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PENERAPAN REGRESI LOGISTIK ORDINAL UNTUK MENGANALISIS PENGARUH PENGGUNAAN APLIKASI ARTIFICIAL INTELLIGENCE TERHADAP PRESTASI AKADEMIK MAHASISWA (STUDI KASUS: MATEMATIKA, KIMIA, DAN BIOLOGI FMIPA UNIVERSITAS SRIWIJAYA)

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The use of Artificial Intelligence (AI) applications such as ChatGPT, Gemini, Grammarly, QuillBot, and Perplexity has become increasingly common among students of the FMIPA Universitas Sriwijaya. The high intensity of AI usage raises questions regarding the extent to which AI applications influence students’ academic achievement. This study aims to develop an ordinal logistic regression model to explain the effect of AI application usage on the academic achievement of Mathematics, Chemistry, and Biology students of FMIPA Universitas Sriwijaya in the seventh semester. This study used primary data collected through questionnaires from 179 students, consisting of 54 Mathematics, 63 Chemistry, and 62 Biology. Departments were selected using purposive sampling, while respondents were determined using accidental sampling. Academic achievement was measured using the GPA, and AI usage variables included intensity of use, academic context, academic ethics, academic self-efficacy, and dependence on AI. The results indicate that the model is not suitable for the Mathematics due to limited variability in academic achievement data dominated by GPA categories above 3.00, while the model is appropriate for the Chemistry and Biology. No significant variables were found for the Mathematics and Chemistry. Whereas in the Biology, the intensity of AI usage significantly affects academic achievement, indicating that Biology students benefit the most from AI due to the conceptual nature of their learning. In the combined analysis, dependence on AI emerges as the most dominant variable. Thus, the effect of AI application usage on students’ academic achievement is not uniform and is influenced by disciplinary context and patterns of AI usage.

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Detail Information

Series Title

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Call Number

T1944072026

Publisher

Indralaya : Prodi Ilmu Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya., 2026

Collation

xiii, 80 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

519.536 07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

analisis regresi
Prodi Ilmu Matematika

Specific Detail Info

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Statement of Responsibility

TUTI

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