CHATGPT ASSISTANCE ON BIOCHEMISTRY LEARNING OUTCOMES OF PRE-SERVICE TEACHERS
Abstract
This research investigates the effect of ChatGPT on the learning outcomes of pre-service biology teachers. Sampling was done by purposive sampling in class A (treated with ChatGPT) and class B (Control) of pre-service biology teachers. There were 3 meetings in each class, with a system of material presentation, review, and test at each meeting. Sampling was done using the Quizizz platform, and it was found that the speed of answering class A (ChatGPT) was faster than class B (Control), but the accuracy of the answers was not significantly different. This could be because ChatGPT presents the material in an easy-to-understand version, but uses the same source of reference material as other conventional search engines. ChatGPT has also been shown to be effective and very easy to use by pre-service biology teachers and can increase their motivation to learn. Challenges such as technical and network disruptions and concerns about ChatGPT dependency, also need to be addressed by policy makers and educators. This study proves that ChatGPT is a valuable learning tool for students, but its presence cannot replace the role of teachers or other conventional sources of information. Through this research, it is hoped that it can provide insight to policymakers and educators, to be able to maximize the potential of ChatGPT, to increase the efficiency and ease of learning for students.
Penelitian ini mengkaji pengaruh penggunaan ChatGPT terhadap hasil belajar calon guru biologi. Pengambilan sampel dilakukan secara purposive pada kelas A (diberi perlakuan dengan ChatGPT) dan kelas B (kontrol) dari mahasiswa calon guru biologi. Setiap kelas mengikuti tiga pertemuan dengan sistem penyampaian materi, ulasan, dan tes pada setiap pertemuan. Pengambilan data dilakukan melalui platform Quizizz, dan ditemukan bahwa kecepatan menjawab kelas A (menggunakan ChatGPT) lebih cepat dibandingkan kelas B (kontrol), namun tingkat ketepatan jawaban tidak menunjukkan perbedaan yang signifikan. Hal ini mungkin disebabkan oleh penyajian materi oleh ChatGPT yang lebih mudah dipahami, meskipun sumber referensi yang digunakan tetap sama dengan mesin pencari konvensional lainnya.
ChatGPT juga terbukti efektif dan sangat mudah digunakan oleh calon guru biologi, serta mampu meningkatkan motivasi mereka dalam belajar. Namun, tantangan seperti gangguan teknis dan jaringan serta kekhawatiran terhadap ketergantungan pada ChatGPT juga perlu mendapat perhatian dari para pembuat kebijakan dan pendidik. Penelitian ini membuktikan bahwa ChatGPT merupakan alat bantu belajar yang berharga bagi siswa, namun kehadirannya tidak dapat menggantikan peran guru atau sumber informasi konvensional lainnya. Melalui penelitian ini, diharapkan dapat memberikan wawasan bagi para pembuat kebijakan dan pendidik untuk memaksimalkan potensi ChatGPT dalam meningkatkan efisiensi dan kemudahan belajar bagi siswa.
Keywords
References
Alafnan, M. A., Dishari, S., Jovic, M., & Lomidze, K. (2023). ChatGPT as an Educational Tool: Opportunities, Challenges, and Recommendations for Communication, Business Writing, and Composition Courses. Journal of Artificial Intelligence and Technology, 3(2), 60–68. https://doi.org/10.37965/jait.2023.0184
Alkharusi, H. (2022). A descriptive Analysis and Interpretation of Data from Likert Scales in Educational and Psychological Research. Indian Journal of Psychology and Education, 12(2), 13–16. https://doi.org/10.1016/j.cptl.2015.08.001
Alya Resti Saraswati, Vasya Ayu Karmina, Maharani Putri Efendi, Zahrina Candrakanti, & Nur Aini Rakhmawati. (2023). Analisis Pengaruh ChatGPT Terhadap Tingkat Kemalasan Berpikir Mahasiswa ITS Dalam Proses Pengerjaan Tugas. Jurnal Pendidikan, Bahasa Dan Budaya, 2(4), 40–48. https://doi.org/10.55606/jpbb.v2i4.2223
Azaria, A. (2022). ChatGPT Usage and Limitations. Researchgate. https://doi.org/10.13140/RG.2.2.26616.11526
Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. Journal of AI, 52(7), 52–62.
Chinonso, O. E., Theresa, A. M.-E., & Aduke, T. C. (2023). ChatGPT for Teaching, Learning and Research: Prospects and Challenges. Global Academic Journal of Humanities and Social Sciences, 5(02), 33–40. https://doi.org/10.36348/gajhss.2023.v05i02.001
Dempere, J., Modugu, K., Hesham, A., & Ramasamy, L. K. (2023). The impact of ChatGPT on higher education. In Frontiers in Education (Vol. 8). Frontiers Media SA. https://doi.org/10.3389/feduc.2023.1206936
Diantama, S. (2023). Pemanfaatan Artificial Intelegent (AI) Dalam Dunia Pendidikan (Vol. 1, Issue 1).
Dilzhan, B. (2024). Teaching English and Artificial Intelligence: EFL Teachers’ Perceptions and Use of ChatGPT.
Elhousni, Z., Laamech, J., Zerhane, R., & Janati-Idrissi, R. (2023). Difficulties in learning biochemistry: Case of 1st year medical students, Tangier. Journal for Educators, Teachers and Trainers, 14(1). https://doi.org/10.47750/jett.2023.14.01.006
Fagerland, M. W. (2012). t-tests, non-parametric tests, and large studies-a paradox of statistical practice? In Fagerland BMC Medical Research Methodology (Vol. 12). http://www.biomedcentral.com/1471-2288/12/78
Gibbons, J. D., & Chakraborti, S. (1991). Comparisons of the mann-whitney, student’s t, and alternate t tests for means of normal distributions. Journal of Experimental Education, 59(3), 258–267. https://doi.org/10.1080/00220973.1991.10806565
Gill, S. S., & Kaur, R. (2023). ChatGPT: Vision and challenges. Internet of Things and Cyber-Physical Systems, 3, 262–271. https://doi.org/10.1016/j.iotcps.2023.05.004
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education. Promise and Implications for Teaching and Learning. https://www.researchgate.net/publication/332180327
Hristidis, V., Ruggiano, N., Brown, E. L., Ganta, S. R. R., & Stewart, S. (2023). ChatGPT vs Google for Queries Related to Dementia and Other Cognitive Decline: Comparison of Results. Journal of Medical Internet Research, 25. https://doi.org/10.2196/48966
Jafar Maulana, M., & Darmawan, C. (2023). Penggunaan ChatGPT Dalam Pendidikan Berdasarkan Perspektif Etika Akademik. 10(01), 58–66.
Johnson, R. B., & Christensen, L. (2014). Educational Research Quantitative, Qualitative, and Mixed Approaches.
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., … Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2
Kalla, D., & Kuraku, S. (2023). Study and Analysis of Chat GPT and its Impact on Different Fields of Study. In International Journal of Innovative Science and Research Technology (Vol. 8, Issue 3). www.ijisrt.com
Kengam, J. (2020). Artificial Intelligence In Education. Research Gate. https://doi.org/10.13140/RG.2.2.16375.65445
Kim, T. (2015). T-test as a parametric statistic. Korean Journal of Anesthesiology, 68, 540–546. http://ekja.org
Luckin, R., Holmes, W., Griffiths, M., & Pearson, L. B. F. (2016). Intelligence Unleashed An argument for AI in Education.
Lund, B. D. (2022). A Chat with ChatGPT: How will AI impact scholarly publishing? Researchgate. https://doi.org/10.13140/RG.2.2.34572.18565/2
Lund, B. D., & Wang, T. (2023). Chatting about ChatGPT: How may AI and GPT impact academia and libraries? https://ssrn.com/abstract=4333415
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence.
McKnight, P. E., & Najab, J. (2010). Mann-Whitney U Test. In The Corsini Encyclopedia of Psychology (Eds I.B. Weiner and W.E. Craighead).
Mehring, J., & Thomson, R. (2016). Brain-Friendly Learning Tips for Long-Term Retention and Recall. The Language Teacher, 40(4), 1–13. http://jalt-publications.org/tlt
Nazir, A., & Wang, Z. (2023). A comprehensive survey of ChatGPT: Advancements, applications, prospects, and challenges. Meta-Radiology, 1(2), 100022. https://doi.org/10.1016/j.metrad.2023.100022
Ngo, T. T. A. (2023). The Perception by University Students of the Use of ChatGPT in Education. International Journal of Emerging Technologies in Learning, 18(17), 4–19. https://doi.org/10.3991/ijet.v18i17.39019
OpenAI. (2024, May 13). Introducing GPT-4o and more tools to ChatGPT free users. OpenAI.
Pearson, E. S., & Bowman, K. O. (1977). Tests for departure from normality: Comparison of powers. In Biometrika (Vol. 64, Issue 2). http://biomet.oxfordjournals.org/
Putri, N. W. S., & Suryati, N. K. (2016). Modul Statistika dengan SPSS.
Royston, J. P. (1982). An Extension of Shapiro and Wilk’s W Test for Normality to Large Samples. In Source: Journal of the Royal Statistical Society. Series C (Applied Statistics) (Vol. 31, Issue 2).
Saghiri, A. M. (2020). A Survey on Challenges in Designing Cognitive Engines. International Conference on Web Research (ICWR), 165–171.
Saleh, Z. (2019). Artificial Intelligence Definition, Ethics and Standards. Journal of Artificial Intelligence, 1–10.
Smith, T. M., & Desimone, L. M. (2003). Do Changes in Patterns of Participation in Teachers’ Professional Development Reflect the Goals of Standards-based Reform? JSTOR, 81(3), 119–129.
Songsiengchai, S., Sereerat, B., & Watananimitgul, W. (2023). Leveraging Artificial Intelligence (AI): Chat GPT for Effective English Language Learning among Thai Students. English Language Teaching, 16(11), 68. https://doi.org/10.5539/elt.v16n11p68
Spillane, J. P., & Callahan, K. A. (2000). Implementing State Standards for Science Education: What District Policy Makers Make of the Hoopla. In J Res Sci Teach (Vol. 37).
Ulusoy, I., Yılmaz, M., & Kıvrak, A. (2023). How Efficient Is ChatGPT in Accessing Accurate and Quality Health-Related Information? Cureus. https://doi.org/10.7759/cureus.46662
Willits, F. K., Theodori, G. L., & Luloff, A. E. (2016). Another Look at Likert Scales. Journal of Rural Social Sciences, 31(3), 126–139. https://egrove.olemiss.edu/jrss
Winkler, R., & Soellner, M. (2018). Unleashing the Potential of Chatbots in Education: A State-Of-The-Art Analysis. Academy of Management Proceedings, 2018(1), 15903. https://doi.org/10.5465/ambpp.2018.15903abstract
Zhai, X. (2023). ChatGPT User Experience: Implications for Education. SSRN, 1–18. https://orcid.org/0000-0003-4519-1931
Zhou, L., & Li, J. (2023). The Impact of ChatGPT on Learning Motivation: A Study Based on Self-Determination Theory. Education Science and Management, 1(1), 19–29. https://doi.org/10.56578/esm010103
Zimmerman, D. W. (1998). Invalidation of parametric and nonparametric statistical tests by concurrent violation of two assumptions. Journal of Experimental Education, 67(1), 55–68. https://doi.org/10.1080/00220979809598344
DOI: https://doi.org/10.17509/e.v24i2.76844
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