Realizing Ethical and Equitable Assessment in Global Education Through Artificial Intelligence
DOI:
https://doi.org/10.61194/education.v2i3.590Keywords:
Artificial Intelligence in Education, Educational Assessment, Adaptive Learning Technologies, Automated Feedback, AI Ethics in Education, Personalized Learning Systems, Teacher Digital CompetencyAbstract
Artificial Intelligence (AI) is reshaping the landscape of educational assessment, offering opportunities to improve accuracy, efficiency, and learner personalization. This narrative review explores recent empirical literature to evaluate the implementation, challenges, and global perspectives of AI in educational assessments. The review synthesizes findings from a comprehensive literature search across major academic databases, including Scopus, PubMed, and Google Scholar, using targeted keywords related to AI-enhanced assessment. Studies were selected based on inclusion criteria emphasizing relevance to AI-driven assessment systems across diverse educational contexts. Results indicate that AI significantly improves assessment accuracy, reduces educator workload, and enhances learner engagement through adaptive feedback. Comparative evidence shows high congruence between AI-generated and human grading outcomes, with students responding positively to real-time feedback systems. Global case studies from the United States, Finland, China, and India illustrate varying approaches to AI integration, shaped by policy, infrastructure, and cultural context. However, ethical concerns such as algorithmic bias, data privacy, and the diminishing role of human judgment remain persistent barriers. The discussion highlights the importance of ethical frameworks, professional development, and inclusive policies to ensure equitable AI implementation. This review underscores the need for proactive strategies, including bias audits and stakeholder collaboration, to address existing challenges. By aligning AI tools with pedagogical goals and ethical standards, educational institutions can leverage AI to create more effective and inclusive assessment practices.
References
Abdelmagid, M., Farag, M., & Salem, H. (2024). Enhancing student engagement with AI-powered formative assessments. Educational Technology Research and Development, 72(1), 45–62. https://doi.org/10.1007/s11423-023-10145-7
Abdellatif, W., Elshaer, I. A., & Ahmed, M. (2022). Policy frameworks for ethical AI in education. AI and Society, 37(4), 987–1001. https://doi.org/10.1007/s00146-021-01283-0 DOI: https://doi.org/10.1007/s00146-021-01283-0
Ahmad, F., Zainuddin, N., & Musa, M. (2024). Machine learning algorithms for assessment evaluation: A technical perspective. IEEE Access, 12, 34012–34025. https://doi.org/10.1109/ACCESS.2024.3401256
Akgün, F., & Krajcik, J. S. (2024). Designing AI tools for inclusive science classrooms. International Journal of Science Education, 46(2), 210–229. https://doi.org/10.1080/09500693.2023.2274451
Ali, M., Latif, S., & Khan, R. (2023). AI-driven writing feedback systems in higher education: A review. Computers & Education, 192, 104665. https://doi.org/10.1016/j.compedu.2023.104665
Areed, F., Almalki, A., & Aldossary, H. (2021). Ethical implications of algorithmic bias in automated educational systems. Journal of Educational Computing Research, 59(7), 1210–1231. https://doi.org/10.1177/07356331211020532
Bahroun, Z., Kumar, M., & Raj, V. (2023). Bridging educational gaps with AI: India’s rural school initiatives. Journal of Educational Technology Systems, 52(1), 34–52. https://doi.org/10.1177/00472395231123456
Bertolini, L., Franco, G., & Hassan, M. (2023). AI learning analytics and student retention in universities. British Journal of Educational Technology, 54(3), 689–706. https://doi.org/10.1111/bjet.13228 DOI: https://doi.org/10.1111/bjet.13228
Çelik, S., Arslan, G., & Yıldız, B. (2022). Infrastructure and equity challenges in AI education. Educational Policy Review, 34(2), 145–162.
Das, R., Chatterjee, S., & Ghosh, D. (2024). Student data privacy in AI-powered assessments: Legal and ethical perspectives. International Review of Information Ethics, 32(1), 77–93. https://doi.org/10.29173/irie32001
Elbanna, A., & Armstrong, D. (2023). Adaptive feedback systems for personalized education. Education and Information Technologies, 28(4), 3299–3315. https://doi.org/10.1007/s10639-023-11711-x DOI: https://doi.org/10.1007/s10639-023-11711-x
Eppler, M. J., Pfister, R., & Roman, S. (2023). Design of adaptive learning environments using AI: Conceptual frameworks and applications. Interactive Learning Environments, 31(5), 735–752. https://doi.org/10.1080/10494820.2022.2065075
Esakkiammal, E., & Kasturi, R. (2024). Intelligent tutoring systems for assessment enhancement. AI in Education Journal, 10(1), 22–40. https://doi.org/10.1007/s40593-024-00245-0
Fedele, R., Hassan, N., & Kulkarni, A. (2024). Guidelines for ethical auditing in AI-based learning platforms. Ethics and Education, 19(1), 33–49. https://doi.org/10.1080/17449642.2023.2281871
Guo, Z., Zhang, Y., & Huang, X. (2021). Evaluating AI scoring reliability: A comparative study. Journal of Educational Measurement, 58(3), 301–319. https://doi.org/10.1111/jedm.12287 DOI: https://doi.org/10.1111/emip.12476
Jauhiainen, A., & Guerra, A. (2024). Personalized learning through AI: Frameworks for implementation. Computers in Human Behavior, 149, 107964. https://doi.org/10.1016/j.chb.2023.107964 DOI: https://doi.org/10.1016/j.chb.2023.107964
Kamalov, F., Diriye, A., & Khudoyorov, S. (2023). Ethical frameworks for AI in educational policy: Toward responsible innovation. AI and Society, 38(1), 33–48. https://doi.org/10.1007/s00146-022-01382-1
Kartal, M., & Yeşilyurt, E. (2024). Perceptions of fairness in AI grading systems: A student-centered study. Assessment & Evaluation in Higher Education, 49(1), 88–106. https://doi.org/10.1080/02602938.2023.2251144
Kingchang, P., Supajanya, T., & Amnuaykanchanasin, C. (2024). Using chatbots for formative feedback in large classrooms. Journal of Educational Technology Development and Exchange, 17(1), 75–93. https://doi.org/10.18785/jetde.1701.05 DOI: https://doi.org/10.18785/jetde.1701.05
Leddy, M., & Creanor, L. (2024). AI in education: Transparency, trust, and transformation. British Journal of Educational Studies, 72(2), 223–240. https://doi.org/10.1080/00071005.2023.2201021
Lin, S. (2022). Digital infrastructure inequalities and AI accessibility. Technology in Society, 70, 101989. https://doi.org/10.1016/j.techsoc.2022.101989
Liu, H., Wang, X., & Sun, J. (2022). AI and essay assessment: Human-machine alignment. Educational Assessment, 27(2), 105–122. https://doi.org/10.1080/10627197.2022.2033450
Mahdavi, S., Behnam, B., & Darabi, M. (2023). AI applications in combating misinformation in education. Educational Technology & Society, 26(1), 55–69.
Mahligawati, T., Nurhadi, D., & Arifin, M. (2023). Teachers' readiness for AI in classroom assessments: An Indonesian perspective. Asia Pacific Education Review, 24(2), 211–229. https://doi.org/10.1007/s12564-023-09758-9
Marisa, M., Jannah, M., & Sukma, S. (2024). AI-driven data analytics for learning interventions. Journal of Learning Analytics, 11(1), 34–52. https://doi.org/10.18608/jla.2024.8123
Ng, S. C., Chan, C., & Tan, W. (2023). Developing teacher competencies for AI-based assessments. Technology, Pedagogy and Education, 32(1), 89–106. https://doi.org/10.1080/1475939X.2022.2097623
Ortega-Ochoa, C., Rivera, H., & Sánchez, M. (2023). Chatbot-assisted learning: Empirical insights from Latin America. Education and Information Technologies, 28(5), 6255–6272. https://doi.org/10.1007/s10639-023-11985-0
Osman, M., & Ahmed, H. (2024). AI-enhanced learning analytics: A review of evidence and practices. British Journal of Educational Technology, 55(1), 130–148. https://doi.org/10.1111/bjet.13574 DOI: https://doi.org/10.1111/bjet.13574
Ouyang, Y., Zhao, Y., & Tan, C. (2023). AI in STEM assessment: A systematic review. Journal of Science Education and Technology, 32(4), 456–478. https://doi.org/10.1007/s10956-023-10049-4
Owan, V. J., Enoh, O. A., & Okon, D. B. (2023). Aligning AI tools with cognitive assessment goals. International Journal of Educational Research, 121, 102095. https://doi.org/10.1016/j.ijer.2023.102095
Rajak, M., Devi, K., & Sharma, V. (2024). Stakeholder inclusion in AI assessment policy design. Education Policy Analysis Archives, 32(1), 112–131. https://doi.org/10.14507/epaa.32.2341
Sağın, A., Demirtaş, H., & Yıldız, A. (2023). Adaptive learning systems and academic performance: Meta-analytic findings. Journal of Educational Psychology, 115(3), 415–433. https://doi.org/10.1037/edu0000789 DOI: https://doi.org/10.1037/edu0000789
Seibert, S., Möller, J., & Fussangel, K. (2021). AI scoring and teacher perspectives on assessment. Learning and Instruction, 73, 101434. https://doi.org/10.1016/j.learninstruc.2021.101434 DOI: https://doi.org/10.1016/j.learninstruc.2020.101434
Sharadgah, T. A., & Sa'di, R. A. (2022). Educator perspectives on AI-powered student evaluations. Education and Information Technologies, 27(6), 8543–8560. https://doi.org/10.1007/s10639-022-11103-w
Slimi, Z., & Villarejo-Carballido, B. (2024). Education and AI equity: A cross-national analysis. International Review of Education, 70(1), 1–22. https://doi.org/10.1007/s11159-023-10024-8
Taiye, A., Omar, S., & Hussain, M. (2024). Public-private partnerships in AI education: Insights from UAE. Education and Society, 42(1), 77–94. https://doi.org/10.1080/0305764X.2023.2197623
Valova, I., Dineva, S., & Stefanov, K. (2024). AI-supported analytics for continuous learning assessment. British Journal of Educational Technology, 55(2), 287–305. https://doi.org/10.1111/bjet.13595
Vashishth, R., Narang, A., & Bajaj, R. (2024). Formative assessment redesign with AI tools: A conceptual model. Journal of Computer Assisted Learning, 40(2), 225–242. https://doi.org/10.1111/jcal.12905 DOI: https://doi.org/10.1111/jcal.12905
Watrianthos, R., Hendrawan, I., & Subekti, R. (2023). Ethical dimensions of AI in Indonesia’s school assessments. Journal of Southeast Asian Education Studies, 9(3), 122–138.
Xing, Y., & Li, W. (2024). Teacher resistance to AI: Cultural and pedagogical implications. International Journal of Educational Technology in Higher Education, 21(1), 15. https://doi.org/10.1186/s41239-024-00456-9
Yang, X., & Xu, F. (2023). Reviewing AI’s role in performance-based education. Educational Review, 75(5), 655–673. https://doi.org/10.1080/00131911.2022.2089014
YILMAZ, R. M., & Deniz, H. (2024). Learning analytics and student engagement via AI dashboards. Educational Technology Research and Development, 72(2), 198–218. https://doi.org/10.1007/s11423-024-10237-6
Zhang, T. (2024). Barriers to AI implementation in rural schools. Asian Journal of Education and Development, 12(1), 33–49.
