AI-Powered Assessments in Mathematics Education: A Systematic Review of Contemporary Research Literature

Rusen Meylani

doi:10.53444/deubefd.1527781

EN TR

Öz

AI-Powered Assessments in Mathematics Education: A Systematic Review of Contemporary Research Literature

Abstract

This paper explores the use of artificial intelligence (AI) technology in assessments within the context of mathematics education, addressing the need for creative assessment methods in light of the changing nature of education. The primary source of the issue is the shortcomings of conventional assessment techniques, which often fall short of providing prompt, personalized feedback necessary to improve student learning outcomes. The paper examines how AI-powered technologies are changing assessment procedures by providing accurate assessments, customized learning paths, and increased engagement. It does this by synthesizing current material using a systematic review technique. According to the research, AI tools that support critical thinking and problem-solving abilities also considerably improve evaluation objectivity and accuracy. These tools include Intelligent Tutoring Systems (ITS) and AI-powered calculators. According to the findings, these tools facilitate data-driven decision-making, enabling teachers to customize education to each student's requirements and resolve differences in assessment results. The research underscores AI's role in fostering a diverse and equal learning environment, thereby contributing to positive social impact. However, it also addresses practical, ethical, and technological difficulties. To sum up, integrating AI into math exams has much potential to change education, but further study and deliberate application are needed to fulfill this promise and overcome current constraints fully.

Keywords

Artificial Intelligence , Mathematics Assessments , Individualized Learning , Educational Technology , Intelligent Tutoring Systems , Adaptive Learning

Matematik Eğitiminde Yapay Zeka Destekli Değerlendirmeler: Çağdaş Araştırma Literatürünün Sistematik Biçimde İncelenmesi

Öz

Bu çalışma, Yapay Zeka (YZ) teknolojilerinin matematik değerlendirmelerinde uygulanmasını, değişen eğitim ortamı ve yenilikçi değerlendirme çözümlerine duyulan ihtiyaç bağlamında incelemektedir. Problemin merkezinde, geleneksel değerlendirme yöntemlerinin genellikle öğrenci öğrenme sonuçlarını geliştirmek için gerekli olan, zamanında ve kişiselleştirilmiş geri bildirimi sağlayamaması yer almaktadır. Sistematik bir inceleme metodolojisi kullanarak, çalışma YZ destekli araçların, hassas değerlendirmeler sunarak, kişiselleştirilmiş öğrenme deneyimleri ve artırılmış katılım yoluyla değerlendirme uygulamalarını nasıl yeniden şekillendirdiğini araştırmak için çağdaş literatürü sentezlemektedir. Bulgular, Akıllı Öğretim Sistemleri ve YZ tabanlı hesap makineleri gibi YZ teknolojilerinin, değerlendirmelerin doğruluğunu ve nesnelliğini önemli ölçüde artırırken eleştirel düşünme ve problem çözme becerilerini teşvik ettiğini ortaya koymaktadır. Sonuçlar, bu teknolojilerin veri odaklı karar verme süreçlerini destekleyerek eğitimcilerin, bireysel öğrenci ihtiyaçlarına göre öğretimi uyarlamasına ve değerlendirme sonuçlarındaki farklılıkları gözeterek bireyselleştirilimiş eğitime olanak tanıdığını göstermektedir. Çalışma, YZ’nin kapsayıcı ve adil bir eğitim ortamı oluşturmadaki faydalarını vurgulamakla birlikte teknik, etik ve uygulama konularına ilişkin zorlukları da vurgulamaktadır. Sonuç olarak, matematik eğitiminde değerlendirmelerde YZ entegrasyonu, eğitimi geliştirmek açısından önemli vaatlerde bulunmaktadır; ancak var olan potansiyelin tam olarak gerçekleşebilmesi ve mevcut sınırlamaların en aza indirgenebilmesi için sürekli araştırma-geliştirme ve stratejik uygulama faaliyetleri gerekmektedir.

Anahtar Kelimeler

Yapay Zeka , Matematik Değerlendirmeleri , Kişiselleştirilmiş Öğrenme , Eğitim Teknolojisi , Akıllı Öğretim Sistemleri , Uyarlamalı Öğrenme

References

Addy, W. (2024). AI in credit scoring: A comprehensive review of models and predictive analytics. Global Journal of Engineering and Technology Advances, 18(2), 118–129. https://doi.org/10.30574/gjeta.2024.18.2.0029
Agarwal, P. (2024). Assessing the challenges and opportunities of artificial intelligence in Indian education. International Journal for Global Academic & Scientific Research, 3(1), 36–44. https://doi.org/10.55938/ijgasr.v3i1.71
Aghaziarati, A. (2023). Artificial intelligence in education: Investigating teacher attitudes. Aitechbesosci, 1(1), 35–42. https://doi.org/10.61838/kman.aitech.1.1.6
Baez, C. (2023). Exploring the perception of AI in learning: Unveiling the role of student and teacher motivation and self-efficacy. https://doi.org/10.31219/osf.io/wqvrh
Bedizel, N. (2023). Evolving landscape of artificial intelligence (AI) and assessment in education: A bibliometric analysis. International Journal of Assessment Tools in Education, 10(Special Issue), 208–223. https://doi.org/10.21449/ijate.1369290
Bodemer, O. (2023). Artificial intelligence in governance: A comprehensive analysis of AI integration and policy development in the German government. https://doi.org/10.36227/techrxiv.24639588.v1
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Chisom, O. (2024). Review of AI in education: Transforming learning environments in Africa. International Journal of Applied Research in Social Sciences, 5(10), 637–654. https://doi.org/10.51594/ijarss.v5i10.725
Chiu, T., Meng, H., Chai, C., King, I., Wong, S., & Yam, Y. (2022). Creation and evaluation of a pretertiary artificial intelligence (AI) curriculum. IEEE Transactions on Education, 65(1), 30–39. https://doi.org/10.1109/TE.2021.3085878
College of Education at the University of Illinois. (2024, October 24). AI in schools: Pros and cons. https://education.illinois.edu/about/news-events/news/article/2024/10/24/ai-in-schools--pros-and-cons

Critical Appraisal Skills Programme (CASP). (2018). CASP qualitative checklist. https://casp-uk.net/wp-content/uploads/2018/03/CASP-Qualitative-Checklist-2018_fillable_form.pdf
Dabingaya, M. (2022). Analyzing the effectiveness of AI-powered adaptive learning platforms in mathematics education. Interdisciplinary Journal Papier Human Review, 3(1), 1–7. https://doi.org/10.47667/ijphr.v3i1.226
Dai, Y., Chai, C., Lin, P., Jong, M., Guo, Y., & Jian-jun, Q. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12(16), 6597. https://doi.org/10.3390/su12166597
Djajasoepena, R. (2024). Utilization of artificial intelligence to support the development of teaching and project modules. JCSSE, 4(1), 7–11. https://doi.org/10.35806/jcsse.v4i1.440
Esmaeilzadeh, P., Mirzaei, T., & Dharanikota, S. (2021). Patients’ perceptions toward human–artificial intelligence interaction in health care: Experimental study. Journal of Medical Internet Research, 23(11), e25856. https://doi.org/10.2196/25856
European Commission. (2022, October 28). An ethical use of artificial intelligence (AI) in education. AI Watch. https://ai-watch.ec.europa.eu/news/ethical-use-artificial-intelligence-ai-education-2022-10-28_en
Geary, D., Hoard, M., Nugent, L., Chu, F., Scofield, J., & Hibbard, D. (2019). Sex differences in mathematics anxiety and attitudes: Concurrent and longitudinal relations to mathematical competence. Journal of Educational Psychology, 111(8), 1447–1461. https://doi.org/10.1037/edu0000355
Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006
Grubaugh, S. (2024). The future of elementary social studies: Harnessing AI’s potential through evidence-based practices. Technium Social Sciences Journal, 58, 87–93. https://doi.org/10.47577/tssj.v58i1.10991
Gupta, D. (2024). Navigating the future of education: The impact of artificial intelligence on teacher–student dynamics. EATP, 6006–6013. https://doi.org/10.53555/kuey.v30i4.2332
Hermann, I. (2021). Artificial intelligence in fiction: Between narratives and metaphors. AI & Society, 38(1), 319–329. https://doi.org/10.1007/s00146-021-01299-6
Hiremath, A. (2024). Transforming handwritten answer assessment: A multi-modal approach combining text detection, handwriting recognition, and language models. https://doi.org/10.21203/rs.3.rs-4301899/v1
Hutson, J., Jeevanjee, T., Graaf, V., Lively, J., Weber, J., Weir, G., & Edele, S. (2022). Artificial intelligence and the disruption of higher education: Strategies for integrations across disciplines. Creative Education, 13(12), 3953–3980. https://doi.org/10.4236/ce.2022.1312253
Hwang, S. (2022). Examining the effects of artificial intelligence on elementary students’ mathematics achievement: A meta-analysis. Sustainability, 14(20), 13185. https://doi.org/10.3390/su142013185
Ibrahim, A. (2024). Artificial intelligence (AI): Perception and utilization of AI technologies in educational assessment in Nigerian universities. Edukasiana Jurnal Inovasi Pendidikan, 3(3), 367–380. https://doi.org/10.56916/ejip.v3i3.763
Institute of Education Sciences. (2023, October 16). Math autoscoring is finally here—Let's tap its potential for students and teachers. U.S. Department of Education. https://ies.ed.gov/director/remarks/10-16-2023.asp
Jiang, W., & Pardos, Z. (2021). Towards equity and algorithmic fairness in student grade prediction. https://doi.org/10.1145/3461702.3462623
Kaplan, R., & Meylani, R. (2025). Dimensions of artificial intelligence literacy: A qualitative synthesis of contemporary research literature. Journal of Computer and Education Research, 13(26), 790–825. https://doi.org/10.18009/jcer.1648380
Khan, H. (2023). Re: Artificial intelligence technology in surgery. Australian and New Zealand Journal of Surgery, 94(3), 489–489. https://doi.org/10.1111/ans.18809
Kitcharoen, P. (2024). Enhancing teachers’ AI competencies through artificial intelligence of things professional development training. International Journal of Interactive Mobile Technologies (iJIM), 18(2), 4–15. https://doi.org/10.3991/ijim.v18i02.46613
Lakomkin, N., Dhamoon, M., Carroll, K., Singh, I., Tuhrim, S., Lee, J., … & Mocco, J. (2018). Prevalence of large vessel occlusion in patients presenting with acute ischemic stroke: A 10-year systematic review of the literature. Journal of NeuroInterventional Surgery, 11(3), 241–245. https://doi.org/10.1136/neurintsurg-2018-014239
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310
Lee, I., & Perret, B. (2022). Preparing high school teachers to integrate AI methods into STEM classrooms. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12783–12791. https://doi.org/10.1609/aaai.v36i11.21557
Lee, J. (2024). Development of a content framework of artificial intelligence integrated education considering ethical factors. International Journal on Advanced Science, Engineering and Information Technology, 14(1), 205–213. https://doi.org/10.18517/ijaseit.14.1.19558
Liang, Y. (2023). Balancing: The effects of AI tools in educational context. Frontiers in Humanities and Social Sciences, 3(8), 7–10. https://doi.org/10.54691/fhss.v3i8.5531
Lin, Z. (2024). Exploring an effective automated grading model with reliability detection for large‐scale online peer assessment. Journal of Computer Assisted Learning, 40(4), 1535–1551. https://doi.org/10.1111/jcal.12970
Lye, C. (2024). Generative artificial intelligence in tertiary education: Assessment redesign principles and considerations. Education Sciences, 14(6), 569. https://doi.org/10.3390/educsci14060569
Mahligawati, F. (2023). Artificial intelligence in physics education: A comprehensive literature review. Journal of Physics: Conference Series, 2596(1), 012080. https://doi.org/10.1088/1742-6596/2596/1/012080
Matzakos, N. (2023). Learning mathematics with large language models. International Journal of Emerging Technologies in Learning (iJET), 18(20), 51–71. https://doi.org/10.3991/ijet.v18i20.42979
Mayo, S. (2024). Co-creating with AI in art education: On the precipice of the next terrain. Education Journal, 13(3), 124–132. https://doi.org/10.11648/j.edu.20241303.15
Melkonian, S., Crowder, J., Adam, E., White, M., & Peipins, L. (2022). Social determinants of cancer risk among American Indian and Alaska Native populations: An evidence review and map. Health Equity, 6(1), 717–728. https://doi.org/10.1089/heq.2022.0097
Messer, M. (2024). Automated grading and feedback tools for programming education: A systematic review. ACM Transactions on Computing Education, 24(1), 1–43. https://doi.org/10.1145/3636515
Meylani, R. (2024a). Innovations with schema theory: Modern implications for learning, memory, and academic achievement. International Journal for Multidisciplinary Research, 6(1). https://doi.org/10.36948/ijfmr.2024.v06i01.13785
Meylani, R. (2024b). A critical glance at adaptive learning systems using artificial intelligence: A systematic review and qualitative synthesis of contemporary research literature. Batı Anadolu Eğitim Bilimleri Dergisi, 15(3), 3519–3547. https://doi.org/10.51460/baebd.1525452
Meylani, R. (2024c). Artificial intelligence in the education of teachers: A qualitative synthesis of the cutting-edge research literature. Journal of Computer and Education Research, 12(24), 600–637. https://doi.org/10.18009/jcer.1477709
Meylani, R. (2024d). An in-depth literature review of remote, online, and hybrid learning with case studies of successful and failed attempts. International Journal of Studies in Education and Science, 5(1), 29–41. https://doi.org/10.46328/ijses.87
Meylani, R. (2024e). Blueprint for the 21st-century online learning environment in STEM education through a systematic review and qualitative synthesis. Edelweiss Applied Science and Technology, 8(6), 8196–8226. https://doi.org/10.55284/25768484.xii86.8763
Meylani, R. (2025a). A critical glance at technology’s role in mathematics education for a sustainable future: Advancing SDG 4 – Quality education through a systematic review and qualitative synthesis. Journal of Lifestyle and SDGs Review, 5(2), e04566. https://doi.org/10.47172/2965-730X.SDGsReview.v5.n02.pe04566
Meylani, R. (2025b). Artificial intelligence in mathematics teacher education: A systematic review and qualitative synthesis of contemporary research literature. International Journal of Technology in Education and Science, 1, 63–91. https://doi.org/10.46328/ijoftes.258
Meylani, R. (2025c). Gamification and game-based learning in mathematics education for advancing SDG 4: A systematic review and qualitative synthesis of contemporary research literature. Journal of Lifestyle and SDGs Review, 5(2), e04567. https://doi.org/10.47172/2965-730X.SDGsReview.v5.n02.pe04567
Meylani, R. (2025d). Integration of TI-84 and TI-89 model graphing calculators in mathematics education: Precalculus instruction using the TPACK framework. Journal of Computer and Education Research, 13(25), 254–282. https://doi.org/10.18009/jcer.1589181
Meylani, R., & Kaplan, R. (2025). Novel approaches for teaching mathematical modeling to preservice teachers: A systematic review. EMTEKA: Jurnal Pendidikan Matematika, 6(1), 505–519. https://doi.org/10.24127/emteka.v6i1.8362
Meylani, R., & Kutluca, T. (2025). AI-powered discourse in mathematics education in support of SDG 4: A systematic review of contemporary research literature. Discourse and Communication for Sustainable Education, 16(2), 25–43. https://doi.org/10.2478/dcse-2025-0014
Molnár, G., & Csapó, B. (2019). Making the psychological dimension of learning visible: Using technology-based assessment to monitor students’ cognitive development. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.01368
Muslimin, A. (2024). Evaluating Cami AI across SAMR stages: Students’ achievement and perceptions in EFL writing instruction. Online Learning, 28(2). https://doi.org/10.24059/olj.v28i2.4246
Nasir, M. (2024). Utilizing artificial intelligence in education to enhance teaching effectiveness. Proceedings of ICE, 2(1), 280–285. https://doi.org/10.32672/pice.v2i1.1367
Odeyemi, O. (2024). Reviewing the role of AI in fraud detection and prevention in financial services. International Journal of Science and Research Archive, 11(1), 2101–2110. https://doi.org/10.30574/ijsra.2024.11.1.0279
OECD. (2024). The potential impact of artificial intelligence on equity and inclusion in education. OECD Publishing. https://www.oecd.org/en/publications/the-potential-impact-of-artificial-intelligence-on-equity-and-inclusion-in-education_15df715b-en.html
Owan, V. (2023). Exploring the potential of artificial intelligence tools in educational measurement and assessment. Eurasia Journal of Mathematics, Science and Technology Education, 19(8), em2307. https://doi.org/10.29333/ejmste/13428
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Pepin, B., Buchholtz, N., & Salinas-Hernández, U. (2025). Mathematics education in the era of ChatGPT: Investigating its meaning and use for school and university education. Digital Experiences in Mathematics Education, 11(1), 1–8. https://doi.org/10.1007/s40751-025-00173-0
Remoto, J. (2023). ChatGPT and other AIs: Personal relief and limitations among mathematics-oriented learners. Environment and Social Psychology, 9(1). https://doi.org/10.54517/esp.v9i1.1911
Rieskamp, J. (2023). Approaches to improve fairness when deploying AI-based algorithms in hiring: Using a systematic literature review to guide future research. https://doi.org/10.24251/hicss.2023.026
Sarwari, A. (2024). Assessment of the impacts of artificial intelligence (AI) on intercultural communication among postgraduate students in a multicultural university environment. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-63276-5
Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18(1). https://doi.org/10.1186/s41239-021-00292-9
Smyrnova-Trybulska, E., Przybyła-Kasperek, M., & Kommers, P. (2023). Educational, social, and ethical aspects of AI in the opinion of IT and pedagogy students: A case study. In International Conferences on E-Society 2023 and Mobile Learning 2023 (p. 171). https://doi.org/10.33965/es_ml2023_202302l021
Stefanova, T. (2024). Possibilities for using AI in mathematics education. MEM, 53, 117–125. https://doi.org/10.55630/mem.2024.53.117-125
Sunarto, M. (2024). Developing a differentiated learning model based on artificial intelligence: Implementation in the mathematics classroom. Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran, dan Pembelajaran, 10(2), 416. https://doi.org/10.33394/jk.v10i2.11135
Tsopra, R., Luchinat, C., Alberghina, L., Lehrach, H., Vanoni, M., Dreher, F., … & Burgun, A. (2021). A framework for validating AI in precision medicine: Considerations from the European ITFOC consortium. BMC Medical Informatics and Decision Making, 21(1). https://doi.org/10.1186/s12911-021-01634-3
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
Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21(15). https://doi.org/10.1186/s41239-024-00448-3
Wu, R., & Yu, Z. (2023). Do AI chatbots improve students' learning outcomes? Evidence from a meta‐analysis. British Journal of Educational Technology, 55(1), 10–33. https://doi.org/10.1111/bjet.13334
Xie, X. (2023). Influence of AI-driven inquiry teaching on learning outcomes. International Journal of Emerging Technologies in Learning (iJET), 18(23), 59–70. https://doi.org/10.3991/ijet.v18i23.45473
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). https://doi.org/10.1186/s40594-022-00377-5
Yang, Y. (2023). Enhancing students' metacognition via AI-driven educational support systems. International Journal of Emerging Technologies in Learning (iJET), 18(24), 133–148. https://doi.org/10.3991/ijet.v18i24.45647

Details

Primary Language

English

Subjects

Development of Science, Technology and Engineering Education and Programs

Journal Section

Review

Authors

Rusen Meylani ^*
0000-0002-3121-6088
Türkiye

Publication Date

December 29, 2025

Submission Date

August 4, 2024

Acceptance Date

October 28, 2025

Published in Issue

Year 1970 Number: 66

DOI

https://doi.org/10.53444/deubefd.1527781

IZ

https://izlik.org/JA84AN42PF

APA

Meylani, R. (2025). AI-Powered Assessments in Mathematics Education: A Systematic Review of Contemporary Research Literature. Dokuz Eylül Üniversitesi Buca Eğitim Fakültesi Dergisi, 66, 3642-3674. https://doi.org/10.53444/deubefd.1527781

Öz

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AI-Powered Assessments in Mathematics Education: A Systematic Review of Contemporary Research Literature

Abstract

Keywords

Matematik Eğitiminde Yapay Zeka Destekli Değerlendirmeler: Çağdaş Araştırma Literatürünün Sistematik Biçimde İncelenmesi

Öz

Anahtar Kelimeler

References

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Primary Language

Subjects

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Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

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