. 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 1122–1127. https://doi.org/10.1109/ICIEA.2018.8397879
Weinstein, B. G. (2018). A computer vision for animal ecology. Journal of Animal Ecology, 87(3), 533–545. https://doi.org/10.1111/1365-2656.12780
Yang, D., Jang, I., Choi, J., Kim, M.-S., Lee, A. J., Kim, H., Eom, J., Kim, D., Jung, I., & Lee, B. (2018). 3DIV: A 3D-genome Interaction Viewer and database. Nucleic Acids Research, 46(D1), D52–D57. https://doi.org/10.1093/nar/gkx1017
Zhu, N., Zhu, C., & Emrouznejad, A. (2021). A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies. Journal of Management Science and Engineering, 6(4), 435–448. https://doi.org/10.1016/j.jmse.2020.10.001
Zou, X. (2020). A Survey on Application of Knowledge Graph. Journal of Physics: Conference Series, 1487(1), 012016. https://doi.org/10.1088/1742-6596/1487/1/012016
Authors
Copyright (c) 2024 Ratri Candrasari, Juan Mukulua, Yessicka Noviasmy, Korlina Makulua, Siminto

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.