The effect of AI-assisted authentic assessment on students’ learning growth in a primary school science classroom

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Ari Hasan Ansori
Ghina Siti Fitria Heriyanti
M. Arie Firdaus Heriansyah

Abstract

This study examines the implementation of artificial intelligence (AI)-assisted authentic assessment and its effect on the learning growth of elementary school students. The study employed a nonequivalent control-group quasi-experimental design involving 72 fifth-grade students. The experimental group received AI-assisted authentic assessment through performance tasks, inquiry-based projects, and open-ended activities, whereas the control group used conventional assessment methods. The findings indicate that the AI-assisted assessment system operated effectively. The natural language processing (NLP)-based system mapped students’ levels of conceptual mastery, identified dominant errors, and generated diagnostic information that teachers used to provide rapid, specific, and adaptive feedback. The system also accelerated response analysis, monitored students’ individual understanding, and promoted active cognitive engagement, enabling students to explain, connect, and reflect on their observational findings related to scientific concepts. The results further show that the experimental group achieved a high N-Gain score (0.72), whereas the control group obtained a moderate N-Gain score (0.43), with t (70) = 11.704, p < 0.001, and Cohen’s d = 2.76, indicating a very large practical effect of AI-assisted authentic assessment on students’ learning growth. These findings confirm that AI-assisted authentic assessment is effective in enhancing learning growth, strengthening the formative function of assessment, and supporting more personalized, reflective, and evidence-based science learning.

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Ansori, A. H., Heriyanti, G. S. F., & Heriansyah, M. A. F. (2026). The effect of AI-assisted authentic assessment on students’ learning growth in a primary school science classroom. Journal of Integrated Elementary Education, 6(1), 173–193. https://doi.org/10.21580/jieed.v6i1.30802
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References

Abrar, M., Aboraya, W., Khaliq, R. A., Subramanian, K. P., Husaini, Y. A., & Husaini, M. A. (2025). AI-Powered Learning Pathways: Personalized Learning and Dynamic Assessments. International Journal of Advanced Computer Science and Applications, 16(1). 454-462. https://doi.org/10.14569/IJACSA.2025.0160145

Agir, N., Effendi, M., Matore, E. M., Eteuati, N. F., & Marquez, N. (2023). Outcome-Based Assessment in The Evaluation of Education Programs Through a Systematic Literature Review. International Journal of Academic Research in Progressive Education and Development, 12(2), 2662-2677. https://doi.org/10.6007/IJARPED/v12-i2/18095

Akmam, A., Ahzari, S., Emiliannur, E., Anshari, R., & Setiawan, D. (2025). Enhancing Science Literacy Through Cognitive Conflict-Based Generative Learning Model: An Experimental Study in Physics Learning. Social Science and Humanities Journal, 9(07), 8507–8522. https://doi.org/10.18535/sshj.v9i07.1934

Akolekar, H., Jhamnani, P., Kumar, V., Tailor, V., Pote, A., Meena, A., Kumar, K., Challa, J. S., & Kumar, D. (2025). The role of generative AI tools in shaping mechanical engineering education from an undergraduate perspective. Scientific Reports, 15(1), 9214. https://doi.org/10.1038/s41598-025-93871-z

Anaroua, F. I., Li, Q., Tang, Y., & Liu, H. P. (2025). AI-driven formative assessment and adaptive learning in data-science education: Evaluating an LLM-powered virtual teaching assistant. arXiv. https://doi.org/10.48550/ARXIV.2509.20369

Ansori, A. H. (2021). Statistika Penelitian. Staisman Press.

Ansori, A. H. (2025). Desain & Analisi Instrumen Penelitian Teori, Praktik Lapangan, dan Aplikasi SPSS. Yayasan Putra Adi Dharma.

Ansori, A. H., & Heriansyah, M. A. F. (2025a). Evaluasi Pendidikan Islam dalam Teori Modern: Integrasi Teknologi dan Nilai Karakter. In Transformasi Pendidikan Islam: Filosofi, Nilai, dan Inovasi Menuju Masyarakat Berkeadaban. Zahir Publishing.

Ansori, A. H., & Heriansyah, M. A. F. (2025b). Transformasi Pembelajaran Abad 21: Sinergi Proyek Kontekstual dan Penilaian Autentik Mewujudkan Pembelajaran Mendalam. Goresan Pena.

Arellanes, F. E. T., Strycker, L., Alvez, G. G., Miller, B., & Vargas, K. (2025). Promoting Agency Among Upper Elementary School Teachers and Students with an Artificial Intelligence Machine Learning System to Score Performance-Based Science Assessments. Education Sciences, 15(1), 54. https://doi.org/10.3390/educsci15010054

Becerra, A., & Cobos, R. (2025). Leveraging Peer, Self, and Teacher Assessments for Generative AI-Enhanced Feedback. arXiv. https://doi.org/10.48550/ARXIV.2512.18306

Bond, M., Khosravi, H., Laat, M. D., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1), 4. https://doi.org/10.1186/s41239-023-00436-z

Coletta, V. P., & Steinert, J. J. (2020). Why normalized gain should continue to be used in analyzing preinstruction and postinstruction scores on concept inventories. Physical Review Physics Education Research, 16(1), 010108. https://doi.org/10.1103/PhysRevPhysEducRes.16.010108

Galanti, T. M., & Holincheck, N. (2022). Beyond content and curriculum in elementary classrooms: Conceptualizing the cultivation of integrated STEM teacher identity. International Journal of STEM Education, 9(1), 43. https://doi.org/10.1186/s40594-022-00358-8

Gao, R., Guo, X., Li, X., Narayanan, A. B. L., Thomas, N., & Srinivasa, A. R. (2024). Towards Scalable Automated Grading: Leveraging Large Language Models for Conceptual Question Evaluation in Engineering. arXiv. https://doi.org/10.48550/ARXIV.2411.03659

Hake, R. R. (1998). Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. American Journal of Physics, 66(1), 64–74. https://doi.org/10.1119/1.18809

Halkiopoulos, C., & Gkintoni, E. (2024). Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis. Electronics, 13(18), 3762. https://doi.org/10.3390/electronics13183762

Hamel, P., & Lee, W. K. (2024). Supporting the evaluation of authentic assessment in environmental sciences: A case study. Cogent Education, 11(1), 2399433. https://doi.org/10.1080/2331186X.2024.2399433

Hattie, J., & Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487

Herianingtyas, N. L. R., Rohman, C., Widiyanto, R., & Amarulloh, R. R. (2023). Evaluation of the Implementation of Science Literacy-Based Learning in Madrasah Ibtidaiyah. JMIE (Journal of Madrasah Ibtidaiyah Education), 7(2), 92. https://doi.org/10.32934/jmie.v7i2.602

Hong, L. (2025). Development and validation of a competency-based ladder pathway for AI literacy enhancement among higher vocational students. Scientific Reports, 15(1), 29898. https://doi.org/10.1038/s41598-025-15202-6

Hopfenbeck, T. N., Zhang, Z., Sun, S. Z., Robertson, P., & McGrane, J. A. (2023). Challenges and opportunities for classroom-based formative assessment and AI: A perspective article. Frontiers in Education, 8, 1270700. https://doi.org/10.3389/feduc.2023.1270700

Ifelebuegu, A. (2023). Rethinking online assessment strategies: Authenticity versus AI chatbot intervention. Journal of Applied Learning and Teaching, 6(2), 385–392. https://doi.org/10.37074/jalt.2023.6.2.2

Joseph, T. S., Gowrie, S., Montalbano, M. J., Bandelow, S., Clunes, M., Dumont, A. S., Iwanaga, J., Tubbs, R. S., & Loukas, M. (2025). The Roles of Artificial Intelligence in Teaching Anatomy: A Systematic Review. Clinical Anatomy, 38(5), 552–567. https://doi.org/10.1002/ca.24272

Kamalov, F., Calonge, D. S., & Gurrib, I. (2023). New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution. Sustainability, 15(16), 12451. https://doi.org/10.3390/su151612451

Karim, A. N., Isa, C. M. M., & Noor, S. M. (2025). A Methodological Framework for AI-Integrated Alternative Assessments in Engineering Education. Jurnal Kejuruteraan, 37(2), 807–819. https://doi.org/10.17576/jkukm-2025-37(2)-20

Kasmi, H., & Anasse, K. (2023). The Status of Alternative Assessment in Morocco: Teachers’ Attitudes and Obstacles. International Journal of Language and Literary Studies, 5(1), 300–311. https://doi.org/10.36892/ijlls.v5i1.1189

Khan, M. A., Kurbonova, O., Abdullaev, D., Radie, A. H., & Basim, N. (2024). Is AI-assisted assessment liable to evaluate young learners? Parents support, teacher support, immunity, and resilience are in focus in testing vocabulary learning. Language Testing in Asia, 14(1), 48. https://doi.org/10.1186/s40468-024-00324-x

Khojasteh, L., Kafipour, R., Pakdel, F., & Mukundan, J. (2025). Empowering medical students with AI writing co-pilots: Design and validation of AI self-assessment toolkit. BMC Medical Education, 25(1), 159. https://doi.org/10.1186/s12909-025-06753-3

Kotsis, K. T. (2024). Chatgpt in Teaching Physics Hands-On Experiments in Primary School. European Journal of Education Studies, 11(10). https://doi.org/10.46827/ejes.v11i10.5549

Kusumawati, E., Suswandari, & Umam, K. (2025). Strengthening teacher competence for leading and sustaining the implementation of the Merdeka Curriculum. Cogent Education, 12(1), 2501458. https://doi.org/10.1080/2331186X.2025.2501458

Lee, C.-A., Huang, N.-F., Tzeng, J.-W., & Tsai, P.-H. (2023). AI-Based Diagnostic Assessment System: Integrated with Knowledge Map in MOOCs. IEEE Transactions on Learning Technologies, 16(5), 873–886. https://doi.org/10.1109/TLT.2023.3308338

Liu, A., Esbenshade, L., Sarkar, S., Tian, Z., Sun, M., Zhang, Z., Han, T., Lapicus, Y., & He, K. (2025). AI as a Teaching Partner: Early Lessons from Classroom Codesign with Secondary Teachers. arXiv. https://doi.org/10.48550/ARXIV.2512.12045

Łodzikowski, K., Foltz, P. W., & Behrens, J. T. (2024). Generative AI and Its Educational Implications. arXiv. https://doi.org/10.48550/ARXIV.2401.08659

Mahligawati, F., Allanas, E., Butarbutar, M. H., & Nordin, N. A. N. (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

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://doi.org/10.48550/ARXIV.2410.09576

Melinda, N. E., & Tanjung, I. F. (2022). Analysis of Impelementing Authentic Assessment of Curriculum 2013 by High School Biologi Teachers. Jurnal Pelita Pendidikan, 10(2). 28-35. https://doi.org/10.24114/jpp.v10i2.34500

Mosher, M., Dieker, L., & Hines, R. (2024). The Past, Present, and Future Use of Artificial Intelligence in Teacher Education. Journal of Special Education Preparation, 4(2), 6–17. https://doi.org/10.33043/8aa9855b

Mulaudzi, L. V., & Hamilton, J. (2025). Lecturer’s Perspective on the Role of AI in Personalized Learning: Benefits, Challenges, and Ethical Considerations in Higher Education. Journal of Academic Ethics, 23(4), 1571–1591. https://doi.org/10.1007/s10805-025-09615-1

Nagaraj, B. K. (2023). The Emerging Role of Artificial Intelligence in STEM Higher Education: A Critical Review. International Research Journal of Multidisciplinary Technovation, 1–19. https://doi.org/10.54392/irjmt2351

Naseer, F., & Khawaja, S. (2025). Mitigating Conceptual Learning Gaps in Mixed-Ability Classrooms: A Learning Analytics-Based Evaluation of AI-Driven Adaptive Feedback for Struggling Learners. Applied Sciences, 15(8), 4473. https://doi.org/10.3390/app15084473

Nuangchalerm, P. (2023). AI-Driven Learning Analytics in STEM Education. International Journal of Research in STEM Education, 5(2), 77–84. https://doi.org/10.33830/ijrse.v5i2.1596

Pacala, F. A. A. (2023). Artificial intelligence in a modernizing science and technology education: A textual narrative synthesis in the COVID-19 era. Journal of Physics: Conference Series, 2611(1), 012028. https://doi.org/10.1088/1742-6596/2611/1/012028

Park, Y., & Doo, M. Y. (2024). Role of AI in Blended Learning: A Systematic Literature Review. The International Review of Research in Open and Distributed Learning, 25(1), 164–196. https://doi.org/10.19173/irrodl.v25i1.7566

Planinic, M., Boone, W. J., Susac, A., & Ivanjek, L. (2019). Rasch analysis in physics education research: Why measurement matters. Physical Review Physics Education Research, 15(2), 020111. https://doi.org/10.1103/PhysRevPhysEducRes.15.020111

Poernomo, J. B., Wiyanto, M., Rusilowati, A., & Saptono, S. (2018). The Development of Integrated Science Learning Instrument Based on Project-Based Learning to Measure Critical Thinking Skills. Proceedings of the International Conference on Science and Education and Technology 2018 (ISET 2018). Proceedings of the International Conference on Science and Education and Technology 2018 (ISET 2018). https://doi.org/10.2991/iset-18.2018.57

Rasheed, H., A., Weber, C., & Fathi, M. (2023). Context based learning: A survey of contextual indicators for personalized and adaptive learning recommendations – a pedagogical and technical perspective. Frontiers in Education, 8, 1210968. https://doi.org/10.3389/feduc.2023.1210968

Ravi, M., & Besharat, M. (2026). A holistic consideration of authentic assessments: Student perception of assessment design, delivery, flexibility and creativity. European Journal of Engineering Education, 51(1), 25–42. https://doi.org/10.1080/03043797.2025.2480116

Riantoni, C. (2024). A Hybrid Automatic Scoring System: Artificial Intelligence- Based Evaluation of Physics Concept Comprehension Essay Test. International Journal of Information and Education Technology, 14(6), 876–882. https://doi.org/10.18178/ijiet.2024.14.6.2113

Salsabila, S., Setiyono, Y. C. P., Damayani, A. T., & Azizah, M. (2024). Implementation of Science Literacy Through Eclipse Diorama Project in Grade VI at Supriyadi Elementary School Semarang. Jurnal Sains Sosio Humaniora, 8(1), 11–23. https://doi.org/10.22437/jssh.v8i1.33955

Saputra, I., Kurniawan, A., Yanita, M., Yeni Putri, E., & Mahniza, M. (2024). The Evolution of Educational Assessment: How Artificial Intelligence is Shaping the Trends and Future of Learning Evaluation. The Indonesian Journal of Computer Science, 13(6). https://doi.org/10.33022/ijcs.v13i6.4465

Shen, H. (2025). Bidirectional Human-AI Alignment in Education for Trustworthy Learning Environments. arXiv. https://doi.org/10.48550/ARXIV.2512.21552

Somers, R., Nelson, S. C., & Boles, W. (2021). Applying natural language processing to automatically assess student conceptual understanding from textual responses. Australasian Journal of Educational Technology, 37(5), 98–115. https://doi.org/10.14742/ajet.7121

Song, C., & Song, Y. (2023). Enhancing academic writing skills and motivation: Assessing the efficacy of ChatGPT in AI-assisted language learning for EFL students. Frontiers in Psychology, 14, 1260843. https://doi.org/10.3389/fpsyg.2023.1260843

Su, M., Dang, B., Nguyen, A., & Nagashima, T. (2025). Choice-making in an adaptive learning system with motivational pedagogical agents. Npj Science of Learning, 10(1), 77. https://doi.org/10.1038/s41539-025-00366-7

Sukmawati, W., & Wahjusaputri, S. (2024). Integrating RADEC Model and AI to Enhance Science Literacy: Student Perspectives. Jurnal Penelitian Pendidikan IPA, 10(6), 3080–3089. https://doi.org/10.29303/jppipa.v10i6.7557

Wang, Y., Wu, J., Chen, F., Wang, Z., Li, J., & Wang, L. (2024). Empirical Assessment of AI-Powered Tools for Vocabulary Acquisition in EFL Instruction. IEEE Access, 12, 131892–131905. https://doi.org/10.1109/ACCESS.2024.3446657

Wei, L. (2023). Artificial intelligence in language instruction: Impact on English learning achievement, L2 motivation, and self-regulated learning. Frontiers in Psychology, 14, 1261955. https://doi.org/10.3389/fpsyg.2023.1261955

Wibawa, I. M. C., Widiana, I. W., & Jampel, N. (2024). How EtnoEduction is Essential and Linked to the Science Learning in the 21st Century Paradigm? Jurnal Edutech Undiksha, 12(1), 11–19. https://doi.org/10.23887/jeu.v12i1.82441

Wodaj, H. (2020). Effects of 7E Instructional Model with Metacognitive Scaffolding on Students’ Conceptual Understanding in Biology. Journal of Education in Science, Environment and Health. https://doi.org/10.21891/jeseh.770794

Xin, K. K., & Nasri, N. B. M. (2024). The Readiness Level of Primary School Teachers Towards the Implementation of Classroom-Based Assessment (PBD). International Journal of Academic Research in Progressive Education and Development, 13(4), Pages 1788-1799. https://doi.org/10.6007/IJARPED/v13-i4/23224

Yu, J. (2023). Exam Culture and Formative Assessment in China: The Gaokao Reform and Its Sociocultural Hindrance. Journal of Education, Humanities and Social Sciences, 23, 291–301. https://doi.org/10.54097/ehss.v23i.12900

Yu, N., Zhang, J., Mitra, S., Smith, R., & Rich, A. (2025). AI-Educational Development Loop (AI-EDL): A Conceptual Framework to Bridge AI Capabilities with Classical Educational Theories. https://doi.org/10.48550/ARXIV.2508.00970

Zhai, X., Neumann, K., & Krajcik, J. (2023). Editorial: AI for tackling STEM education challenges. Frontiers in Education, 8, 1183030. https://doi.org/10.3389/feduc.2023.1183030

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