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All Journal Jurnal Nalar Pendidikan Artificial Intelligence in Lifelong and Life-Course Education
Afidatunisa, Shera
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Computer Science & IT Education Social Sciences Other

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COMPUTATIONAL THINKING SKILLS IN MATHEMATICS PROBLEM-SOLVING: A SYSTEMATIC LITERATURE REVIEW Afidatunisa, Shera; Juandi, Dadang
JURNAL NALAR PENDIDIKAN Vol 12, No 2 (2024): JURNAL NALAR PENDIDIKAN
Publisher : Lembaga Penelitian Mahasiswa Penalaran UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/jnp.v12i2.64838

Abstract

Computational thinking and Problem-Based Learning have educational benefits for learners and have been widely used in teaching and learning. However, research into integrating these theories into the teaching and learning process is scarce, leaving educators with little guidance in using constructive learning theory techniques. This research aims to thoroughly review and determine the place of research, level, learning materials, type of research, and instruments used to get an overview of how computational thinking, problem-solving, and learning methodologies lead to the successful application of computational thinking and problem-solving. From 2018-2023, this research tracks relevant published articles to identify gaps in implementing computational thinking and problem-solving. Research on computational thinking in problem-solving in the last five years has been widely used, but it still needs to be improved for other researchers, especially in provinces with few or even few researchers. Other researchers, especially in provinces that still have little or no research on computational thinking in problem-solving. have not examined computational thinking in problem-solving. In addition, the material that is taught to support computational thinking is the most widely used number pattern material at the junior high school level with test instruments and interviews.
Academic Dependency, AI Literacy, and Cognitive Offloading Predict Students’ Cognitive Ability in Generative AI Learning Andini Noviyanti Fitriani; Risaldy, Rezky; Rauf, Annajmi; Afidatunisa, Shera
Artificial Intelligence in Lifelong and Life-Course Education Vol 1 No 2 (2026): Artificial Intelligence in Lifelong and Life-Course Education
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aillce.v1i2.18

Abstract

Purpose – This study examines the cognitive effects of generative artificial intelligence use in higher education by testing whether academic dependency, AI literacy, and cognitive offloading predict students’ cognitive ability.Design/methods/approach – A quantitative cross-sectional survey was conducted with 93 undergraduate students at Universitas Negeri Makassar who actively use generative AI tools for academic purposes. Data were collected through a structured online questionnaire and analyzed using partial least squares structural equation modeling to evaluate measurement reliability and validity and to test structural relationships among academic dependency, AI literacy, cognitive offloading, and student cognitive ability.Findings – The structural model shows that academic dependency, AI literacy, and cognitive offloading positively and significantly predict student cognitive ability. AI literacy is the strongest predictor, indicating that students’ capacity to understand, evaluate, and use AI outputs critically is central to cognitive development. The findings also suggest that adaptive dependency can function as productive scaffolding, while strategic cognitive offloading may support higher-order thinking by reallocating limited cognitive resources.Research implications/limitations – The cross-sectional design limits causal inference, self-reported measures may introduce bias, and a single-institution context limits generalizability.Originality/value – This study provides integrated empirical evidence on the cognitive impact of generative AI use by jointly modeling academic dependency, AI literacy, and cognitive offloading, informing balanced AI literacy interventions and responsible AI governance in higher education.
Co-Authors Andini Noviyanti Fitriani Dadang Juandi Rauf, Annajmi Risaldy, Rezky