Conceptual Framework of an AI-Based Adaptive Formative Assessment to Support Differentiated Elementary Science Learning

Windi Febriana Putri; Diana Diana; Ellianawati Ellianawati; Decky Avrilianda

doi:10.51276/edu.v7i2.1574

Authors

Windi Febriana Putri Universitas Negeri Semarang
Diana Diana Universitas Negeri Semarang
Ellianawati Ellianawati Universitas Negeri Semarang
Decky Avrilianda Universitas Negeri Semarang

DOI:

https://doi.org/10.51276/edu.v7i2.1574

Keywords:

AI-Based Adaptive Assessment , Differentiated Learning , Elementary Science , Formative Assessment

Abstract

Formative assessment in elementary science is often homogeneous, limiting its diagnostic role and support for differentiated learning. This study designs a conceptual framework for an AI-based adaptive formative assessment system to support differentiated learning in elementary science. Using R&D with the ADDIE model, the article focuses on the Analysis and Design stages. Analysis identified assessment problems and teachers' needs through semi-structured interviews and a needs questionnaire; design produced system specifications and conceptual designs from empirical findings and theory. Key outputs include: (1) an assessment blueprint with progressive difficulty, (2) adaptive logic driven by student responses, (3) a system flow diagram, and (4) an initial application prototype. It emphasizes diagnostic function, meaningful formative feedback, and teacher usability. Theoretically, it advances formative assessment research by integrating adaptivity within an assessment-for-learning perspective. In practice, the framework can guide developers, schools, and policymakers in enhancing teachers' assessment literacy and implementing adaptive formative assessment in elementary science.

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Author Biographies

Windi Febriana Putri, Universitas Negeri Semarang

She was born in Kebumen on 4 February, 1996. Currently pursuing postgraduate education in Elementary Education at Semarang State University (UNNES). Earned a Bachelor of Education (S.Pd.) from the Indonesian Education University (UPI) in 2018 and completed the Teacher Professional Education Program (PPG) at the University of Muhammadiyah Jakarta (UMJ) in 2022.

Diana Diana, Universitas Negeri Semarang

She was born in Jakarta, 20 December, 1979. Lecturer at Semarang State University. Holds a Master of Education (M.Pd.) and Doctorate. Senior Lecturer, Early Childhood Education (S2), Faculty of Education and Psychology at Semarang State University.

Ellianawati Ellianawati, Universitas Negeri Semarang

She Was born in Grobogan on 26 November, 1974. Lecturer at Universitas Negeri Semarang. Holds a Master of Science (M.Si.) and a Doctoral degree in Science Education. Academic expertise includes Physics Learning Based on Local Wisdom, STEM Education, and Multidisciplinary Education. Actively engaged in research, curriculum development, and teacher training programs focused on integrating cultural context and interdisciplinary approaches into science education.

Decky Avrilianda, Universitas Negeri Semarang

He was born in Banyuwangi, 11 April 1995. Lecturer at Universitas Negeri Semarang. Holds a Bachelor's degree in Education (S.Pd.), a Master's degree in Education (M.Pd.), and a Doctoral degree in education. Academic expertise includes Non-Formal Education, Community Empowerment, and Basic Education. Actively involved in research and community service related to educational development and the improvement of learning opportunities across diverse community groups.

References

Balitbang Kemendikbud. (2020). Laporan Hasil Ujian Nasional Sekolah Dasar Tahun 2020. Kementerian Pendidikan dan Kebudayaan Republik Indonesia.

Black, P., & Wiliam, D. (2014). Inside the Black Box: Raising Standards Through Classroom Assessment (Rev. ed.). GL Assessment.

Branch, R. M. (2009). Instructional Design: The ADDIE Approach. Springer. https://doi.org/10.1007/978-0-387-09506-6

Carless, D. (2020). Long-Term Perspectives on Learning-Oriented Assessment. Assessment & Evaluation in Higher Education, 45(6), 993–1002. https://doi.org/10.1080/02602938.2020.1726921

Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020). Application and Theory Gaps During the Rise of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002

Diana, D., & Kartika, A. (2024). Deskripsi Keterampilan Asesmen dan Stimulasi HOTS Guru Sekolah Dasar: Studi Kualitatif. Jurnal Psikohumanika, 16(1), 60–74.

Earl, L. M. (2013). Assessment as Learning: Using Classroom Assessment to Maximize Student Learning (2nd ed.). Corwin Press.

Hattie, J. (2023). Visible Learning: The Sequel: A Synthesis of Over 2,100 Meta-Analyses Relating to Achievement. Routledge. https://doi.org/10.4324/9781003380542

Hattie, J., & Clarke, S. (2018). Visible Learning: Feedback (1st ed.). Routledge. https://doi.org/10.4324/9780429485480

Heritage, M. (2021). Formative Assessment in Practice: A Process of Inquiry and Action. Harvard Education Press.

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

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in Education: Towards a Community-Wide Framework. British Journal of Educational Technology, 53(3), 739–755. https://doi.org/10.1111/bjet.13171

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson.

Meidya, L., Haryani, S., Wijayati, N., Avrilianda, D., & Subali, B. (2024). Pengembangan Mobile Learning Berbasis RADEC pada Materi Sistem Pencernaan untuk Melatih Literasi Digital dan Hasil Belajar Kognitif Siswa. Jurnal Pendidikan Biologi Undiksha, 11(3), 70–83.

OECD. (2019). PISA 2018 Results (Volume I): What Students Know and Can Do. OECD Publishing. https://doi.org/10.1787/5f07c754-en

Permatasari, G. A., Ellianawati, E., & Hardyanto, W. (2019). Online Web-Based Learning and Assessment Tool in Vocational High School for Physics. Jurnal Penelitian & Pengembangan Pendidikan Fisika, 5(1), 1–8. https://doi.org/10.21009/1.05101

Prasetyo, Z. K., & Kusuma, A. B. (2020). Pengembangan Four-Tier Online Diagnostic Test Berbasis Web untuk Mendiagnosis Pemahaman Konsep Siswa. Jurnal Penelitian dan Pengembangan Pendidikan, 14(1), 45–58.

Rasmini, N. W., Antara, P. A., & Wulandari, I. G. A. A. M. (2023). The Use of Technology-Based Formative Assessment in Improving Mathematics Achievement of Elementary School Students. Journal of Education Technology, 7(3), 497–503. https://doi.org/10.23887/jet.v7i3.67770

Sadler, D. R. (2022). Three In-Course Assessment Reforms to Improve Higher Education Learning Outcomes. Assessment & Evaluation in Higher Education, 47(2), 167–183. https://doi.org/10.1080/02602938.2021.1903063

Subali, B., Ellianawati, Faizah, Z., & Sidiq, M. (2023). Indonesian National Assessment Support: Can RE-STEM Android App Improve Students' Scientific Literacy Skills? International Journal of Evaluation and Research in Education, 12(3), 1399–1407. https://doi.org/10.11591/ijere.v12i3.24794

Wylie, E. C., & Lyon, C. J. (2021). Formative Assessment for Learning: Science, Theory, and Practice. Review of Education, 9(2), e3283. https://doi.org/10.1002/rev3.3283

Zakiyyah, H., Sutopo, Y., Harianingsih, T., Subali, B., & Widiari, N. (2025). Literature Review: Differentiated E-Worksheets to Boost Primary School Learning Outcomes Between 2020–2025. Edunesia: Jurnal Ilmiah Pendidikan, 6(2), 808–821. https://doi.org/10.51276/edu.v6i2.1216

Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021). A Review of Artificial Intelligence (AI) in Education From 2010 to 2020. Complexity, 2021, Article 8812542. https://doi.org/10.1155/2021/8812542