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All Journal International Journal of Science and Society (IJSOC) TOFEDU: The Future of Education Journal
Prasetya, Muhammad Gibran Alif
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Education Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences Other

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A Systematic Review of Artificial Intelligence-Based Computer Adaptive Testing (CAT) and Item Response Theory for Enhancing the Effectiveness of Science Learning Assessment Prasetya, Muhammad Gibran Alif; Widiyatmoko, Arif; Rusilowati, Ani
International Journal of Science and Society Vol 7 No 4 (2025): International Journal of Science and Society (IJSOC)
Publisher : GoAcademica Research & Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54783/ijsoc.v7i4.1581

Abstract

The advancement of technology has accelerated the adoption of Computerized Adaptive Testing (CAT) in educational assessment due to its ability to dynamically adjust item difficulty levels, thereby producing more precise, efficient, and valid measurements compared to conventional tests. While Item Response Theory (IRT) serves as the primary psychometric foundation of CAT, traditional IRT implementation faces computational challenges because ability estimation requires lengthy iterative processes, resulting in reduced system responsiveness. To address this issue, Artificial Intelligence (AI), particularly Fuzzy Logic, offers a promising solution through rapid inference mechanisms and monotonic reasoning that can adaptively map students’ cognitive abilities to corresponding item difficulty levels. This study aims to develop a hybrid CAT system that integrates Fuzzy Logic for fast inference with IRT as a robust and valid psychometric framework in the context of science learning. The research employs a systematic literature review using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, encompassing the stages of Identification, Screening, and Inclusion of relevant studies. The findings indicate that the integration of AI/ML with IRT in CAT consistently enhances assessment accuracy and efficiency. Algorithms such as Maximum Information (MI) and Expected a Posteriori (EAP) effectively reduce test length without compromising reliability, while Fast Adaptive Cognitive Diagnosis (FACD) improves early-stage ability prediction. Furthermore, Fuzzy Logic demonstrates strong effectiveness in selecting adaptive test items aligned with students’ ability levels. The study concludes that developing CAT systems based on AI and IRT yields adaptive, personalized, efficient, and diagnostic evaluation mechanisms that support personalized science learning.
Systematic Literature Review on the Integration and Role of Artificial Intelligence in the Development of Computer Adaptive Testing (CAT) Prasetya, Muhammad Gibran Alif; Widiyatmoko, Arif
The Future of Education Journal Vol 4 No 9 (2025): #1
Publisher : Lembaga Penerbitan dan Publikasi Ilmiah Yayasan Pendidikan Tumpuan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61445/tofedu.v4i9.1331

Abstract

The integration of Artificial Intelligence (AI) has sparked a fundamental transformation in assessment systems, shifting from static methods to dynamic and personalized paradigms through Computer Adaptive Testing (CAT). This study aims to map the state-of-the-art AI integration in CAT, identify technological evolution trends, and analyze the contributions of dominant algorithms in optimizing the core components of tests. A Systematic Literature Review (SLR) following the PRISMA guidelines was used to synthesize data from Scopus, WoS, IEEE, ERIC, and Google Scholar databases from 2020 to 2025. Macro analysis was performed with VOSviewer, and micro synthesis with NVivo. The results indicate an evolution trend from basic machine learning integration in 2020 towards automation systems based on Reinforcement Learning and Generative AI by 2025. Algorithms such as Deep Learning and Multi-Objective Optimization have been shown to improve the precision of ability estimation, while empirical findings demonstrate that the use of Model Trees (M5P) can reduce item counts by 85%–93% on clinical instruments without compromising score accuracy. In conclusion, AI is transforming CAT into a smart, efficient, and personalized assessment ecosystem, with challenges in transparency (explainability) and algorithmic bias as key priorities for the future development of evaluation systems.
Co-Authors Ani Rusilowati Arif Widiyatmoko, Arif