International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
권호 표지
DOI QR코드

DOI QR Code

Best Practices for Implementing AI in STEM Education: A Systematic Literature Review

  • 투고 : 2024.08.05
  • 발행 : 2024.08.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Artificial intelligence (AI) describes a variety of approaches in computer applications to mimic human learning. As this technology becomes increasingly prevalent, it is inevitable that it will enter the educational environment, as both an educational tool and topic of learning. STEM education, which deals with science, technology, engineering, and math, is perhaps the most appropriate educational field in which to introduce students to this new and rapidly growing technology. In recent years, educators, AI engineers, and educational researchers have published trial results of experimental curricula implementing AI technology in student and teacher education. This systematic literature review analyzed a sample of seven such publications to identify key trends in suggested best practices for the usage of AI in STEM classrooms. The sample was analyzed for keywords using MaxQDA. The results indicated three key trends among suggested best practices. The first was that AI is best taught to students when the technology itself is the topic of education. Another trend was that simulating real world applications of AI technology was most impactful in showing students the potential, limits, and ethical implications of AI. Finally, it was found that educator's familiarity with AI is an important factor in their ability to employ it in the classroom.

키워드

참고문헌

  1. Anghileri, J. (2006). Scaffolding practices that enhance mathematics learning. Journal of Mathematics Teacher Education, 9, 33-52.
  2. Bacca Acosta, J. L., Baldiris Navarro, S. M., Fabregat Gesa, R., & Graf, S. (2014). Augmented reality trends in education: a systematic review of research and applications. Journal of Educational Technology and Society, 2014, vol. 17, num. 4, p. 133-149.
  3. Beigman Klebanov, B., Burstein, J., Harackiewicz, J. M., Priniski, S. J., & Mulholland, M. (2017). Reflective writing about the utility value of science as a tool for increasing STEM motivation and retention-can AI help scale up?. International Journal of Artificial Intelligence in Education, 27, 791-818.
  4. Casal-Otero, L., Catala, A., Fernandez-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K-12: a systematic literature review. International Journal of STEM Education, 10(1), 29.
  5. Chiu, T. K., Meng, H., Chai, C. S., King, I., Wong, S., & Yam, Y. (2021). Creation and evaluation of a pretertiary artificial intelligence (AI) curriculum. IEEE Transactions on Education, 65(1), 30-39.
  6. Chng, E., Tan, A. L., & Tan, S. C. (2023). Examining the use of emerging technologies in schools: A review of artificial intelligence and immersive technologies in STEM education. Journal for STEM Education Research, 6(3), 385-407.
  7. Cope, B., & Kalantzis, M. (2023). A little history of e-learning: finding new ways to learn in the PLATO computer education system, 1959-1976. History of Education, 52(6), 905-936.
  8. Gelo, O., Braakmann, D., & Benetka, G. (2008). Quantitative and qualitative research: Beyond the debate. Integrative psychological and behavioral science, 42, 266-290.
  9. Gibellini, G., Fabretti, V., & Schiavo, G. (2023). Ai education from the educator's perspective: Best practices for an inclusive ai curriculum for middle school. Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems.
  10. Ghosh, A., Chakraborty, D., & Law, A. (2018). Artificial intelligence in Internet of things. CAAI Transactions on Intelligence Technology, 3(4), 208-218.
  11. Gonzalez, H. B., & Kuenzi, J. J. (2012). Science, technology, engineering, and mathematics (STEM) education: A primer. Washington, DC: Congressional Research Service, Library of Congress.
  12. Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542-570.
  13. Jang, J., Jeon, J., & Jung, S. K. (2022). Development of STEM-based AI education program for sustainable improvement of elementary learners. Sustainability, 14(22), 15178.
  14. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
  15. Karampelas, A. (2021). Artificial Intelligence and Machine Learning in the STEAM classroom: Analysis of performance data and reflections of international high school students. Hellenic Journal of STEM Education, 1(2), 59-66.
  16. Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering-a systematic literature review. Information and software technology, 51(1), 7-15.
  17. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
  18. Lee, I., & Perret, B. (2022). Preparing high school teachers to integrate AI methods into STEM classrooms. In Proceedings of the AAAI conference on artificial intelligence (Vol. 36, No. 11, pp. 12783-12791).
  19. Lin, C. H., Yu, C. C., Shih, P. K., & Wu, L. Y. (2021). STEM based artificial intelligence learning in general education for non-engineering undergraduate students. Educational Technology & Society, 24(3), 224-237.
  20. Luzano, J. (2024). An Integrative Review of AI-Powered STEM Education. International Journal of Academic Pedagogical Research, 8(4), 113-118.
  21. Martin-Paez, T., Aguilera, D., Perales-Palacios, F. J., & Vilchez-Gonzalez, J. M. (2019). What are we talking about when we talk about STEM education? A review of literature. Science Education, 103(4), 799-822.
  22. Oliveira, M., Bitencourt, C., Teixeira, E., & Santos, A. C. (2013, July). Thematic content analysis: Is there a difference between the support provided by the MAXQDA® and NVivo® software packages. In Proceedings of the 12th European Conference on Research Methods for Business and Management Studies (pp. 304-314).
  23. Onwuegbuzie, A. J., Leech, N. L., & Collins, K. M. (2012). Qualitative analysis techniques for the review of the literature. Qualitative Report, 17, 56.
  24. Page, J., Bain, M., & Mukhlish, F. (2018, August). The risks of low level narrow artificial intelligence. In 2018 IEEE international conference on intelligence and safety for robotics (ISR) (pp. 1-6). IEEE.
  25. Qin, C., Zhang, A., Zhang, Z., Chen, J., Yasunaga, M., & Yang, D. (2023). Is ChatGPT a general-purpose natural language processing task solver?. arXiv preprint arXiv:2302.06476.
  26. Russell, H. R., Wutich, A., & Ryan, G. W. (2016). Analyzing qualitative data: Systematic approaches. SAGE publications.
  27. Sakulkueakulsuk, B., Witoon, S., Ngarmkajornwiwat, P., Pataranutaporn, P., Surareungchai, W., Pataranutaporn, P., & Subsoontorn, P. (2018, December). Kids making AI: Integrating machine learning, gamification, and social context in STEM education. In 2018 IEEE international conference on teaching, assessment, and learning for engineering (TALE) (pp. 1005-1010). IEEE.
  28. Sharma, R., & Mohan, M. (2022). Artificial Intelligence and E-Learning: Best Practices Across the Globe. In Adoption and Implementation of AI in Customer Relationship Management (pp. 1-25). IGI Global.
  29. Sharma, S., Rawal, R., & Shah, D. (2023). Addressing the challenges of AI-based telemedicine: Best practices and lessons learned. Journal of education and health promotion, (1), 338.
  30. Torres-Carrion, P. V., Gonzalez-Gonzalez, C. S., Aciar, S., & Rodriguez-Morales, G. (2018, April). Methodology for systematic literature review applied to engineering and education. In 2018 IEEE Global engineering education conference (EDUCON) (pp. 1364-1373). IEEE.
  31. Van Tuijl, C., & van der Molen, J. H. W. (2016). Study choice and career development in STEM fields: An overview and integration of the research. International journal of technology and design education, 26(2), 159-183.
  32. Wahono, B., Lin, P. L., & Chang, C. Y. (2020). Evidence of STEM enactment effectiveness in Asian student learning outcomes. International Journal of STEM Education, 7(1), 36.
  33. Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: a systematic review from 2011 to 2021. International Journal of STEM Education, 9(1), 59.
  34. Yannier, N., Hudson, S. E., & Koedinger, K. R. (2020). Active learning is about more than hands-on: A mixed-reality AI system to support STEM education. International Journal of Artificial Intelligence in Education, 30, 74-96.