RETHINKING ASSESSMENT: DIFFERENTIATED INSTRUCTION IN THE AI ERA
Main Article Content
Abstract
This article re-examines the role of assessment within the rapidly evolving landscape of artificial intelligence (AI), focusing specifically on differentiated instruction. As AI tools become more integrated into education, traditional, one-size-fits-all assessment methods are proving to be inadequate for capturing the full scope of student learning. This paper argues for a paradigm shift, urging educators to rethink assessment by embracing a differentiated approach where students can demonstrate their mastery through diverse and creative products. We explore how AI can serve as a powerful ally in this process, offering new ways to automate feedback, analyze complex student outputs, and provide personalized support that goes beyond conventional grading. However, this shift also presents significant challenges, including concerns about academic integrity and the ethical use of AI. By reviewing current literature and proposing a forward-looking framework, this paper highlights the potential of AI to not only streamline the assessment process but also cultivate a more equitable and student-centered learning environment.
Keywords
artificial intelligence (AI), differentiated instruction, assessment products, literature, framework
Article Details
References
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Khlaif, Z. N., Alkouk, W. A., Salama, N., & Abu Eideh, B. (2025). Redesigning assessments for AI-enhanced learning: A framework for educators in the generative AI era. Education Sciences, 15(2), 174. https://doi.org/10.3390/educsci15020174
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Maity, S., & Deroy, A. (2024). The future of learning in the age of generative AI: Automated question generation and assessment with large language models. arXiv. https://arxiv.org/abs/2410.09576
Manokore, K., Manokore, A. S., & Chigora, T. B. (2025). Educational assessments in the age of generative AI: Transforming Educational Assessment in Africa - Opportunities and challenges of generative AI. In P. Wachira, X. Liu, & S. Koc (Eds.), Educational assessments in the age of generative AI (pp. 59–92). IGI Global. https://doi.org/10.4018/979-8-3693-6351-5.ch003
Matheis, P., & John, J. J. (2024). Reframing assessments: Designing authentic assessments in the age of generative AI. In S. Mahmud (Ed.), Academic integrity in the age of artificial intelligence (pp. 139–161). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-0240-8.ch008
Naik, S., Shukla, P., Obi, I., Backus, J., Rasche, N., & Parsons, P. C. (2025). Tracing the invisible: Understanding students’ judgment in AI-supported design work. In Proceedings of the 2025 Conference on Creativity and Cognition (pp. 438–442). Association for Computing Machinery. https://doi.org/10.1145/3698061.3734399
Ning, Y., Zhang, C., Xu, B., Zhou, Y., & Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the relationship between knowledge elements. Sustainability, 16(3), 978. https://doi.org/10.3390/su16030978
O’Riordan, F., Thangaraj, J., Girme, P., & Ward, M. (2025). Interactive oral assessment: Staff perceptions, challenges and benefits of this robust, authentic assessment design approach. Innovations in Education and Teaching International, 63(2), 494-507. https://doi.org/10.1080/14703297.2025.2477160
Ruslim, M. I., & Khalid, F. (2024). The use of artificial intelligence in differentiated instruction classrooms. International Journal of Academic Research in Business and Social Sciences, 14(8), 680–695. https://dx.doi.org/10.6007/IJARBSS/v14-i8/22435
Silmi, T. A., Lubis, A., & Maulidi, A. (2025). Model differentiated learning in 21st century education: A systematic review of strategies, results, and challenges. Al Ulya: Jurnal Pendidikan Islam, 10(1), 60–80. https://doi.org/10.32665/alulya.v10i1.3572
Spyropoulou, E., Wallace, M., & Poulopoulos, V. (2025). Differentiated education using technology in junior and high school classrooms. Encyclopedia, 5(2), 71. https://doi.org/10.3390/encyclopedia5020071
Tensen, D., Grainger, P., & Graham, W. (2026). Using AI to generate formative feedback in doctoral education. Assessment & Evaluation in Higher Education, 51(3), 476-492. https://doi.org/10.1080/02602938.2025.2536558
Thomas, P. (2025). AI-driven assessment systems: Reducing teacher workload in grading and feedback. https://www.researchgate.net/publication/396245710_AI-Driven_Assessment_Systems_Reducing_Teacher_Workload_in_Grading_and_Feedback
Todeschini, B., Sollberger, E., & Hugo, A. (2023). E-portfolios for student assessment in the age of artificial intelligence. In Proceedings of 16th Annual International Conference of Education, Research and Innovation (p. 76). Academic Conferences International. https://doi.org/10.21125/iceri.2023.2144
Tomisu, H., Ueda, J., & Yamanaka, T. (2025). The cognitive mirror: A framework for AI-powered metacognition and self-regulated learning. Frontiers in Education, 10, Article 1697554. https://doi.org/10.3389/feduc.2025.1697554
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Tulsyan, A. (2025). Every teacher’s ally: Harnessing AI for equity and excellence. Observer Research Foundation. https://www.orfonline.org/expert-speak/every-teacher-s-ally-harnessing-ai-for-equity-and-excellence
UNESCO. (2021). AI and education: Guidance for policy-makers. UNESCO Publishing. https://doi.org/10.54675/PCSP7350
University of Chicago. (2024). Academic technology solutions. https://academictech.uchicago.edu/
U.S. Department of Education, Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf
Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence on students’ academic development. Education Sciences, 15(3), 343. https://doi.org/10.3390/educsci15030343
Williams, A. (2025). Integrating artificial intelligence into higher education assessment. Intersection: A Journal at the Intersection of Assessment and Learning, 6(1), 128-154. https://doi.org/10.61669/001c.131915
World Economic Forum. (2023). The future of jobs report 2023. https://www.weforum.org/publications/the-future-of-jobs-report-2023/
Ye, L. (2025). AI-driven personalized learning path optimization and adaptive resource recommendation for foreign language education. In R. Su (Ed.), Proceedings of the Seventh International Conference on Image, Video Processing, and Artificial Intelligence (Vol. 13731, Article 137311B). SPIE. https://doi.org/10.1117/12.3076209
Zhong, R., & Zhao, Y. (2025). Paradigm shifts in education: An ecological analysis. ECNU Review of Education, 8(1), 21-40. https://doi.org/10.1177/20965311241296162
Alexandrowicz, V. (2024). Artificial intelligence integration in teacher education: Navigating benefits, challenges, and transformative pedagogy. Journal of Education and Learning, 13(6), 346–364. https://doi.org/10.5539/jel.v13n6p346
Arqam, & Asrifan, A. (2024). Integrating AI in project-based learning for differentiated English language instruction: A scoping review. Journal of English Education and Teaching, 8(3), 586–608. https://doi.org/10.33369/jeet.8.3.586-608
Awadallah Alkouk, W., & Khlaif, Z. N. (2024). AI-resistant assessments in higher education: Practical insights from faculty training workshops. Frontiers in Education, 9, Article 1499495. https://doi.org/10.3389/feduc.2024.1499495
Bagida Urazaliyevna, Y. (2025). Enhancing differentiated instruction: how ai tools support personalized lesson planning for diverse learners. Hamkor Konferensiyalar, 1(20), 108–114. https://academicsbook.com/index.php/konferensiya/article/view/2700
Boateng, O., & Boateng, B. (2025). Algorithmic bias in educational systems: Examining the impact of AI-driven decision making in modern education. World Journal of Advanced Research and Reviews, 25(1), 2012–2017. https://doi.org/10.30574/wjarr.2025.25.1.0253
Borović, F., Kovačić, M., & Aleksić-Maslać, K. (2025, July). The use of the Gen AI tool Brisk Teaching in the educational process and its impact on student motivation [Paper presentation]. 48th International Convention on Information, Communication and Electronic Technology (MIPRO 2025), Opatija, Croatia. https://doi.org/10.1109/MIPRO65660.2025.11131911
Bouchrika, I. (2026, March 14). Differentiated instruction: Definition, examples & strategies for the classroom for 2026. Research.com. https://research.com/education/differentiated-instruction
Dagunduro, A. O., Chikwe, C. F., Ajuwon, O. A., & Ediae, A. A. (2024). Adaptive learning models for diverse classrooms: Enhancing educational equity. International Journal of Applied Research in Social Sciences, 6(9), 2228–2240. https://doi.org/10.51594/ijarss.v6i9.1588
Damyanov, K. (2024). Differentiation of educational content through artificial intelligence systems in inclusive education. International Journal of Education, 12(3), 13-20. https://doi.org/10.5121/ije2024.12302
Fajardo-Ramos, D. C., Andrés, C., & Mella-Norembuena, J. (2025). Human-in-the-loop assessment with AI: Implications for teacher education in Ibero-American universities. Frontiers in Education, 10, Article 1710992. https://doi.org/10.3389/feduc.2025.1710992
Farag, W. A., Nadeem, M., & Helal, M. (2024). Assessment transformation in the age of AI: Moving beyond the influence of generative tools. In Proceedings of the 2024 Mediterranean Smart Cities Conference (MSCC) (pp. 1–6). IEEE. https://doi.org/10.1109/MSCC62288.2024.10697011
Florida International University - Center for the Advancement of Teaching. (2025). Design strategies for assessing learning with AI. https://cat.fiu.edu/resources/teaching-with-ai/design-strategies-for-assessing-learning-with-ai/
Godor, B. P. (2021). The many faces of teacher differentiation: Using Q methodology to explore teachers’ preferences for differentiated instruction. The Teacher Educator, 56(1), 43–60. https://doi.org/10.1080/08878730.2020.1785068
Gonsalves, C. (2025). Contextual assessment design in the age of generative AI. Journal of Learning Development in Higher Education, 34, 1-14. https://files.eric.ed.gov/fulltext/EJ1464858.pdf
Hafdi, Z. S., & El Kafhali, S. (2025). A comparative evaluation of machine learning methods for predicting student outcomes in coding courses. AppliedMath, 5(2), 75. https://doi.org/10.3390/appliedmath5020075
Iqbal, M., Khan, N. U., & Imran, M. (2024). The role of artificial intelligence in transforming educational practices: Opportunities, challenges, and implications. Qlantic Journal of Social Sciences, 5(2), 348–359. https://doi.org/10.55737/qjss.349319430
Khlaif, Z. N., Alkouk, W. A., Salama, N., & Abu Eideh, B. (2025). Redesigning assessments for AI-enhanced learning: A framework for educators in the generative AI era. Education Sciences, 15(2), 174. https://doi.org/10.3390/educsci15020174
Lewis, H., Johnson, S. M., & Phillips, E. (2024). Redefining student achievement: Authentic assessment in the era of artificial intelligence. In L. Marron (Ed.), Cases on authentic assessment in higher education (pp. 17–36). IGI Global. https://doi.org/10.4018/979-8-3693-1001-4.ch002
Maity, S., & Deroy, A. (2024). The future of learning in the age of generative AI: Automated question generation and assessment with large language models. arXiv. https://arxiv.org/abs/2410.09576
Manokore, K., Manokore, A. S., & Chigora, T. B. (2025). Educational assessments in the age of generative AI: Transforming Educational Assessment in Africa - Opportunities and challenges of generative AI. In P. Wachira, X. Liu, & S. Koc (Eds.), Educational assessments in the age of generative AI (pp. 59–92). IGI Global. https://doi.org/10.4018/979-8-3693-6351-5.ch003
Matheis, P., & John, J. J. (2024). Reframing assessments: Designing authentic assessments in the age of generative AI. In S. Mahmud (Ed.), Academic integrity in the age of artificial intelligence (pp. 139–161). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-0240-8.ch008
Naik, S., Shukla, P., Obi, I., Backus, J., Rasche, N., & Parsons, P. C. (2025). Tracing the invisible: Understanding students’ judgment in AI-supported design work. In Proceedings of the 2025 Conference on Creativity and Cognition (pp. 438–442). Association for Computing Machinery. https://doi.org/10.1145/3698061.3734399
Ning, Y., Zhang, C., Xu, B., Zhou, Y., & Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the relationship between knowledge elements. Sustainability, 16(3), 978. https://doi.org/10.3390/su16030978
O’Riordan, F., Thangaraj, J., Girme, P., & Ward, M. (2025). Interactive oral assessment: Staff perceptions, challenges and benefits of this robust, authentic assessment design approach. Innovations in Education and Teaching International, 63(2), 494-507. https://doi.org/10.1080/14703297.2025.2477160
Ruslim, M. I., & Khalid, F. (2024). The use of artificial intelligence in differentiated instruction classrooms. International Journal of Academic Research in Business and Social Sciences, 14(8), 680–695. https://dx.doi.org/10.6007/IJARBSS/v14-i8/22435
Silmi, T. A., Lubis, A., & Maulidi, A. (2025). Model differentiated learning in 21st century education: A systematic review of strategies, results, and challenges. Al Ulya: Jurnal Pendidikan Islam, 10(1), 60–80. https://doi.org/10.32665/alulya.v10i1.3572
Spyropoulou, E., Wallace, M., & Poulopoulos, V. (2025). Differentiated education using technology in junior and high school classrooms. Encyclopedia, 5(2), 71. https://doi.org/10.3390/encyclopedia5020071
Tensen, D., Grainger, P., & Graham, W. (2026). Using AI to generate formative feedback in doctoral education. Assessment & Evaluation in Higher Education, 51(3), 476-492. https://doi.org/10.1080/02602938.2025.2536558
Thomas, P. (2025). AI-driven assessment systems: Reducing teacher workload in grading and feedback. https://www.researchgate.net/publication/396245710_AI-Driven_Assessment_Systems_Reducing_Teacher_Workload_in_Grading_and_Feedback
Todeschini, B., Sollberger, E., & Hugo, A. (2023). E-portfolios for student assessment in the age of artificial intelligence. In Proceedings of 16th Annual International Conference of Education, Research and Innovation (p. 76). Academic Conferences International. https://doi.org/10.21125/iceri.2023.2144
Tomisu, H., Ueda, J., & Yamanaka, T. (2025). The cognitive mirror: A framework for AI-powered metacognition and self-regulated learning. Frontiers in Education, 10, Article 1697554. https://doi.org/10.3389/feduc.2025.1697554
Tucker, C. (2024). 5 tips for designing AI-resistant tasks. https://catlintucker.com/2024/10/ai-resistant-tasks/
Tulsyan, A. (2025). Every teacher’s ally: Harnessing AI for equity and excellence. Observer Research Foundation. https://www.orfonline.org/expert-speak/every-teacher-s-ally-harnessing-ai-for-equity-and-excellence
UNESCO. (2021). AI and education: Guidance for policy-makers. UNESCO Publishing. https://doi.org/10.54675/PCSP7350
University of Chicago. (2024). Academic technology solutions. https://academictech.uchicago.edu/
U.S. Department of Education, Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf
Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence on students’ academic development. Education Sciences, 15(3), 343. https://doi.org/10.3390/educsci15030343
Williams, A. (2025). Integrating artificial intelligence into higher education assessment. Intersection: A Journal at the Intersection of Assessment and Learning, 6(1), 128-154. https://doi.org/10.61669/001c.131915
World Economic Forum. (2023). The future of jobs report 2023. https://www.weforum.org/publications/the-future-of-jobs-report-2023/
Ye, L. (2025). AI-driven personalized learning path optimization and adaptive resource recommendation for foreign language education. In R. Su (Ed.), Proceedings of the Seventh International Conference on Image, Video Processing, and Artificial Intelligence (Vol. 13731, Article 137311B). SPIE. https://doi.org/10.1117/12.3076209
Zhong, R., & Zhao, Y. (2025). Paradigm shifts in education: An ecological analysis. ECNU Review of Education, 8(1), 21-40. https://doi.org/10.1177/20965311241296162