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All Journal Journal of Mathematics, Computation and Statistics (JMATHCOS) International Journal of Education, Vocational and Social Science
SH, Khaerun Nisa
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Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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The AI-Dependent Learning Phenomenon Exploring Students’ Dependence on AI in Completing College Academic Task SH, Khaerun Nisa; Sahade; Mufaridho, Lailatul Maziyah Wildan
International Journal of Education, Vocational and Social Science Vol. 5 No. 01 (2026): International Journal of Education, Vocational and Social Science( IJVESS)
Publisher : Cita konsultindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63922/ijevss.v5i01.2645

Abstract

This study investigates the emerging phenomenon of AI-dependent learning among university students, focusing on how reliance on generative artificial intelligence tools shapes their academic behaviors, learning motivation, and engagement with course tasks. The rapid advancement of AI applications, such as ChatGPT, has transformed students’ study habits, yet concerns arise regarding excessive reliance that may weaken independent cognitive processes. Using a qualitative descriptive method, this research involved 30 accounting students from State University of Makassar. Participants were selected through purposive sampling to ensure they had substantial exposure to AI-assisted academic work. The findings indicate that AI dependence is commonly driven by academic workload, tight deadlines, a desire for efficiency, and the perception that AI provides faster and higher-quality output. However, overreliance leads to diminished critical thinking, reduced confidence in completing tasks independently, and concerns related to academic integrity. This study highlights the urgent need for universities to establish AI literacy guidelines, promote responsible use, and design learning activities that balance technological assistance with genuine cognitive engagement.
Backtesting of the Value-at-Risk Based on GARCH Model (VaR-GARCH) in Measuring Stock Market Risk Mufaridho, Lailatul Maziyah Wildan; SH, Khaerun Nisa; Jalaludin, Paiz; Pratama, Muhammad Isbar
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/eyqd4722

Abstract

Accurate market risk measurement is a crucial aspect of stock portfolio management, particularly in volatile market conditions. One commonly used method for measuring market risk is Value-at-Risk (VaR). However, the conventional VaR approach often fails to capture the dynamics of volatile volatility. Therefore, this study aims to measure stock market risk using a GARCH-based Value-at-Risk approach and test the model's reliability using the Kupiec Proportion of Failures Test. The data used are daily stock price data processed into logarithmic returns. Return volatility is estimated using the GARCH(1,1) model, and the VaR value is calculated based on conditional volatility at a 5 percent significance level. VaR backtesting is then performed to identify violations and evaluate the model's validity using the Kupiec Test. The results of the study show that out of 653 observations, there were 27 VaR violations, with a Kupiec statistic value of 1.0909 and a p-value of 0.2963. A p-value greater than the significance level indicates that the VaR–GARCH model is statistically valid and able to measure market risk well. This study concludes that the VaR–GARCH approach is a reliable method in measuring stock market risk and can be used as a supporting tool in investment decision-making and risk management.
Co-Authors Lailatul Maziyah Wildan Mufaridho Muhammad Isbar Pratama Paiz Jalaludin Sahade