Journal of Pedagogical Sociology and Psychology
A critical reflection on the affordances of web 3.0 and artificial intelligence in life sciences education
Lindelani Mnguni 1 *
More Detail
1 Centre for Health Sciences Education, Faculty of Health Sciences, University of the Witwatersrand, South Africa
* Corresponding Author
Open Access Full Text (PDF)
ARTICLE INFO

Journal of Pedagogical Sociology and Psychology, 2023 - Volume 5 Issue 2, pp. 1-9
https://doi.org/10.33902/jpsp.202322298

Article Type: Conceptual Article

Published Online: 19 Jul 2023

Views: 729 | Downloads: 854

ABSTRACT
Life Sciences Education has become increasingly important in today's rapidly changing world, as it equips students with the knowledge and skills needed to tackle complex global challenges in various biology fields. With the emergence of Web 3.0 and Artificial Intelligence (AI), numerous opportunities exist to revolutionize Life Sciences Education and enhance student learning. However, integrating these technologies into traditional teaching methods poses significant challenges. This paper aims to explore the opportunities and challenges of Web 3.0 and AI in Life Sciences Education and provide recommendations for successful integration. The opportunities of Web 3.0 and AI in Life Sciences Education include enhanced personalized learning, increased engagement, access to vast amounts of data, and innovative assessment strategies. However, ethical concerns related to AI, integration with traditional teaching methods, training and professional development for educators, and cost and accessibility issues are among the challenges. The paper also provides case studies of successful implementation and recommendations for addressing ethical concerns, professional development, funding and accessibility, and collaboration between educators and technology experts. The paper concludes with implications for future research and practice in Life Sciences Education.
KEYWORDS
In-text citation: (Mnguni, 2023)
Reference: Mnguni, L. (2023). A critical reflection on the affordances of web 3.0 and artificial intelligence in life sciences education. Journal of Pedagogical Sociology and Psychology, 5(2), 1-9. https://doi.org/10.33902/jpsp.202322298
In-text citation: (1), (2), (3), etc.
Reference: Mnguni L. A critical reflection on the affordances of web 3.0 and artificial intelligence in life sciences education. Journal of Pedagogical Sociology and Psychology. 2023;5(2), 1-9. https://doi.org/10.33902/jpsp.202322298
In-text citation: (1), (2), (3), etc.
Reference: Mnguni L. A critical reflection on the affordances of web 3.0 and artificial intelligence in life sciences education. Journal of Pedagogical Sociology and Psychology. 2023;5(2):1-9. https://doi.org/10.33902/jpsp.202322298
In-text citation: (Mnguni, 2023)
Reference: Mnguni, Lindelani. "A critical reflection on the affordances of web 3.0 and artificial intelligence in life sciences education". Journal of Pedagogical Sociology and Psychology 2023 5 no. 2 (2023): 1-9. https://doi.org/10.33902/jpsp.202322298
In-text citation: (Mnguni, 2023)
Reference: Mnguni, L. (2023). A critical reflection on the affordances of web 3.0 and artificial intelligence in life sciences education. Journal of Pedagogical Sociology and Psychology, 5(2), pp. 1-9. https://doi.org/10.33902/jpsp.202322298
In-text citation: (Mnguni, 2023)
Reference: Mnguni, Lindelani "A critical reflection on the affordances of web 3.0 and artificial intelligence in life sciences education". Journal of Pedagogical Sociology and Psychology, vol. 5, no. 2, 2023, pp. 1-9. https://doi.org/10.33902/jpsp.202322298
REFERENCES
  • Albreiki, B., Zaki, N., & Alashwal, H. (2021). A systematic literature review of student performance prediction using machine learning techniques. Education Sciences, 11(9), 552. https://doi.org/10.3390/educsci11090552
  • Ali, M., & Abdel-Haq, M. K. (2021). Bibliographical analysis of artificial intelligence learning in Higher Education: is the role of the human educator and educated a thing of the past? In M. B. Ali & T. Wood-Harper (Eds.), Fostering Communication and Learning with Underutilized Technologies in Higher Education (pp. 36-52). IGI Global. https://doi.org/10.4018/978-1-7998-4846-2.ch003
  • Alsariera, Y. A., Baashar, Y., Alkawsi, G., Mustafa, A., Alkahtani, A. A., & Ali, N. A. (2022). Assessment and evaluation of different machine learning algorithms for predicting student performance. Computational Intelligence and Neuroscience, 2022, 4151487. https://doi.org/10.1155/2022/4151487
  • Balakrishnan, B. (2018). Motivating engineering students learning via monitoring in a personalized learning environment with tagging system. Computer Applications in Engineering Education, 26(3), 700-710. https://doi.org/10.1002/cae.21924
  • Barata, G., Gama, S., Jorge, J., & Gonçalves, D. (2013, September). Engaging engineering students with gamification [Paper presentation]. 5th International Conference on Games and Virtual Worlds for Serious Applications (VS-GAMES), IEEE, Poole, UK.
  • Cassidy, K. C., Šefčík, J., Raghav, Y., Chang, A., & Durrant, J. D. (2020). ProteinVR: Web-based molecular visualization in virtual reality. PLoS Computational STEM, 16(3), e1007747. https://doi.org/10.1371/journal.pcbi.1007747
  • Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial Intelligence trends in education: a narrative overview. Procedia Computer Science, 136, 16-24.
  • Delgado, H. O. K., de Azevedo Fay, A., Sebastiany, M. J., & Silva, A. D. C. (2020). Artificial intelligence adaptive learning tools. BELT-Brazilian English Language Teaching Journal, 11(2), e38749-e38749. https://doi.org/10.15448/2178-3640.2020.2.38749
  • Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019). Hybrid intelligence. Business & Information Systems Engineering, 61, 637-643. https://doi.org/10.1007/s12599-019-00595-2
  • Du, S., & Xie, C. (2021). Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities. Journal of Business Research, 129, 961-974. https://doi.org/10.1016/j.jbusres.2020.08.024
  • Eddy, S. L., & Hogan, K. A. (2014). Getting under the hood: How and for whom does increasing course structure work? CBE—Life Sciences Education, 13(3), 453-468. https://doi.org/10.1187/cbe.14-03-0050
  • Ferdig, R. E., Cohen, M., Ling, E., & Hartshorne, R. (2022). Examining Blockchain Protocols, Cryptocurrency, NFTs, and Other Web 3.0 Affordances in Teacher Education. Journal of Technology and Teacher Education, 30(1), 5-19.
  • Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the national academy of Sciences, 111(23), 8410-8415. https://doi.org/10.1073/pnas.1319030111
  • Gasiewski, J. A., Eagan, M. K., Garcia, G. A., Hurtado, S., & Chang, M. J. (2012). From gatekeeping to engagement: A multi contextual, mixed method study of student academic engagement in introductory STEM courses. Research in Higher Education, 53, 229-261. https://doi.org/10.1007/s11162-011-9247-y
  • Glover, I. (2013, June). Play as you learn: gamification as a technique for motivating learners. In Edmedia+ innovate learning (pp. 1999-2008). Association for the Advancement of Computing in Education (AACE).
  • Gonzalez, H. B., & Kuenzi, J. J. (2012). Science, technology, engineering, and mathematics (STEM) education: A primer. Congressional Research Service, Library of Congress.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
  • Hiremath, B. K., & Kenchakkanavar, A. Y. (2016). An alteration of the Web 1.0, web 2.0 and Web 3.0: a comparative study. Imperial Journal of Interdisciplinary Research, 2(4), 705-710.
  • Hussain, M., Zhu, W., Zhang, W., & Abidi, S. M. R. (2018). Student engagement predictions in an e-learning system and their impact on student course assessment scores. Computational Intelligence and Neuroscience, 2018, Article ID 6347186. https://doi.org/10.1155/2018/6347186
  • Ibarra-Herrera, C. C., Carrizosa, A., Yunes-Rojas, J. A., & Mata-Gómez, M. A. (2019). Design of an app based on gamification and storytelling as a tool for STEM courses. International Journal on Interactive Design and Manufacturing (IJIDeM), 13, 1271-1282. https://doi.org/10.1007/s12008-019-00600-8
  • Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415
  • Lal, M. (2011). Web 3.0 in Education & Research. BVICAM's International Journal of Information Technology, 3(2), 16-22
  • Marshan, A., & Marshan, A. (2021). Artificial intelligence: Explainability, ethical issues, and bias. Annals of Robotics and Automation, 5(1), 34-37.
  • Medsker, L. R. (2012). Hybrid intelligent systems. Springer Science & Business Media.
  • Miranda, P., Isaias, P., & Costa, C. J. (2014). E-Learning and web generations: Towards Web 3.0 and E-Learning 3.0. International Proceedings of Economics Development and Research, 81, 92-103.
  • Mnguni, L. E. (2014). The theoretical cognitive process of visualization for science education. SpringerPlus, 3, 1-9. https://doi.org/10.1186/2193-1801-3-184
  • National Research Council. (2011). Successful K-12 Life Sciences Education: Identifying effective approaches in science, technology, engineering, and mathematics. National Academies Press.
  • Osborne, J., & Dillon, J. (2008). Science education in Europe: Critical reflections. The Nuffield Foundation.
  • Pattnayak, J., & Pattnaik, S. (2016). Integration of web services with e-learning for the knowledge society. Procedia Computer Science, 92, 155-160. https://doi.org/10.1016/j.procs.2016.07.340
  • Peirce, N., Conlan, O., & Wade, V. (2008, November). Adaptive educational games: Providing non-invasive personalized learning experiences. In M. E. Kinshuk, M. Chang, & R. McGreal (Eds.), 2008 second IEEE international conference on digital game and intelligent toy enhanced learning (pp. 28-35). IEEE. https://doi.org/10.1109/DIGITEL.2008.30
  • Pokrivčáková, S. (2019). Preparing teachers for the application of AI-powered technologies in foreign language education. Journal of Language and Cultural Education, 7(3), 135-153. https://doi.org/10.2478/jolace-2019-0025
  • Reen, F. J., Jump, O., McSharry, B. P., Morgan, J., Murphy, D., O’Leary, N., O’Mahony, B., Scallan, M., & Supple, B. (2021). The use of virtual reality in the teaching of challenging concepts in virology, cell culture, and molecular STEM. Frontiers in Virtual Reality, 2, 670909. https://doi.org/10.3389/frvir.2021.670909
  • Rodríguez, F. C., Dal Peraro, M., & Abriata, L. A. (2022). Online tools to easily build virtual molecular models for display in augmented and virtual reality on the Web. Journal of Molecular Graphics and Modelling, 114, 108164. https://doi.org/10.1016/j.jmgm.2022.108164
  • Rudman, R., & Bruwer, R. (2016). Defining Web 3.0: opportunities and challenges. The electronic library. https://doi.org/10.1108/EL-08-2014-0140
  • Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications, and research directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007%2Fs42979-021-00592-x
  • Shim, K. C., Park, J. S., Kim, H. S., Kim, J. H., Park, Y. C., & Ryu, H. I. (2003). Application of virtual reality technology in Life Sciences Education. Journal of Biological Education, 37(2), 71-74. https://doi.org/10.1080/00219266.2003.9655854
  • Sinatra, G. M., Heddy, B. C., & Lombardi, D. (2015). The challenges of defining and measuring student engagement in science. Educational Psychologist, 50(1), 1-13. https://doi.org/10.1080/00461520.2014.1002924
  • Stahl, B. C., & Wright, D. (2018). Ethics and privacy in AI and big data: Implementing responsible research and innovation. IEEE Security & Privacy, 16(3), 26-33. https://doi.org/10.1109/MSP.2018.2701164
  • Tan, M., & Hew, K. F. (2016). Incorporating meaningful gamification in a blended learning research methods class: Examining student learning, engagement, and affective outcomes. Australasian Journal of Educational Technology, 32(5), 19-34. https://doi.org/10.14742/ajet.2232
  • Tarik, A., Aissa, H., & Yousef, F. (2021). Artificial intelligence and machine learning to predict student performance during the COVID-19. Procedia Computer Science, 184, 835-840. https://doi.org/10.1016/j.procs.2021.03.104
  • Wang, Y., Liu, C., & Tu, Y. F. (2021). Factors affecting the adoption of AI-based applications in higher education. Educational Technology & Society, 24(3), 116-129.
  • 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), 1-27. https://doi.org/10.1186/s41239-019-0171-0
  • Zhang, L., Bowman, D. A., & Jones, C. N. (2019). Enabling immunology learning in virtual reality through storytelling and interactivity. In J. Y. C. Chen, & G. Fragomeni (Eds.), Virtual, Augmented, and Mixed Reality. Applications and Case Studies: 11th International Conference, VAMR 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26–31, 2019, Proceedings, Part II 21 (pp. 410-425). Springer. https://doi.org/10.1007/978-3-030-21565-1_28
LICENSE
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.