SWOT Analysis of ChatGPT: Implications for Educational Practice and Research

Mesiono Mesiono; Nazli Fahada; Irwansyah Irwansyah; Diana Diana; Andi Suhendra Siregar

doi:10.31851/jmksp.v9i1.14137

Authors

Mesiono Mesiono Universitas Islam Negeri (UIN) Sumatera Utara
Nazli Fahada Universitas Prima Indonesia
Irwansyah Irwansyah Universitas Al Washliyah Medan
Diana Diana Universitas Islam Negeri (UIN) Sumatera Utara
Andi Suhendra Siregar Pesantren TAHFIZ Qur'an dan Hadits AL FAIZ

DOI:

https://doi.org/10.31851/jmksp.v9i1.14137

Keywords:

Analysis, ChatGPT, Education Technology

Abstract

Technology plays a significant role in our lives today. OpenAI, a company at the forefront of artificial intelligence since 2015, has taken remarkable steps to make artificial intelligence more accessible and beneficial for everyone. One significant achievement in its journey is the development of Chat Generative Pre-trained Transformers (ChatGPT). We used the SWOT analysis framework to outline ChatGPT’s strengths and weaknesses and to discuss its opportunities for and threats to education. The strengths include using a sophisticated natural language model to generate plausible answers, self-improving capability, and providing personalised and real-time responses. As such, ChatGPT can increase access to information, facilitate personalised and complex learning, and decrease teaching workload, thereby making key processes and tasks more efficient. The weaknesses are a lack of deep understanding, difficulty in evaluating the quality of responses, a risk of bias and discrimination, and a lack of higher-order thinking skills. Threats to education include a lack of understanding of the context, threatening academic integrity, perpetuating discrimination in education, democratising plagiarism, and declining high-order cognitive skills.

References

Ahsan, K., Akbar, S., & Kam, B. (2022). Contract cheating in higher education: a systematic literature review and future research agenda. Assessment and Evaluation in Higher Education, 47(4), 523–539. https://doi.org/10.1080/02602938.2021.1931660

Bakti, I. K., Zulkarnain, Yarun, A., Rusdi, Syaifudin, M., & Syafaq, H. (2023). The Role of Artificial Intelligence in Education: A Systematic Literature Review. Jurnal Iqra’ : Kajian Ilmu Pendidikan, 8(2), 182–197. https://doi.org/10.25217/JI.V8I2.3194

Benzaghta, M. A., Elwalda, A., Mousa, M. M., Erkan, I., & Rahman, M. (2021). SWOT analysis applications: An integrative literature review. Journal of Global Business Insights, 6(1), 55–73. https://doi.org/10.5038/2640-6489.6.1.1148

Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186. https://doi.org/10.1126/SCIENCE.AAL4230

Carr, S. E., Canny, B. J., Wearn, A., Carmody, D., Celenza, A., Diug, B., Leech, M., & Wilkinson, T. J. (2022). Twelve tips for medical students experiencing an interruption in their academic progress. Medical Teacher, 44(10), 1081–1086. https://doi.org/10.1080/0142159X.2021.1921134

Chan, K. S., & Zary, N. (2019). Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review. JMIR Medical Education, 5(1), e13930. https://doi.org/10.2196/13930

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

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International. https://doi.org/10.1080/14703297.2023.2190148

Dehouche, N. (2021). Plagiarism in the age of massive Generative Pre-trained Transformers (GPT-3). Ethics in Science and Environmental Politics, 21, 17–23. https://doi.org/10.3354/ESEP00195

Dogan, M. E., Goru Dogan, T., & Bozkurt, A. (2023). The use of artificial intelligence (AI) in online learning and distance education processes: A systematic review of empirical studies. Applied Sciences, 13(5), 3056. https://doi.org/10.3390/app13053056

Evawati, D., & Susilowati. (2023). Optimization of Nutrition Science Learning through Educational Technology at PGRI Adi Buana University Surabaya. Jurnal Iqra’ : Kajian Ilmu Pendidikan, 8(1), 385–401. https://doi.org/10.25217/JI.V8I1.2734

Elbanna, S., & Armstrong, L. (2023). Exploring the integration of ChatGPT in education: adapting for the future. Management & Sustainability: An Arab Review, ahead-of-print(ahead-of-print). https://doi.org/10.1108/MSAR-03-2023-0016

Elkins, K., & Chun, J. (2020). Can GPT-3 Pass a Writer’s Turing Test? Journal of Cultural Analytics, 5(2). https://doi.org/10.22148/001C.17212

Floridi, L., & Chiriatti, M. (2020). GPT-3: Its Nature, Scope, Limits, and Consequences. Minds and Machines, 30(4), 681–694. https://doi.org/10.1007/S11023-020-09548-1

Gao, J. (2021). Exploring the Feedback Quality of an Automated Writing Evaluation System Pigai. International Journal of Emerging Technologies in Learning, 16(11), 322–330. https://doi.org/10.3991/IJET.V16I11.19657

Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature 2015 521:7553, 521(7553), 452–459. https://doi.org/10.1038/nature14541

Hastiana, Y., Hidayat, S., & Rohmah, S. (2023). The Implementation of the Quizizz Platform as an Evaluation Medium for Teachers and Students in High School Education in Muara Enim District. JMKSP (Jurnal Manajemen, Kepemimpinan, Dan Supervisi Pendidikan), 8(2), 1278–1287. https://doi.org/10.31851/JMKSP.V8I2.13017

Halaweh, M. (2023). ChatGPT in education: Strategies for responsible implementation. Contemporary Educational Technology, 15(2), 2. https://doi.org/10.30935/cedtech/13036

Imaduddin, M., & Astuti, A. P. (2022). Strengthening Chemistry Teachers’ Technological Pedagogical Content Knowledge through the Introduction of Augmented Reality and Learning Management Systems. Bulletin of Community Engagement, 2(1), 21–31. https://doi.org/10.51278/BCE.V2I1.299

Jafar, J. (2021). The Influence of Using Internet Media on Learning Achievement in Islamic Religious Education in Class XI High School Students. Jurnal Al-Qiyam, 2(1), 188–193. https://doi.org/10.33648/ALQIYAM.V2I1.211

Katznelson, G., & Gerke, S. (2021). The need for health AI ethics in medical school education. Advances in Health Sciences Education, 26(4), 1447–1458. https://doi.org/10.1007/S10459-021-10040-3

Klimova, B., Pikhart, M., & Kacetl, J. (2023). Ethical issues of the use of AI-driven mobile apps for education. Frontiers in Public Health, 10. https://doi.org/10.3389/FPUBH.2022.1118116

Komalasari, Y., Abdullah, A., Yiharodiyah, L., & Amanda, M. (2023). FITERN: Firefighting Tactic and Technique Application as A Web-Based Learning Media Integrated with A Robotic Simulator. JMKSP (Jurnal Manajemen, Kepemimpinan, Dan Supervisi Pendidikan), 8(1), 631–641. https://doi.org/10.31851/JMKSP.V8I1.13114

Komalasari, Y., Abdullah, A., Yiharodiyah, L., Sutiyo, S., Parjan, P., & Amanda, M. (2023). Radio Frequency Identification (RFID) Technology Devices in Library Services: Improving Education Services. JMKSP (Jurnal Manajemen, Kepemimpinan, Dan Supervisi Pendidikan), 8(2), 1288–1297. https://doi.org/10.31851/JMKSP.V8I2.13136

Küçük, S., Kapakin, S., & Göktaş, Y. (2016). Learning anatomy via mobile augmented reality: Effects on achievement and cognitive load. Anatomical Sciences Education, 9(5), 411–421. https://doi.org/10.1002/ASE.1603

Lee, H. (2023). The rise of ChatGPT: Exploring its potential in medical education. Anatomical Sciences Education. https://doi.org/10.1002/ASE.2270

Lee, J., Wu, A. S., Li, D., & Kulasegaram, K. (mahan). (2021). Artificial Intelligence in Undergraduate Medical Education: A Scoping Review. Academic Medicine, 96(11), S62–S70. https://doi.org/10.1097/ACM.0000000000004291

Li, C., & Xing, W. (2021). Natural Language Generation Using Deep Learning to Support MOOC Learners. International Journal of Artificial Intelligence in Education, 31(2), 186–214. https://doi.org/10.1007/S40593-020-00235-X

Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain Intelligence: Go beyond Artificial Intelligence. Mobile Networks and Applications, 23(2), 368–375. https://doi.org/10.1007/S11036-017-0932-8/METRICS

Niza, & Suyanto, S. (2023). Impact of Innovative Learning in Mathematics and Natural Sciences on Student Learning Achievements: A Meta-Analysis. Jurnal Iqra’ : Kajian Ilmu Pendidikan, 8(1), 87–99. https://doi.org/10.25217/JI.V8I1.3007

Lucy, L., & Bamman, D. (2021). Gender and Representation Bias in GPT-3 Generated Stories. 48–55. https://doi.org/10.18653/V1/2021.NUSE-1.5

Masters, K. (2019). Artificial intelligence in medical education. Medical Teacher, 41(9), 976–980. https://doi.org/10.1080/0142159X.2019.1595557

Miller, S. A., Perrotti, W., Silverthorn, D. U., Dalley, A. F., & Rarey, K. E. (2002). From college to clinic: Reasoning over memorization is key for understanding anatomy. Anatomical Record, 269(2), 69–80. https://doi.org/10.1002/AR.10071

Motlagh, N. Y., Khajavi, M., Sharifi, A., & Ahmadi, M. (2023). The Impact of Artificial Intelligence on the Evolution of Digital Education: A Comparative Study of OpenAI Text Generation Tools including ChatGPT, Bing Chat, Bard, and Ernie. https://arxiv.org/abs/2309.02029v1

Paranjape, K., Schinkel, M., Panday, R. N., Car, J., & Nanayakkara, P. (2019). Introducing artificial intelligence training in medical education. JMIR Medical Education, 5(2). https://doi.org/10.2196/16048

Perez, S., Massey-Allard, J., Butler, D., Ives, J., Bonn, D., Yee, N., & Roll, I. (2017). Identifying productive inquiry in virtual labs using sequence mining. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10331 LNAI, 287–298. https://doi.org/10.1007/978-3-319-61425-0_24

Purba, D., Sinurat, B., & Herman, H. (2021). Utilizing Instagram Social Media on Language Style: An Analysis to Teenagers as Millennial Generations in Their Captions. Anglophile Journal, 2(1), 1–11. https://doi.org/10.51278/ANGLOPHILE.V2I1.268

Ritual, P., Perahu, P., Di, P., Ara, D., Bontobahari, K., Bulukumba, K., Parera, A., Iswary, E., & Hasyim, M. (2020). Development of Augmented Reality Media on Cultural Objects in the Pinisi Boat Making Ritual Procession in Ara Village, Bontobahari District, Bulukumba Regency. Jurnal Al-Qiyam, 1(2), 63–73. https://doi.org/10.33648/ALQIYAM.V1I2.128

Rizky, M. H. (2022). Investigation of Teacher Attitudes of Using Information and Communications Technology (ICT) for Pandemic Era. Bulletin of Science Education, 2(2), 58–67. https://doi.org/10.51278/BSE.V2I2.355

Rohibni, R., Rokhmawan, T., Sayer, I. M., & Fitriyah, L. (2022). The Variety of Mathematics Learning Media for Early Childhood in Improving Basic Mathematics Ability. Bulletin of Science Education, 2(3), 102–114. https://doi.org/10.51278/BSE.V2I3.427

Roisatin, U. A., Thamrin, M., & Wahyuningsih, N. (2022). An Analysis of Application of Project-Based Learning to Improve Writing Skills of Mechanical Engineering Students. Bulletin of Pedagogical Research, 2(1), 64–74. https://doi.org/10.51278/BPR.V2I1.257

Qin, C., Zhang, A., Zhang, Z., Chen, J., Yasunaga, M., & Yang, D. (2023). Is ChatGPT a General-Purpose Natural Language Processing Task Solver? http://arxiv.org/abs/2302.06476

Rawas, S. (2023). ChatGPT: Empowering lifelong learning in the digital age of higher education. Education and Information Technologies, 1–14. https://doi.org/10.1007/S10639-023-12114-8/METRICS

Reiss, M. J. (2021). The use of AI in education: Practicalities and ethical considerations. London Review of Education, 19(1), 1–14. https://doi.org/10.14324/LRE.19.1.05

Stokel-Walker, C. (2022). AI bot ChatGPT writes smart essays — should academics worry? Nature. https://doi.org/10.1038/D41586-022-04397-7

Sugiarto, S., & Suhono, S. (2023). Case Study of Using ChatGPT among Students at PTKI Lampung. Jurnal Al-Qiyam, 4(2), 110-119

Triayomi, R., & Pamugkas, S. P. (2023). Development of Android Based Mathematics Learning Media for Primary School Students. Bulletin of Science Education, 3(3), 249–259. https://doi.org/10.51278/BSE.V3I3.855

Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/S41591-018-0300-7

Whalley, B., France, D., Park, J., Mauchline, A., & Welsh, K. (2021). Towards flexible personalized learning and the future educational system in the fourth industrial revolution in the wake of Covid-19. Higher Education Pedagogies, 6(1), 79–99. https://doi.org/10.1080/23752696.2021.1883458

Yeadon, W., Inyang, O. O., Mizouri, A., Peach, A., & Testrow, C. P. (2023). The death of the short-form physics essay in the coming AI revolution. Physics Education, 58(3). https://doi.org/10.1088/1361-6552/acc5cf

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). https://doi.org/10.1186/S41239-019-0171-0

Zhao, J., Xu, X., Jiang, H., & Ding, Y. (2020). The effectiveness of virtual reality-based technology on anatomy teaching: A meta-analysis of randomized controlled studies. BMC Medical Education, 20(1). https://doi.org/10.1186/S12909-020-1994-Z