ViLabS: Deep learning AI and computer vision-based smart website for improving basic laboratory techniques
DOI:
https://doi.org/10.59672/ijed.v7i2.6999Keywords:
Basic laboratory technique, Computer vision, Deep learning AI, Smart websiteAbstract
To address excessive cognitive load and the lack of subject-based psychomotor evaluation in chemistry education, this study evaluates ViLabS, a deep-learning and computer vision website, for improving students' cognitive and psychomotor abilities. Using an explanatory sequential mixed-methods design, a quasi-experimental study involved 40 Grade X students selected via purposive sampling. Data were collected through cognitive tests, AI system logs, video-observation rubrics, and practicality questionnaires, followed by in-depth interviews. Results revealed ViLabS is highly feasible and practical (>92%). Cognitively, the experimental group achieved a significantly higher N-Gain (0.71) than the control (0.41) (p<0.05). Autonomously monitored psychomotor accuracy also significantly outperformed classical manual demonstrations (p<0.05). Thematic analysis confirmed the AI's instant corrective feedback minimized cognitive load and fostered precise muscle memory. Despite technical constraints such as internet and lighting dependency, ViLabS shows strong efficacy in accelerating theoretical understanding and kinesthetic proficiency. Future research should expand datasets and develop offline mobile applications. Ultimately, this study contributes a proactive, multimodal AI framework that advances the paradigm of AI-assisted laboratory learning.
Downloads
References
Abrahams, I., & Millar, R. (2008). Does practical work really work? A study of the effectiveness of practical work as a teaching and learning method in school science. International Journal of Science Education, 30(14), 1945–1969. https://doi.org/10.1080/09500690701749305
Bakti, I., Winarti, A., Mirah Surya Dewi, N. P. P., Mahdian, & Eka Sari, S. M. (2026). Profiling readiness for deep learning: A study of chemistry students’ multiple intelligences and learning reflections. Indonesian Journal of Educational Development (IJED), 6(4), 1526–1541. https://doi.org/10.59672/ijed.v6i4.5400
Campagner, A., Ciucci, D., & Cabitza, F. (2021). Ground truthing from multi-rater labeling with three-way decision and possibility theory. Information Sciences, 545, 771–790. https://doi.org/10.1016/j.ins.2020.09.049
Carney, M., Webster, B., Alvarado, I., Phillips, K., Howell, N., Griffis, J., Roberts, J., Barman, P. R., & Chen, S. Y. (2020). Teachable machine: Approachable web-based machine learning. Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 1–8. https://doi.org/10.1145/3334480.3382839
Celik, I. (2023). Towards intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468
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
Cheung, D. (2011). Teacher beliefs about implementing guided-inquiry laboratory experiments for secondary school chemistry. Journal of Chemical Education, 88(11), 1462–1468. https://doi.org/10.1021/ed1008409
Chiu, W. K. (2021). Pedagogy of emerging technologies in chemical education. Education Sciences, 11(11), 709. https://doi.org/10.3390/educsci11110709
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.1016/j.caeai.2022.100118
Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE.
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22. https://doi.org/10.1186/s41239-023-00392-8
DeVellis, R. F., & Thorpe, C. T. (2021). Scale development: Theory and applications (5th ed.). SAGE Publications.
Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). SAGE Publications.
Fukuda, K. (2020). Science, technology and innovation policy for Society 5.0. Journal of Open Innovation: Technology, Market, and Complexity, 6(2), 47. https://doi.org/10.1016/j.ijpe.2019.07.033
Gopalan, M., Rosinger, K., & Ahn, J. B. (2020). Use of quasi-experimental research designs in education research: Growth, promise, and challenges. Review of Research in Education, 44(1), 218–243. https://doi.org/10.3102/0091732X20903302
Gwet, K. L. (2014). Handbook of inter-rater reliability: The definitive guide to measuring the extent of agreement among raters (4th ed.). Advanced Analytics, LLC.
Hake, R. R. (1998). Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. American Journal of Physics, 66(1), 64–74. https://doi.org/10.1119/1.18809
Hariyono, H., Hidayati, U., & Suharto. (2026). The effect of AI-optimized project-based learning on digital pedagogical readiness. Indonesian Journal of Educational Development, 7(1), 49–64. https://doi.org/10.59672/ijed.v7i1.6145
Hidayah, R., & Nuraini, S. (2024). The effectiveness of website on improving students’ creative thinking skills in chemical bonding. AIP Conference Proceedings, 3116(1), 100014. https://doi.org/10.1063/5.0210532
Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542–570. https://doi.org/10.1111/ejed.12533
Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001
I Made Elia Cahaya, I. M. E., Suryaningsih, N. M. A., Parwata, I. M. Y., Poerwati, C. E. (2024). The influence of a guided inquiry learning model in improving students’ creative thinking abilities: A meta-analysis study. (2024). Indonesian Journal of Educational Development (IJED), 5(3), 376–384. https://doi.org/10.59672/ijed.v5i3.4211
Joshi, A., Kale, S., Chandel, S., & Pal, D. K. (2015). Likert scale: Explored and explained. British Journal of Applied Science & Technology, 7(4), 396–403. https://doi.org/10.9734/BJAST/2015/14975
Kyynäräinen, R., Vilhunen, E., Li, P.-H., Laakso, M.-J., & Vesterinen, V.-M. (2024). How making mistakes shapes students' situational engagement in chemistry laboratory?. International Journal of Science Education, 1-21. https://doi.org/10.1080/09500693.2024.2439142
Kong, Y., & Fu, Y. (2022). Human action recognition and prediction: A survey. International Journal of Computer Vision, 130(5), 1366–1401. https://doi.org/10.1007/s11263-022-01594-9
Liu, Q., Ma, J., Yu, S., Wang, Q., & Xu, S. (2023). Effects of an augmented reality-based chemistry experiential application on student knowledge gains, learning motivation, and technology perception. Journal of Science Education and Technology, 32(2), 153–167. https://doi.org/10.1007/s10956-022-10014-z
Mahsup, Syaharudin, Nepomuceno, M., & Jali, H. (2026). Effectiveness of guided inquiry learning and self-regulated learning on students' numeracy skills. Indonesian Journal of Educational Development (IJED), 6(4), 1427–1440. https://doi.org/10.59672/ijed.v6i4.5756
Mayer, R. E. (2021a). Evidence-based principles for how to design effective instructional videos. Journal of Applied Research in Memory and Cognition, 10(2), 229–240. https://doi.org/10.1016/j.jarmac.2021.03.007
Mayer, R. E. (2021b). Multimedia learning (3rd ed.). Cambridge University Press. https://doi.org/10.1017/9781316941355
Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020. https://doi.org/10.1016/j.caeai.2021.100020
Plomp, T., & Nieveen, N. (2007). An introduction to educational design research. SLO.
Popenici, S. A. D., & Kerr, S. J. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22. https://doi.org/10.1186/s41039-017-0062-8
Sasaki, R., Fujinami, M., & Nakai, H. (2024). Application of object detection and action recognition toward automated recognition of chemical experiments. Digital Discovery, 3(1), 2458–2469. https://doi.org/10.1039/D4DD00015C
Schechtel, S., & Bongers, A. (2026). The faces of failure: Understanding students' affective experiences with failure in introductory chemistry laboratory learning activities. Chemistry Education Research and Practice, 27(1), 479–493. https://doi.org/10.1039/d5rp00297d
Schober, P., & Vetter, T. R. (2020). Nonparametric statistical methods in medical research. Anesthesia & Analgesia, 131(6), 1862–1863. https://doi.org/10.1213/ANE.0000000000005101
Seery, M. K., Agustian, H. Y., Doidge, E. D., Kucharski, M. M., Owen, S., & Shirran, A. (2017). Developing laboratory skills by incorporating peer-review and digital badges. Chemistry Education Research and Practice, 18(2), 403–419. https://doi.org/10.1039/C7RP00003K
Seery, M. K. (2020). Establishing the laboratory as the place to learn how to do chemistry. Journal of Chemical Education, 97(3), 666–672. https://doi.org/10.1021/acs.jchemed.9b00764
Slavin, R. E. (2014). Educational psychology: Theory and practice (11th ed.). Pearson.
Suryaningsih, N. M. A. ., & Cahaya, I. M. E. . (2026). Enhancing early childhood science literacy through deep learning–based instruction using simple coding games . Indonesian Journal of Educational Development (IJED), 7(1), 230–243. https://doi.org/10.59672/ijed.v7i1.5924
Tharwat, A. (2021). Classification assessment methods. Applied Computing and Informatics, 17(1), 168-192. https://doi.org/10.1016/j.aci.2018.08.003
Wang, X., & Chen, Y. (2023). Deep learning-based human pose estimation for real-time action recognition in physical education. Computers & Education, 198, 104770. https://doi.org/10.1016/j.engappai.2022.105813
Weaver, M. G., Samoshin, A. V., Lewis, R. B., & Gainer, M. J. (2016). Developing students' critical thinking, problem solving, and analysis skills in an inquiry-based synthetic organic laboratory course. Journal of Chemical Education, 93(5), 847–851. https://doi.org/10.1021/acs.jchemed.5b00678
Widana, I. W., Sopandi, A. T., Suwardika, I. G. (2021). Development of an authentic assessment model in mathematics learning: A science, technology, engineering, and mathematics (STEM) approach. Indonesian Research Journal in Education, 5(1), 192-209. https://doi.org/10.22437/irje.v5i1.12992
Williams, S. R., Ziebell, A. L., Kitson, R. R. A., Coppo, P., Thompson, C. D., & Overton, T. L. (2018). ‘What do you think the aims of doing a practical chemistry course are?’ A comparison of the views of students and teaching staff across three universities. Chemistry Education Research and Practice, 19(2), 463–473. https://doi.org/10.1039/C7RP00177K
Wisniewski, B., Zierer, K., & Hattie, J. (2020). The power of feedback revisited: A meta-analysis of educational feedback research. Frontiers in Psychology, 10, 3087. https://doi.org/10.3389/fpsyg.2019.03087
Yaman, F. (2020). Pre-service science teachers' development and use of multiple levels of representation and written arguments in general chemistry laboratory courses. Research in Science Education, 50(6), 2331–2362. https://doi.org/10.1007/s11165-018-9781-0
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of AI applications in higher education. International Journal of Educational Technology in Higher Education, 16(39), 1–27. https://doi.org/10.1186/s41239-019-0171-0
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Idrus, A., & Li, Y. (2021). A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity, 2021, 1–18. https://doi.org/10.1155/2021/8812542
Zheng, C., Wu, W., Yang, T., Zhu, S., Chen, C., Liu, R., Yuan, J., Zou, C., & Shah, M. (2023). Deep learning-based human pose estimation: A survey. ACM Computing Surveys, 56(1), 1–37. https://doi.org/10.1145/3603618
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Muhammad Farhan Ivan Phalosa, Rusly Hidayah, Muhammad Zainur Rifai
This work is licensed under a Creative Commons Attribution 4.0 International License.
This is an Open Access article distributed under the terms of Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.
