Research Article

Sustainable artificial intelligence-driven classroom assessment in higher institutions: Lessons from Estonia, China, the USA, and Australia for Nigeria

Usani Joseph Ofem 1 * , Ginika Chukwujama 2
More Detail
1 Alex Ekwueme Federal University, Ikwo, Ebonyi State, NIGERIA2 University of Calabar, Calabar, Cross River State, NIGERIA* Corresponding Author
European Journal of Interactive Multimedia and Education, 5(2), July 2024, e02403, https://doi.org/10.30935/ejimed/15265
Submitted: 12 June 2024, Published: 03 October 2024
OPEN ACCESS   122 Views   110 Downloads
Download Full Text (PDF)

ABSTRACT

The advent of artificial intelligence (AI) in higher education presents unprecedented opportunities for enhancing teaching methodologies, assessment systems, and administrative efficiencies. As Nigerian higher education institutions consider integrating AI-driven assessments, this study explores the potential benefits, challenges, and strategic approaches necessary for successful implementation. Drawing from global case studies in Estonia, China, the USA, and Australia, we analyze how AI has been employed to personalize learning, streamline assessment processes, and enhance educational outcomes. The findings highlight not only the transformative potential of AI in education but also the significant challenges related to fairness, privacy, and security. The study proposes a comprehensive framework involving policy reform, infrastructure development, multi-stakeholder collaboration, and ethical considerations. By adopting these strategies, Nigerian higher education institutions can harness the benefits of AI to foster an inclusive, efficient, and innovative educational environment. This study offers insights into how AI can be strategically implemented to enhance educational systems in Nigeria, ensuring that they are sustainable, equitable, and aligned with global technological advancements.
Keywords: sustainability, artificial intelligence, assessment, environment, scalability, ethical consideration

CITATION (APA)

Ofem, U. J., & Chukwujama, G. (2024). Sustainable artificial intelligence-driven classroom assessment in higher institutions: Lessons from Estonia, China, the USA, and Australia for Nigeria. European Journal of Interactive Multimedia and Education, 5(2), e02403. https://doi.org/10.30935/ejimed/15265

REFERENCES

  1. Adams, J., & Thompson, R. (2021). Community involvement in AI projects: Ensuring social sustainability. Journal of Sustainable Technology, 12(3), 234–249.
  2. Adams, K., & Patel, S. (2023). AI and the integrity of online assessments. Journal of Educational Technology, 44(2), 112–129.
  3. Adejo, P. E., & Connolly, T. (2021). Challenges and prospects of implementing AI in examination processes in Nigerian universities. Journal of Educational Technology in Developing Nations, 17(2), 134–150.
  4. Adewumi, M. G., Olowookere, E. I., & Aliyu, H. (2021). The role of learning management systems in Nigerian higher education: A case study of Moodle implementation. Nigerian Journal of Technology and Education, 5(1), 45–60.
  5. Afolabi, M. O. (2020). Challenges and innovations in Nigerian higher education amid population growth. Journal of African Higher Education, 22(3), 100–115.
  6. Baker, R., & Smith, L. (2019). Adaptive learning and educational AI: Applications and implications. Journal of Interactive Learning Research, 30(4), 409–425.
  7. Baker, S., & Green, H. (2020). Efficiency in AI-driven educational technologies: Evidence from randomized controlled trials. Journal of Educational Psychology, 112(3), 544–556. https://doi.org/10.1037/edu0000391
  8. Barrera, C., & Reyes, S. (2022). AI in inclusive education: Breaking barriers for disabled learners. Journal of Modern Education Review, 12(1), 45–59.
  9. Brooks, J. (2021). Personalized learning through AI: A case study approach. International Journal of Educational Research, 39(4), 34–48.
  10. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company.
  11. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 1–15.
  12. Chen, C. M., & Hwang, G. J. (2020). An AI approach to enhancing and personalizing learning experiences in smart learning environments. Smart Learning Environments, 7, Article 5.
  13. Chukwuemeka, E. J., Nzeadibe, T. C., & Ajaero, C. K. (2019). Diversity and equity challenges in Nigerian universities: Strategies for inclusion. International Journal of Inclusive Education, 23(7), 748–764. https://doi.org/10.1080/13603116.2019.1622804
  14. Chukwuma, G. (2022). Data security in digital education: Emerging challenges. Nigerian Journal of Educational Technology, 10(1), 76–88.
  15. Davis, M., & Thompson, R. (2023). Virtual reality in education: Case studies on immersive learning. Journal of Interactive Learning, 25(1), 65–83.
  16. Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56–62. https://doi.org/10.1145/2844110
  17. Dike, V. E. (2022). AI innovations in Nigerian education: Emerging trends and challenges. African Journal of Computing & ICT, 15(2), 30–42.
  18. Ezekwesili, U. O. (2021). Advanced cheating techniques in Nigerian higher education: The rising menace and solutions. Journal of Academic Ethics, 19(4), 345–359. https://doi.org/10.1007/s10805-021-09394-5
  19. Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2019). The (im)possibility of fairness: Different value systems require different mechanisms for fair decision making. Communications of the ACM, 62(2), 136–143. https://doi.org/10.1145/3287560
  20. Goel, A. K., & Polepeddi, L. (2016). Jill Watson: A virtual teaching assistant for online education. Georgia Institute of Technology. http://hdl.handle.net/1853/59104
  21. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), Article 93. https://doi.org/10.1145/3236009
  22. Gunning, D., & Aha, D. W. (2019). DARPA’s explainable artificial intelligence (XAI) program. AI Magazine, 40(2), 44–58. https://doi.org/10.1609/aimag.v40i2.2850
  23. Herold, B. (2021). Estonia’s digital education success story: AI-enhanced teaching. Education Week. https://www.edweek.org/technology/estonias-digital-education-success-story-ai-enhanced-teaching
  24. Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher-AI complementarity. Journal of Learning Analytics, 6(2), 27–52. https://doi.org/10.18608/jla.2019.62.5
  25. Jenkins, R., & Hamilton, M. (2021). Leveraging AI for predictive analytics in higher education. Educational Technology & Society, 24(2), 97–110.
  26. Johnson, D. (2020). Sustainable technology deployment: Key concepts and considerations for policy. Technology in Society, 62, Article 101383. https://doi.org/10.1016/j.techsoc.2020.101383
  27. Johnson, L. (2021). Accuracy and reliability in AI-based assessment: A new paradigm. Educational Technology Research and Development, 69(1), 105–123.
  28. Johnson, L., & Turner, G. (2020). Scalability and economic viability of artificial intelligence in education. Journal of Educational Technology Systems, 48(4), 490–508.
  29. Jones, B., & Lee, H. (2022). Augmented reality in higher education: Trends and applications. Educational Technology & Society, 24(2), 204–219.
  30. Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165, 633–705.
  31. Lee, J., & Nguyen, H. (2021). Scalability of AI in education: Challenges and opportunities. Journal of AI and Education, 2(1), 34–49.
  32. Lee, S., & Kim, J. (2022a). Energy efficient AI: Practices in green computing. Computers & Environment, 45(2), 130–145.
  33. Lee, S., & Kim, J. (2022b). Maximizing operational efficiency with AI in higher education. Education and Information Technologies, 27(1), 1345–1360.
  34. Lipton, Z. C. (2018). The mythos of model interpretability. Communications of the ACM, 61(10), 36–43. https://doi.org/10.1145/3233231
  35. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.
  36. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), Article 115. https://doi.org/10.1145/3457607
  37. Morrison, T., & Zimmerman, E. (2022). Accessible AI in education: Opportunities for innovation. Journal of Technology in Education, 15(2), 200–215.
  38. National Universities Commission. (2022). Annual report on university education in Nigeria. NUC Publications.
  39. Nwosu, A. C., & Oyelere, S. S. (2021). AI and fairness in assessment: Exploring the ethical dimensions in sub-Saharan African contexts. Ethics and Information Technology, 23(1), 45–58.
  40. O’Donnell, A. (2022). The power of predictive analytics in education. Educational Researcher, 51(1), 22–34.
  41. O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
  42. O’Reilly, T. (2021a). Economic sustainability of AI technologies in emerging markets. Emerging Economy Studies, 7(1), 22–38.
  43. O’Reilly, T. (2021b). Understanding total cost of ownership in AI implementations. Tech Management Review, 15(3), 42–56.
  44. Obioma, G. N., & Adeniji, I. A. (2021). Resource challenges and management strategies in Nigerian universities. Journal of Educational Management and Leadership, 12(1), 54–70.
  45. Okafor, U., & Emeka, B. (2023). Leveraging AI for educational policy insights. African Journal of Educational Management, 35(3), 410–425.
  46. Okeke, R. I. (2022a). Addressing higher education growth in Nigeria with AI-driven solutions. African Journal of Educational Technology, 20(4), 110–125.
  47. Okeke, R. I. (2022b). Economic and infrastructural barriers to technology integration in Nigerian higher education. African Journal of Educational Studies, 19(1), 77–93.
  48. Olagunju, A., Adekola, P. O., & Oyelere, S. S. (2020). ICT integration in Nigerian higher education: An analysis of case studies. Information Technology and Development, 26(2), 189–207.
  49. Patel, S., & Wang, F. (2022). Algorithmic bias in education: Challenges and solutions for ethical AI. AI & Society, 37(1), 123–136.
  50. Polonetsky, J., Tene, O., & Jerome, J. (2015). Beyond the common rule: Ethical structures for data research in non-academic settings. Columbia Science and Technology Law Review, 17.
  51. Prinsloo, P., & Slade, S. (2017). Big data, higher education and learning analytics: Beyond justice, towards an ethics of care. In B. Kei Daniel (Ed.), Big data and learning analytics in higher education (pp. 109–124). Springer. https://doi.org/10.1007/978-3-319-06520-5_8
  52. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). ACM. https://doi.org/10.1145/2939672.2939778
  53. Roman, R., Zhou, J., & Lopez, J. (2013). On the features and challenges of security and privacy in distributed internet of things. Computer Networks, 57(10), 2266–2279. https://doi.org/10.1016/j.comnet.2012.12.018
  54. Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Wiley.
  55. Smith, J., & Doe, A. (2022). Personalized learning through AI: Impact on academic achievement. International Review of Education Technology, 18(2), 200–215.
  56. Smith, J., & White, P. (2023). Biometric technologies in examination security. International Journal of Educational Integrity, 19(1), 22–37.
  57. Smith, M. (2023a). Evaluating ROI in educational technology investments. Journal of Higher Education Policy and Management, 45(2), 198–213.
  58. Smith, M. (2023b). Innovative cooling technologies in data centers using AI. Journal of Green Engineering, 17(1), 88–104.
  59. Tailor, J., & Kumar, P. (2021). Adaptable learning: The role of AI in personalizing education. AI in Education Journal, 3(4), 58–74.
  60. Williamson, B. (2020a). Decoding AI in education: Critical questions for a new frontier in learning. Columbia University Press.
  61. Williamson, B. (2020b). Smarter balances: Automated essay scoring. University of Edinburgh.
  62. Williamson, B. (2021). The future of education or a digital apocalypse: Advocating AI in education. Learning, Media and Technology.
  63. 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, Article 39. https://doi.org/10.1186/s41239-019-0171-0
  64. Zhang, J. (2019). Squirrel AI: Revolutionizing education with personalized learning. TechCrunch. https://techcrunch.com/2019/education-innovation
  65. Zhang, R., & Choi, T. Y. (2022a). Risk management strategies for AI projects in academia. Risk Management in Higher Education, 17(1), 75–93. https://doi.org/10.1111/rmir.12156
  66. Zhang, R., & Choi, T. Y. (2022b). Towards green AI: Sustainable machine learning algorithms. Journal of Artificial Intelligence Research, 69, 805–832.
  67. Zhao, Y. (2021). AI and educational administration: Streamlining for the future. Journal of Educational Administration, 59(5), 620–635.