Artificial Intelligence Research in Biology Learning: A Systematic Review

Ali Usman

Abstract


This study aimed to investigate the trends in artificial intelligence publication, author contributions, active countries, and key information in articles. A systematic review used in this study. The research parameters include the publication period from 2018 to 2023, English-language articles, article accessibility, and the application of artificial intelligence in biology learning. The results of the study show fluctuations in interest in artificial intelligence research in biology learning. In addition, the various contributions from the authors reflect the diversity of views and analytical approaches. The results also reveal that the analyzed documents originate from multiple countries. Furthermore, widely used artificial intelligence applications include learning personalization, interactive material development, student performance evaluation, intelligent tutoring, big data analysis, and collaborative learning. The conclusion is that artificial intelligence has been applied in various countries, demonstrating its potential to enhance the learning experience and improve the effectiveness of the learning process. The study's recommendation is that further research is needed to develop the use of artificial intelligence in supporting student-focused learning models that empower 21st-century competencies, especially in rural areas, to minimize the learning quality gap in Indonesia.


Keywords


Artificial Intelligence; Automatic Scoring; Biology Education; Collaborative Learning; Interactive Material

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References


Abella-García, V., Delgado-Benito, V., Ausín-Villaverde, V., Hortigüela-Alcalá, D., 2019, ‘To tweet or not to tweet: Student perceptions of the use of Twitter on an undergraduate degree course’, Innovations in Education and Teaching Internasional’, vol. 56, pp. 402–411. https://doi.org/10.1080/14703297.2018.1444503

Ahmad, N.I.N., Junaini, S.N., 2020, ‘Augmented reality for learning mathematics: A systematic literature review’, International Journal of Emerging Technologies in Learning (iJET), vol.15, no.106, https://doi.org/10.3991/ijet.v15i16.14961

Al Yakin, A., Muthmainnah, M., Al-Matari, A.S., de Barros Cardoso, L.M.O., Yunus, N.H., Hajar, S., Harianti, H., 2025, ’Transferability cybersocialization as a digital civility through artificial intelligence-based design thinking mindset to modern pedagogy’, Lecture Notes in Networks and Systems, vol. 1075 pp. 237–250. https://doi.org/10.1007/978-981-97-6106-7_13

Alam, Md.A., Shahjaman, M., Rahman, Md.F., Hossain, F., Deng, H.-W., 2019, ‘Gene shaving using a sensitivity analysis of kernel based machine learning approach, with applications to cancer data’, PLoS One, vol. 14, pp. e0217027–e0217027. https://doi.org/10.1371/journal.pone.0217027

Begum, A., Gaytan, J.C.T., Ahmed, G., 2023, ’The nexus between technology and finnovation: A sustainable development model’, in International Conference on Business Analytics for Technology and Security (ICBATS). IEEE 2023, pp.1–8. https://doi.org/10.1109/ICBATS57792.2023.10111102

Beltran, M., Calvo, M., Gonzalez, S., 2018, ‘Experiences using capture the flag competitions to introduce gamification in undergraduate computer security labs’, in International Conference on Computational Science and Computational Intelligence (CSCI). IEEE 2018, pp. 574–579. https://doi.org/10.1109/CSCI46756.2018.00116

Bertolini, R., Finch, S.J., Nehm, R.H., 2023, ‘An application of Bayesian inference to examine student retention and attrition in the STEM classroom’, Front Educ (Lausanne), vol. 8, no. 1073829. https://doi.org/10.3389/feduc.2023.1073829

Blaser, M., 2019, ’Combining pre-class preparation with collaborative in-class activities to improve student engagement and success in general chemistry’, ACS Symposium Series, vol. 1322, pp. 21–33. https://doi.org/10.1021/bk-2019-1322.ch002

Boyer, R.C., Scherer, W.T., Smith, M.C., 2017, ‘Trends over two decades of transportation research’, Transportation Research Record’, Journal of the Transportation Research Board, vol. 2614, pp. 1–9. https://doi.org/10.3141/2614-01

Cao, F., Lei, M., Lin, S., Xiang, M., 2022, ‘Application of artificial intelligence-based big data ai technology in physical education reform’, Mobile Information Systems, vol. 2022, pp. 1–12. https://doi.org/10.1155/2022/4017151

Chang-Tik, C., Song, B.-K., 2023, ‘Active learning in informal learning spaces: Science students’ experiences’, Pedagogies: An International Journal, vol. 18, pp. 374–391. https://doi.org/10.1080/1554480X.2022.2061978

Frerejean, J., van Merriënboer, J.J.G., Condron, C., Strauch, U., Eppich, W., 2023, ‘Critical design choices in healthcare simulation education: A 4C/ID perspective on design that leads to transfer’, Advances in Simulation, vol. 8, pp. 5. https://doi.org/10.1186/s41077-023-00242-7

Gibson, D., Kovanovic, V., Ifenthaler, D., Dexter, S., Feng, S., 2023, ‘Learning theories for artificial intelligence promoting learning processes’, British Journal of Educational Technology, vol. 54, pp. 1125–1146. https://doi.org/10.1111/bjet.13341

Gupta, H., Kaur, A., Kavita, Verma, S., Rawat, P., 2023, ‘Recognition of handwritten digits using convolutional neural network in python and comparison of performance for various hidden layers’, In book: International Conference on Innovative Computing and Communications, pp. 727–739. https://doi.org/10.1007/978-981-99-3010-4_58

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’, Front Educ (Lausanne), vol. 8, no. 1216318. https://doi.org/10.3389/feduc.2023.1216318

Hettiarachchi, E., 2019, ‘Analyzing the impact of introducing active learning in a blended educational environment’, International Journal of Learning and Teaching, vol. 8, no. 4, pp. 285–291. https://doi.org/10.18178/ijlt.5.4.285-291

Huang, C.-D., Tseng, H.-M., Jenq, C.-C., Ou, L.-S., 2020, ‘Active learning of medical students in Taiwan: A realist evaluation’, BMC Med Educ, vol. 20, pp. 1-8. https://doi.org/10.1186/s12909-020-02392-y

Hubbard, J.K., Couch, B.A., 2018, ‘The positive effect of in-class clicker questions on later exams depends on initial student performance level but not question format’, Comput Educ, vol. 120, pp. 1–12. https://doi.org/10.1016/j.compedu.2018.01.008

Hudha, A.M., Amin, M., Sumitro, S.B., Akbar, S., 2018, ‘The effectiveness of oidde learning model in the improvement of bioethics knowledge, ethical decision, and ethical attitude of biology pre-service teachers’, Journal of Baltic Science Education, vol. 17, pp. 960–971. https://doi.org/10.33225/jbse/18.17.960

Jaedun‬, Amat, Harto, S.P., Hastutiningsih, A.D., Hasbi, H., 2022, ‘Training effect on teacher ability to implement the 21st century skills in learning’, Cypriot Journal of Educational Sciences, vol. 17, pp. 3516–3528. https://doi.org/10.18844/cjes.v17i9.8089‬

Jahnke, I., Meinke-Kroll, M., Todd, M., Nolte, A., 2022, ‘Exploring artifact-generated learning with digital technologies: Advancing active learning with co-design in higher education across disciplines’, Technology, Knowledge and Learning, vol. 27, pp. 335–364. https://doi.org/10.1007/s10758-020-09473-3

Jain, S., Jain, B.K., Jain, P.K., Marwaha, V., 2022, ‘“Technology Proficiency” in medical education: Worthiness for worldwide wonderful competency and sophistication’, Adv Med Educ Pract, vol. 13, pp. 1497–1514. https://doi.org/10.2147/AMEP.S378917

Jebril Noor A., Abu Al-Haija Qasem, 2021, ‘Artificial intelligent and machine learning methods in bioinformatics and medical informatics’, Springer Nature Link, pp. 13–30. https://doi.org/10.1007/978-3-030-14647-4_2

Johnston, I.G., Slater, M., Cazier, J.-B., 2022, ‘Interdisciplinary and transferable concepts in bioinformatics education: Observations and approaches from a uk msc course’, Front Educ (Lausanne), vol. 7, no. 826951. https://doi.org/10.3389/feduc.2022.826951

Koć-Januchta, M.M., Schönborn, K.J., Roehrig, C., Chaudhri, V.K., Tibell, L.A.E., Heller, H.C., 2022, ‘“Connecting concepts helps put main ideas together”: Cognitive load and usability in learning biology with an AI-enriched textbook’, International Journal of Educational Technology in Higher Education, vol. 19, no. 1, pp. 1-22. https://doi.org/10.1186/s41239-021-00317-3

Koć-Januchta, M.M., Schönborn, K.J., Tibell, L.A.E., Chaudhri, V.K., Heller, H.C., 2020, ’Engaging with biology by asking questions: Investigating students’ interaction and learning with an artificial intelligence-enriched textbook’, Journal of Educational Computing Research, vol. 58, pp. 1190–1224. https://doi.org/10.1177/0735633120921581

Lu, G., 2022, ‘Prediction model and data simulation of sports performance based on the artificial intelligence algorithm’, Comput Intell Neurosci, vol. 2022, no.1, pp. 1–10. https://doi.org/10.1155/2022/7238789

Ma, Y., Yu, Z., Han, G., Li, J., Anh, V., 2018, ‘Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs’, BMC Bioinformatics, vol. 19, pp. 1-11. https://doi.org/10.1186/s12859-018-2518-2

Maganathan, T., Senthilkumar, S., Balakrishnan, V., 2020, ‘Machine learning and data analytics for environmental science: A review, prospects and challenges’, IOP Conf Ser Mater Sci Eng, vol. 955, pp. 1-9. https://doi.org/10.1088/1757-899X/955/1/012107

Manikandan, S., Chinnadurai, M., 2020, ‘Evaluation of students’ performance in educational sciences and prediction of future development using tensorflow’, International Journal of Engineering Education, vol. 36, pp. 1783–1790.

Metcalf, L.E., Bernacki, M.L., Bernacki, L.E., 2023, ‘How do digital textbook platforms promote active learning in undergraduate biology courses?’, J Res Sci Teach, vol. 60, pp. 1579–1610. https://doi.org/10.1002/tea.21845

Omarova, N.O., Omarov, О.А., Osmanova, А.О., 2021, ‘Design and implementation of pedagogical innovations based on digital technologies’, Springer Nature Link, pp. 371–378. https://doi.org/10.1007/978-3-030-57065-1_37

Onah, D.F.O., Pang, E.L.L., Sinclair, J.E., 2020, ‘Cognitive optimism of distinctive initiatives to foster self-directed and self-regulated learning skills: A comparative analysis of conventional and blended-learning in undergraduate studies’, Educ Inf Technol (Dordr), vol. 25, pp. 4365–4380. https://doi.org/10.1007/s10639-020-10172-w

Ostafe, R., Fontaine, N., Frank, D., Ng Fuk Chong, M., Prodanovic, R., Pandjaitan, R., Offmann, B., Cadet, F., Fischer, R., 2020, ‘One‐shot optimization of multiple enzyme parameters: Tailoring glucose oxidase for pH and electron mediators’, Biotechnol Bioeng, vol. 117, pp. 17–29. https://doi.org/10.1002/bit.27169

Parvez, M., Khan, T., 2023, ‘Applications in the field of bioinformatics, in: A Guide to Applied Machine Learning for Biologists’, Springer International Publishing, Cham, pp. 175–188. https://doi.org/10.1007/978-3-031-22206-1_7

Pence, H.E., 2020, ‘How should chemistry educators respond to the next generation of technology change?’, Educ Sci (Basel), vol. 10, no. 2, pp. 34. https://doi.org/10.3390/educsci10020034

Rabelo, L.P., Sodré, D., dos Santos, M.S., Lima, C.C.S., Ferrari, S.F., Sampaio, I., Vallinoto, M., 2022, ‘ForAlexa, an online tool for the rapid development of artificial intelligence skills for the teaching of evolutionary biology using Amazon’s Alexa’, Evolution: Education and Outreach, vol. 15, pp. 1-10. https://doi.org/10.1186/s12052-022-00169-z

Robberts, A.S., van Ryneveld, L., 2019, ‘Work in progress: Enabling learning environments for underprepared engineering students: Blending game-based and project-oriented methodologies’, in IEEE Global Engineering Education Conference (EDUCON), IEEE 2019, pp. 722–726. https://doi.org/10.1109/EDUCON.2019.8725040

Rusu, A., Bealor, M.T., Lopez, H., 2014, ‘Interdisciplinary faculty-faculty collaborations for the development of learning technologies’, in IEEE Frontiers in Education Conference (FIE) Proceedings, IEEE 2014, pp. 1–8. https://doi.org/10.1109/FIE.2014.7044282

Salsabila, E., Rahayu, W., Kharis, S., Putri, A., 2020, ‘A comparison between generative learning and conventional learning model on students’ mathematical literacy in the 21st century’, in Proceedings of the Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 2019, 12 October 2019, Bandung, West Java, Indonesia. EAI. https://doi.org/10.4108/eai.12-10-2019.2296539

Santos, A., Scanavini, H.F.A., Pedrini, H., Schiozer, D.J., Munerato, F.P., Barreto, C.E.A.G., 2022,’ An artificial intelligence method for improving upscaling in complex reservoirs’, J Pet Sci Eng, vol. 211, no. 110071. https://doi.org/10.1016/j.petrol.2021.110071

Siddique, F., Sakib, S., Siddique, Md.A.B., 2019, ‘Recognition of handwritten digit using convolutional neural network in python with tensorflow and comparison of performance for various hidden layers’, in 5th International Conference on Advances in Electrical Engineering (ICAEE), IEEE 2019, pp. 1–6. https://doi.org/10.1109/ICAEE48663.2019.8975496

Sundar, S.S., Liao, M., 2023, ‘Calling BS on ChatGPT: Reflections on AI as a communication source’, Journal Commun Monogr, vol. 25, pp. 165–180. https://doi.org/10.1177/15226379231167135

Tan, K.F., Ahmad Sabari, M., Osran, S.A., Noh, N., Ahmad Fuad, A.A., 2023, ‘Comparing blended e-learning and conventional classroom methods in teaching the basic statistics subject’, Education in Medicine Journal, vol. 15, pp. 1–15. https://doi.org/10.21315/eimj2023.15.3.1

Tariq, M.U., Poulin, M., Abonamah, A.A., 2021, ‘Achieving operational excellence through artificial intelligence: Driving forces and barriers’, Front Psychol, vol. 12, pp. 1-15. https://doi.org/10.3389/fpsyg.2021.686624

Thirugnanasambandam, K., Rajeswari, M., Bhattacharyya, D., Kim, J., 2022, ‘Directed artificial bee colony algorithm with revamped search strategy to solve global numerical optimization problems’, Automated Software Engineering, vol. 29, no. 13. https://doi.org/10.1007/s10515-021-00306-w

Usman, A., Hartati, T.A.W., 2024, ‘Analysis of “Merdeka Belajar - Kampus Merdeka“ program research in Scopus indexed journals: A critical review’, JPBI (Jurnal Pendidikan Biologi Indonesia), vol. 10, pp. 616–630. https://doi.org/10.22219/jpbi.v10i2.32576




DOI: http://dx.doi.org/10.30870/jppi.v11i1.23075

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