IJIET 2026 Vol.16(5): 1252-1266
doi: 10.18178/ijiet.2026.16.5.2594
doi: 10.18178/ijiet.2026.16.5.2594
AI-Powered Computer-Based Assessment for Literacy and Numeracy: Multi-School Validation in Indonesian Secondary Education
Anna Fitri Hindriana1,*, Rio Priantama2, Ina Setiawati3, and Alin Rizki Pratami3
1. Program Studi Pendidikan Biologi, Sekolah Pascasarjana, Universitas Kuningan, Kuningan, Indonesia
2. Program Studi Teknik Informatika, Fakultas Komputer, Universitas Kuningan, Kuningan, Indonesia
3. Program Studi Pendidikan Biologi, Fakultas Keguruan dan Ilmu Pendidikan, Universitas Kuningan, Kuningan, Indonesia
Email: anna@uniku.ac.id (A.F.H.); rio.priantama@uniku.ac.id (R.P.); ina.setiawati@uniku.ac.id (I.S.); alinpratami@gmail.com (A.R.P.)
*Corresponding author
2. Program Studi Teknik Informatika, Fakultas Komputer, Universitas Kuningan, Kuningan, Indonesia
3. Program Studi Pendidikan Biologi, Fakultas Keguruan dan Ilmu Pendidikan, Universitas Kuningan, Kuningan, Indonesia
Email: anna@uniku.ac.id (A.F.H.); rio.priantama@uniku.ac.id (R.P.); ina.setiawati@uniku.ac.id (I.S.); alinpratami@gmail.com (A.R.P.)
*Corresponding author
Manuscript received September 1, 2025; revised October 15, 2025; accepted December 5, 2025; published May 15, 2026
Abstract—Accurately diagnosing literacy and numeracy competencies remains a persistent challenge in developing countries despite significant investments in educational technology. This study developed and validated Asesmen Kompetensi Minimum (AKM) Online, an Artificial Intelligence (AI)-enhanced Computer-Based Assessment (CBA) system that integrates Fisher–Yates Shuffle and Regular Expression algorithms to enable secure test randomization, automated response validation, and real-time diagnostic feedback at scale. Through multi-school implementation across three contrasting socioeconomic contexts (N = 552), the system generated 17,049 student responses and demonstrated high technical reliability (AI scoring accuracy = 94.35%). Results revealed severe and consistent numeracy deficiencies (95.8–100% requiring intervention) and substantial variation in literacy needs (53.9–78.46%), indicating that contextual factors influence literacy more strongly than numeracy. Mathematical reasoning showed complete failure (0% proficiency), a finding corroborated through expert validation, cross-context replication, and alignment with national and international benchmarks). Expert evaluations confirmed high system quality in both educational assessment (81%) and informatics performance (82%). The study provides empirical evidence that AI-supported CBA can overcome major limitations of existing national assessments by enabling whole-population testing with immediate, actionable competence profiles. The scalability, transparency, and resource-efficient design of AKM Online underscore its potential for broader adoption in developing countries seeking reliable, evidence-based approaches to foundational literacy and numeracy assessment.
Keywords—computer-based assessment, Artificial Intelligence (AI), literacy, numeracy, educational technology
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Keywords—computer-based assessment, Artificial Intelligence (AI), literacy, numeracy, educational technology
Cite: Anna Fitri Hindriana, Rio Priantama, Ina Setiawati, and Alin Rizki Pratami, "AI-Powered Computer-Based Assessment for Literacy and Numeracy: Multi-School Validation in Indonesian Secondary Education," International Journal of Information and Education Technology, vol. 16, no. 5, pp. 1252-1266, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).