Bibliometric Analysis: Research Trends in the Utilization of Artificial Intelligence (AI) in Biology Learning
DOI:
https://doi.org/10.55927/ajae.v4i1.13685Keywords:
Biology Learning, Artificial Intelligence, Educational Technology, Industrial Revolution 4.0, Bibliometric AnalysisAbstract
The rapid development of technology in the era of the Industrial Revolution 4.0 has affected various fields, including the field of education. One of the innovations that has emerged is the use of Artificial Intelligence (AI) in biology learning. This study aims to analyze research trends related to the use of AI in biology learning with a bibliometric analysis method on 68 articles from the Scopus database. This study identifies publication patterns, research contributions, and methods used. The results of the study show that the use of AI can improve students' understanding of complex biological concepts and improve students' learning experience. In addition, AI plays an important role in assisting teachers in evaluating and assessing learning outcomes. This research provides up-to-date insights into the potential of AI in biology learning and emphasizes the importance of following technology trends to improve the quality of education.
Downloads
References
Adelana, O. P. (2024). Exploring pre-service biology teachers’ intention to teach genetics using an AI intelligent tutoring - based system. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2024.2310976
Albdrani, R. N., & Al-Shargabi, A. A. (2023). Investigating the Effectiveness of ChatGPT for Providing Personalized Learning Experience: A Case Study. International Journal of Advanced Computer Science and Applications, 14(11), 1208–1213. https://doi.org/10.14569/IJACSA.2023.01411122
Alissa, R. A. S., & Hamadneh, M. A. (2023). The Level of Science and Mathematics Teachers’ Employment of Artificial Intelligence Applications in the Educational Process. International Journal of Education in Mathematics, Science and Technology, 11(6), 1597–1608. https://doi.org/10.46328/ijemst.3806
AlKanaan, H. M. N. (2022). Awareness Regarding the Implication of Artificial Intelligence in Science Education among Pre-Service Science Teachers. International Journal of Instruction, 15(3), 895–912. https://doi.org/10.29333/iji.2022.15348a
Alshorman, S. (2024). THE READINESS TO USE AI IN TEACHING SCIENCE: SCIENCE TEACHERS’ PERSPECTIVE. Journal of Baltic Science Education, 23(3), 432–448. https://doi.org/10.33225/jbse/24.23.432
Andersen, R., Mørch, A. I., & Litherland, K. T. (2022). Collaborative learning with block-based programming: investigating human-centered artificial intelligence in education. Behaviour and Information Technology, 41(9), 1830–1847. https://doi.org/10.1080/0144929X.2022.2083981
Angraini, L., Fitri, R., & Darussyamsu, R. (2022). Model pembelajaran problem based learning untuk meningkatkan hasil belajar biologi peserta didik : literature review. Bio-Pedagogi, 11(1), 42. https://doi.org/10.20961/bio-pedagogi.v11i1.62436
Aparicio, F., Morales-Botello, M. L., Rubio, M., Hernando, A., Muñoz, R., López-Fernández, H., … Buenaga, M. D. (2018). Perceptions of the use of intelligent information access systems in university level active learning activities among teachers of biomedical subjects. International Journal of Medical Informatics, 112, 21–33. https://doi.org/10.1016/j.ijmedinf.2017.12.016
Arada, K., Sanchez, A., & Bell, P. (2023). Youth as pattern makers for racial justice: How speculative design pedagogy in science can promote restorative futures through radical care practices. Journal of the Learning Sciences, 32(1), 76–109. https://doi.org/10.1080/10508406.2022.2154158
Bewersdorff, A., Seßler, K., Baur, A., Kasneci, E., & Nerdel, C. (2023). Assessing student errors in experimentation using artificial intelligence and large language models: A comparative study with human raters. Computers and Education: Artificial Intelligence, 5. https://doi.org/10.1016/j.caeai.2023.100177
Bredeweg, B., & Kragten, M. (2022). Requirements and challenges for hybrid intelligence: A case-study in education. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.891630
Canale, L., Cagliero, L., Farinetti, L., & Torchiano, M. (2024). On Predicting Exam Performance Using Version Control Systems’ Features. Computers, 13(6). https://doi.org/10.3390/computers13060150
Cooper, G. (2023). Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence. Journal of Science Education and Technology, 32(3), 444–452. https://doi.org/10.1007/s10956-023-10039-y
Crowther, G. J. (2023). Chatbot responses suggest that hypothetical biology questions are harder than realistic ones. Journal of Microbiology and Biology Education, 24(3). https://doi.org/10.1128/jmbe.00153-23
Dao, X. Q. (2023). LLMs Performance on Vietnamese High School Biology Examination. International Journal of Modern Education and Computer Science, 15(6), 14–30. https://doi.org/10.5815/ijmecs.2023.06.02
Dengel, A., Gehrlein, R., Fernes, D., Görlich, S., Maurer, J., Pham, H. H., … Eisermann, N. D. G. (2023). Qualitative Research Methods for Large Language Models: Conducting Semi-Structured Interviews with ChatGPT and BARD on Computer Science Education. Informatics, 10(4). https://doi.org/10.3390/informatics10040078
Deveci Topal, A., Dilek Eren, C., & Kolburan Geçer, A. (2021). Chatbot application in a 5th grade science course. Education and Information Technologies, 26(5), 6241–6265. https://doi.org/10.1007/s10639-021-10627-8
Dos Anjos, J. R., de Souza, M. G., de Andrade Neto, A. S., & de Souza, B. C. (2024). AN ANALYSIS OF THE GENERATIVE AI USE AS ANALYST IN QUALITATIVE RESEARCH IN SCIENCE EDUCATION. Revista Pesquisa Qualitativa, 12(30). https://doi.org/10.33361/RPQ.2024.v.12.n.30.724
Ferro, L. S., Sapio, F., Terracina, A., Temperini, M., & Mecella, M. (2021). Gea2: A Serious Game for Technology-Enhanced Learning in STEM. IEEE Transactions on Learning Technologies, 14(6), 723–739. https://doi.org/10.1109/TLT.2022.3143519
Forbus, K. D., Garnier, B., Tikoff, B., Marko, W., Usher, M., & McLure, M. (2020). Sketch worksheets in science, technology, engineering, and mathematics classrooms: Two deployments. AI Magazine, 41(1), 19–32. https://doi.org/10.1609/aimag.v41i1.5189
Friese, S., & Rother, K. (2016). A mixed-paradigm component architecture for implementing web-based game servers. Open Computer Science, 6(1), 25–32. https://doi.org/10.1515/comp-2016-0004
Gerard, L., Linn, M. C., & Holtmann, M. (2024). A Comparison of Responsive and General Guidance to Promote Learning in an Online Science Dialog. Education Sciences, 14(12). https://doi.org/10.3390/educsci14121383
Gunawan, K. D. H., Liliasari, S., Kaniawati, I., & Setiawan, W. (2020). Exploring science teachers’ lesson plans by the implementation of intelligent tutoring systems in blended learning environments. Universal Journal of Educational Research, 8(10), 4776–4783. https://doi.org/10.13189/ujer.2020.081049
Haidir, H., Muhamad, T., Roviati, R., Evi, E., & Deka, D. (2024). Penerapan Chat GPT dalam Pembelajaran Biologi. Jurnal Sosial Teknologi, 4(3), 182–189. https://doi.org/10.59188/jurnalsostech.v4i3.1064
Halawa, D. (2016). Pelaksanaan Pembelajaran Biologi Pada Pokok Bahasan Pencemaran Lingkungan Dengan Kelas Immersi. Jurnal Warta, 1(1), 1–12.
Halonen, N., Ståhle, P., Juuti, K., Paavola, S., & Lonka, K. (2023). Catalyst for co-construction: the role of AI-directed speech recognition technology in the self-organization of knowledge. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1232423
Hao, M., Wang, Y., & Peng, J. (2024). Empirical Research on AI Technology-Supported Precision Teaching in High School Science Subjects. Applied Sciences (Switzerland), 14(17). https://doi.org/10.3390/app14177544
Haudek, K. C., & Zhai, X. (2024). Examining the Effect of Assessment Construct Characteristics on Machine Learning Scoring of Scientific Argumentation. International Journal of Artificial Intelligence in Education, 34(4), 1482–1509. https://doi.org/10.1007/s40593-023-00385-8
Henrich, M., Formella-Zimmermann, S., Gübert, J., & Dierkes, P. W. (2023). Students’ technology acceptance of computer-based applications for analyzing animal behavior in an out-of-school lab. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1216318
Henze, J., Schatz, C., Malik, S., & Bresges, A. (2022). How Might We Raise Interest in Robotics, Coding, Artificial Intelligence, STEAM and Sustainable Development in University and On-the-Job Teacher Training? Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.872637
Herawati, P., Utami, S. B., & Karlina, N. (2022). Analisis Bibliometrik: Perkembangan Penelitian Dan Publikasi Mengenai Koordinasi Program Menggunakan Vosviewer. Jurnal Pustaka Budaya, 9(1), 1–8. https://doi.org/10.31849/pb.v9i1.8599
Issa, W. B., Shorbagi, A., Al-Sharman, A., Rababa, M., Al-Majeed, K., Radwan, H., … Fakhry, R. (2024). Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey. BMC Medical Education, 24(1). https://doi.org/10.1186/s12909-024-06076-9
Jeon, I.-S., Kim, S.-Y., & Kang, S.-J. (2024). Developing Standards for Educational Datasets by School Level: A Framework for Sustainable K-12 Education. Sustainability (Switzerland) , 16(12). https://doi.org/10.3390/su16124954
Jiang, S., McClure, J., Mao, H., Chen, J., Liu, Y., & Zhang, Y. (2024). Integrating Machine Learning and Color Chemistry: Developing a High-School Curriculum toward Real-World Problem-Solving. Journal of Chemical Education, 101(2), 675–681. https://doi.org/10.1021/acs.jchemed.3c00589
Kaldaras, L., & Haudek, K. C. (2022). Validation of automated scoring for learning progression-aligned Next Generation Science Standards performance assessments. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.968289
Kaldaras, L., Yoshida, N. R., & Haudek, K. C. (2022). Rubric development for AI-enabled scoring of three-dimensional constructed-response assessment aligned to NGSS learning progression. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.983055
Karaca, O., Çalışkan, S. A., & Demir, K. (2021). Medical artificial intelligence readiness scale for medical students (MAIRS-MS) – development, validity and reliability study. BMC Medical Education, 21(1). https://doi.org/10.1186/s12909-021-02546-6
Kerneža, M., & Zemljak, D. (2023). SCIENCE TEACHERS’ APPROACH TO CONTEMPORARY ASSESSMENT WITH A READING LITERACY EMPHASIS. Journal of Baltic Science Education, 22(5), 851–864. https://doi.org/10.33225/jbse/23.22.851
Koć-Januchta, M. M. (2020). Engaging With Biology by Asking Questions: Investigating Students’ Interaction and Learning With an Artificial Intelligence-Enriched Textbook. Journal of Educational Computing Research, 58(6), 1190–1224. https://doi.org/10.1177/0735633120921581
Kosar, T., Ostojić, D., Liu, Y. D., & Mernik, M. (2024). Computer Science Education in ChatGPT Era: Experiences from an Experiment in a Programming Course for Novice Programmers. Mathematics, 12(5). https://doi.org/10.3390/math12050629
Krtalić, A., & Bajić, M. (2019). Development of the TIRAMISU Advanced Intelligence Decision Support System. European Journal of Remote Sensing, 52(1), 40–55. https://doi.org/10.1080/22797254.2018.1550351
Kubsch, M., Czinczel, B., Lossjew, J., Wyrwich, T., Bednorz, D., Bernholt, S., … Rummel, N. (2022). Toward learning progression analytics — Developing learning environments for the automated analysis of learning using evidence centered design. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.981910
Kumar, A., Saudagar, A. K. J., Alkhathami, M., Alsamani, B., Khan, M. B., Hasanat, M. H. A., … Srinivasan, B. (2023). Gamified Learning and Assessment Using ARCS with Next-Generation AIoMT Integrated 3D Animation and Virtual Reality Simulation. Electronics (Switzerland), 12(4). https://doi.org/10.3390/electronics12040835
Laupichler, M. C., Aster, A., Haverkamp, N., & Raupach, T. (2023). Development of the “Scale for the assessment of non-experts’ AI literacy” – An exploratory factor analysis. Computers in Human Behavior Reports, 12. https://doi.org/10.1016/j.chbr.2023.100338
Lee, G.-G., Choi, M., An, T., Mun, S., & Hong, H.-G. (2023). Development of the Hands-free AI Speaker System Supporting Hands-on Science Laboratory Class: A Rapid Prototyping. International Journal of Emerging Technologies in Learning, 18(1), 115–136. https://doi.org/10.3991/ijet.v18i01.34843
Lee, G. G. (2024). Using ChatGPT for Science Learning: A Study on Pre-service Teachers’ Lesson Planning. IEEE Transactions on Learning Technologies, 17, 1683–1700. https://doi.org/10.1109/TLT.2024.3401457
Lee, J., An, T., Chu, H.-E., Hong, H.-G., & Martin, S. N. (2023). Improving Science Conceptual Understanding and Attitudes in Elementary Science Classes through the Development and Application of a Rule-Based AI Chatbot. Asia-Pacific Science Education, 13(2). https://doi.org/10.1163/23641177-bja10070
Lewis, S., Bhyat, F., Casmod, Y., Gani, A., Gumede, L., Hajat, A., … Vermeulen, L. (2024). Medical imaging and radiation science students’ use of artificial intelligence for learning and assessment. Radiography, 30, 60–66. https://doi.org/10.1016/j.radi.2024.10.006
Li, Y., Wang, Y., Lee, Y., Chen, H., Petri, A. N., & Cha, T. (2023). Teaching Data Science through Storytelling: Improving Undergraduate Data Literacy. Thinking Skills and Creativity, 48. https://doi.org/10.1016/j.tsc.2023.101311
Lukman, L., Riska Agustina, & Rihadatul Aisy. (2024). Problematika Penggunaan Artificial Intelligence (AI) untuk Pembelajaran di Kalangan Mahasiswa STIT Pemalang. Jurnal Madaniyah, 13(2), 242–255. https://doi.org/10.58410/madaniyah.v13i2.826
Mendonça, N. C. (2024). Evaluating ChatGPT-4 Vision on Brazil’s National Undergraduate Computer Science Exam. ACM Transactions on Computing Education, 24(3). https://doi.org/10.1145/3674149
Mnguni, L. (2024). AI Integration in Biology Education: Comparative Insights into Perceived Benefits and TPACK among South African and Indonesian Pre-service Teachers. Asia-Pacific Science Education. https://doi.org/10.1163/23641177-bja10086
Mnguni, L., Nuangchalerm, P., Zaky El Islami, R. A., Sibanda, D., Sari, I. J., & Ramulumo, M. (2024). The behavioural intentions for integrating artificial intelligence in science teaching among pre-service science teachers in South Africa and Thailand. Computers and Education: Artificial Intelligence, 7. https://doi.org/10.1016/j.caeai.2024.100334
Moola, Z., Dhurumraj, T., & Ramaila, S. (2024). Teachers’ Views on the Interdependence of Humanity and Technology in Life Sciences Teaching and Learning within the Context of the 5IR. International Journal of Learning, Teaching and Educational Research, 23(7), 476–498. https://doi.org/10.26803/ijlter.23.7.24
Murakami, Y., Sho, Y., & Inagaki, T. (2024). Improving Motivation in Learning AI for Undergraduate Students by Case Study. Journal of Information Processing, 32, 175–181. https://doi.org/10.2197/ipsjjip.32.175
Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). Teachers’ trust in AI-powered educational technology and a professional development program to improve it. British Journal of Educational Technology, 53(4), 914–931. https://doi.org/10.1111/bjet.13232
Ng, D. T. K., Tan, C. W., & Leung, J. K. L. (2024). Empowering student self-regulated learning and science education through ChatGPT: A pioneering pilot study. British Journal of Educational Technology, 55(4), 1328–1353. https://doi.org/10.1111/bjet.13454
Nja, C. O., Idiege, K. J., Uwe, U. E., Meremikwu, A. N., Ekon, E. E., Erim, C. M., … Cornelius-Ukpepi, B. U. (2023). Adoption of artificial intelligence in science teaching: From the vantage point of the African science teachers. Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00261-x
Nyaaba, M., Zhai, X., & Faison, M. Z. (2024). Generative AI for Culturally Responsive Science Assessment: A Conceptual Framework. Education Sciences, 14(12). https://doi.org/10.3390/educsci14121325
Park, J., Teo, T. W., Teo, A., Chang, J., Huang, J. S., & Koo, S. (2023). Integrating artificial intelligence into science lessons: teachers’ experiences and views. International Journal of STEM Education, 10(1). https://doi.org/10.1186/s40594-023-00454-3
Piccolo, S. R., Denny, P., Luxton-Reilly, A., Payne, S. H., & Ridge, P. G. (2023). Evaluating a large language model’s ability to solve programming exercises from an introductory bioinformatics course. PLoS Computational Biology, 19(9 September). https://doi.org/10.1371/journal.pcbi.1011511
Puspitawati, R. P., Afnan, M. Z., Abrizal, H. P., Anjani, G. A. D. K., & Adistria, B. (2024). Artificial Intelligence dalam Artikel Pendidikan Biologi pada Rentang Tahun 2003-2024. In Seminar Nasional Biologi “Inobasi Penelitian dan Pembelajaran Biologi VIII (IP2B VIII)” (pp. 44–60). Retrieved from http://jurnal.fkip.unila.ac.id/index.php/JPVTI/index
Rahioui, F. (2024). Exploring Complex Biological Processes Through Artificial Intelligence. Journal of Educators Online, 21(2). https://doi.org/10.9743/JEO.2024.21.2.9
Rochmawati, D. R., Arya, I., & Zakariyya, A. (2023). Manfaat Kecerdasan Buatan Untuk Pendidikan. Jurnal Teknologi Komputer Dan Informatika, 2(1), 124–134. https://doi.org/10.59820/tekomin.v2i1.163
Septiyanto, A., Ashidiq, R. M., & Prima, E. C. (2022). Investigasi Tren Penelitian Pendidikan STEM: Analisis Bibliometrik Dari Tahun 2018-2022. In Seminar Nasional IPA XIII “Kecemerlangan Pendidikan IPA untuk Konservasi Sumber Daya Alam” (pp. 649–665).
Shen, C., Laloy, E., Elshorbagy, A., Albert, A., Bales, J., Chang, F.-J., … Tsai, W.-P. (2018). HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community. Hydrology and Earth System Sciences, 22(11), 5639–5656. https://doi.org/10.5194/hess-22-5639-2018
Shi, Y., & Rao, L. (2022). Construction of STEAM Graded Teaching System Using Backpropagation Neural Network Model under Ability Orientation. Scientific Programming, 2022. https://doi.org/10.1155/2022/7792943
Siregar, F. P., Wahyudi, S., Chandra, D. A., & Dwiana, A. A. (2024). ChatGPT Dalam Mendukung Pembelajaran di Sekolah. Jurnal Pendidikan Teknologi Informasi Dan Vokasional, 6(1), 24–34. Retrieved from http://jurnal.fkip.unila.ac.id/index.php/JPVTI/index
Sobron, M., & Lubis. (2021). Implementasi Artificial Intelligence Pada System Manufaktur Terpadu. Seminar Nasional Teknik (SEMNASTEK) UISU, 4(1), 1–7. Retrieved from https://jurnal.uisu.ac.id/index.php/semnastek/article/view/4134
Veras, M., Dyer, J.-O., Rooney, M., Silva, P. G. B., Rutherford, D., & Kairy, D. (2023). Usability and Efficacy of Artificial Intelligence Chatbots (ChatGPT) for Health Sciences Students: Protocol for a Crossover Randomized Controlled Trial. JMIR Research Protocols, 12(1). https://doi.org/10.2196/51873
Veras, M., Dyer, J.-O., Shannon, H., Bogie, B. J. M., Ronney, M., Sekhon, H., … Kairy, D. (2024). A mixed methods crossover randomized controlled trial exploring the experiences, perceptions, and usability of artificial intelligence (ChatGPT) in health sciences education. Digital Health, 10. https://doi.org/10.1177/20552076241298485
Wang, J. T. H. (2023). Is the laboratory report dead? AI and ChatGPT. Microbiology Australia, 44(3), 144–148. https://doi.org/10.1071/MA23042
Wang, L., Zhang, H., Zhang, Y., Hu, K., & An, K. (2023). A Deep Learning-Based Experiment on Forest Wildfire Detection in Machine Vision Course. IEEE Access, 11, 32671–32681. https://doi.org/10.1109/ACCESS.2023.3262701
Xu, Y., Vigil, V., Bustamante, A. S., & Warschauer, M. (2022). Contingent interaction with a television character promotes children’s science learning and engagement. Journal of Applied Developmental Psychology, 81. https://doi.org/10.1016/j.appdev.2022.101439
Yan, A., Zou, Y., & Mirchandani, D. A. (2020). How hospitals in mainland China responded to the outbreak of COVID-19 using information technology-enabled services: An analysis of hospital news webpages. Journal of the American Medical Informatics Association, 27(7), 991–999. https://doi.org/10.1093/jamia/ocaa064
Yannier, N., Hudson, S. E., Chang, H., & Koedinger, K. R. (2024). AI Adaptivity in a Mixed-Reality System Improves Learning. International Journal of Artificial Intelligence in Education, 34(4), 1541–1558. https://doi.org/10.1007/s40593-023-00388-5
Yoo, J., Park, J., Ha, M., & Mae Lagmay Darang, C. (2024). Exploring Pre-Service Teachers’ Cognitive Processes and Calibration with an Unsupervised Learning-Based Automated Evaluation System. SAGE Open, 14(3). https://doi.org/10.1177/21582440241262864
Yunus, M., & Mitrohardjono, M. (2020). Pengembangan Tehnologi Di Era Industri 4.0 Dalam Pengelolaan Pendidikan Sekolah Dasar Islam Plus Baitul Maal. Jurnal Tahdzibi: Manajemen Pendidikan Islam, Vol 3(No. 2), 134. https://doi.org/10.24853/tahdzibi.3.2.129-138
Zhai, X., He, P., & Krajcik, J. (2022). Applying machine learning to automatically assess scientific models. Journal of Research in Science Teaching, 59(10), 1765–1794. https://doi.org/10.1002/tea.21773
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Saskia Fadilla Nur Rachman

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






























