Artificial intelligence (AI) is reshaping the landscape of educational assessment, offering opportunities to move beyond standardized examinations toward adaptive, personalized, and competency-based approaches. Globally, AI-driven assessments provide real-time feedback, enable multimodal evaluation, and foster inclusivity for learners with diverse needs (Luckin et al., 2016; Holmes et al., 2022). For Fiji and the wider Pacific, these developments hold transformative potential in addressing persistent challenges such as inequitable access, limited teacher capacity, and reliance on narrow, exam-oriented systems. At the same time, the integration of AI into assessment raises critical questions about epistemology and the future of Pacific knowledge systems. Traditional learning in Fiji and the Pacific is grounded in communal, relational, and holistic epistemologies (Nabobo-Baba, 2006; Thaman, 2003), which may be marginalized if AI-driven assessment frameworks privilege Western, data-centric approaches. This paper critically examines the dual role of AI in assessment: as a tool for advancing equity, inclusivity, and educational development, and as a disruptive force that risks exacerbating the digital divide and eroding cultural epistemologies. It argues that for AI to contribute meaningfully to educational transformation in Fiji and the Pacific, policies and practices must prioritize cultural relevance, contextual adaptation, and equity-driven implementation. By aligning AI innovations with Pacific pedagogical values and development goals, educational assessment can evolve into a more inclusive, sustainable, and culturally grounded practice in the 21st century.

artificial intelligence, educational assessment, equity, epistemology, Fiji, Pacific education, educational development

1. Thaman, K. H. (2003). Decolonizing Pacific studies: Indigenous perspectives, knowledge, and wisdom in higher education. The Contemporary Pacific, 15(1), 1–17. https://doi.org/10.1353/cp.2003.0010

2. Nabobo-Baba, U. (2006). Knowing and learning: An indigenous Fijian approach. Institute of Education, University of the South Pacific.

3. Darling-Hammond, L., & Adamson, F. (2014). Beyond the bubble test: How performance assessments support 21st century learning. Jossey-Bass.

4. United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. https://sdgs.un.org/2030agenda

5. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.

6. Broadfoot, P. (2017). Assessment for learning: The future of educational assessment. Routledge.

7. Schwab, K. (2017). The Fourth Industrial Revolution. World Economic Forum.

8. Luckin, R. (2018). Enhancing learning and teaching with AI: The role of artificial intelligence in education. UCL Knowledge Lab.

9. OECD. (2019). Trends shaping education 2019. OECD Publishing. https://doi.org/10.1787/trends_edu-2019-en

10. Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.

11. Fiji Ministry of Education. (2020). Fiji education sector strategic plan 2020–2030. Suva: Government of Fiji.

12. Williamson, B., & Eynon, R. (2020). Historical perspectives on AI in education. Learning, Media and Technology, 45(2), 107–119. https://doi.org/10.1080/17439884.2020.1747025

13. Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

14. UNESCO. (2024). Pacific education policy and digital learning: Generative AI and educational opportunities. UNESCO Asia-Pacific Regional Office.

15. AI Asia Pacific Institute. (2024). The state of artificial intelligence in the Pacific Islands: Opportunities and challenges. AI Asia Pacific Institute.