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All Journal Al Qodiri : Jurnal Pendidikan, Sosial dan Keagamaan Artificial Intelligence in Educational Decision Sciences Journal of Vocational, Informatics and Computer Education
Stephen Amukune
Unknown Affiliation
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

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Analisis Perkembangan Local Wisdom Di Bumi Nusantara Pada Era Disrupsi Tekhnologi Jannah, Nur; Halim Soebahar; Moch. Chotib; Muhammad Noor Harisudin; Stephen Amukune
Al Qodiri : Jurnal Pendidikan, Sosial dan Keagamaan Vol. 23 No. 1 (2025): Al Qodiri : Jurnal Pendidikan, Sosial dan Keagamaan
Publisher : Lembaga Penelitian, Pengabdian kepada Masyarakat dan Publikasi Ilmiah (LP3M) Institut Agama Islam (IAI) Al-Qodiri Jember, Jawa Timur Indonesia bekerjasama dengan Kopertais Wilayah 4 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53515/qodiri.2025.23.1.156-167

Abstract

The development of digital technology has significantly changed the way the people of the archipelago preserve local wisdom. As a cultural heritage that reflects traditional values, local wisdom now faces challenges from the dominance of global culture, homogenization of values, and decreased involvement of the younger generation. On the other hand, digital technology offers opportunities as a tool for documentation, promotion, and preservation of local culture through platforms such as digital applications and social media. This study aims to analyze the opportunities and challenges faced by digitalization in preserving local wisdom in Indonesia. With a qualitative approach based on library research, data were obtained from relevant literature, such as books, journals, and official documents. The research findings show that digital technology can document and promote local wisdom through social media, applications, and video platforms. However, serious challenges arise from the risk of cultural commodification and inequality of access to technology in remote areas. Therefore, synergy between traditional methods and digital technology is very important to maintain the sustainability and authenticity of local wisdom amidst the flow of globalization. Keywords: local wisdom; digitalization; local wisdom; cultural preservation; digital era.
Predicting Student Dependency on ChatGPT for Academic Tasks Using Naive Bayes Classification Risha Febrianti; Sul Fitriana; Asrafah; Stephen Amukune
Artificial Intelligence in Educational Decision Sciences Vol 1 No 2 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i2.23

Abstract

Purpose – This study aims to predict and classify the level of student dependency on ChatGPT in completing academic tasks using the Naive Bayes algorithm to support data-driven decision making in higher education.Methods – A quantitative survey approach was employed involving 254 active undergraduate students from the Department of Informatics and Computer Engineering at a public university in Indonesia. Data were collected through a Likert-scale questionnaire measuring five behavioral indicators: purpose of ChatGPT use, interaction frequency and duration, understanding of generated outputs, trust in AI responses, and learning independence. The collected data were cleaned, numerically encoded, and labeled into three dependency categories (low, medium, high). A Naive Bayes classification model was implemented using Orange Data Mining and evaluated under three data split scenarios: 90:10, 80:20, and 70:30.Findings – The results indicate that the 70:30 data split achieved the highest classification performance, with an AUC value of 0.973, accuracy of 85.3%, F1-score of 0.866, and precision of 0.909. These results demonstrate that the Naive Bayes algorithm is effective in identifying distinct patterns of student dependency on ChatGPT based on multidimensional behavioral data.Research limitations – This study is limited to a single academic program and relies on self-reported questionnaire data, which may constrain the generalizability of the findings across different educational contexts.Originality – This study provides empirical evidence on the application of probabilistic classification models to assess student dependency on generative AI, contributing to educational decision sciences by informing institutional policies on balanced and responsible AI use in higher education.
The Concern Over Brain Rot from Generative AI Use Among Preservice Teachers: A UTAUT Approach Masna, Ummul Khaeri; Sidin, Udin Sidik; Mushaf; Stephen Amukune
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.347

Abstract

Purpose – The increasing use of generative AI on campus has raised concerns about a potential decline in students’ critical thinking skills. While the UTAUT theory is widely used to examine technology adoption, its relationship with the phenomenon of brain rot remains underexplored, particularly among preservice teachers. This study aims to analyze the factors associated with preservice teachers’ intention to use generative AI within the UTAUT framework, as well as to examine its association with tendencies toward brain rot.Method – A quantitative cross-sectional design was conducted with 243 preservice teachers from Universitas Negeri Makassar. Data were collected via a validated 30 item questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships between technology adoption constructs and brain rot tendencies.Findings – Social influence was the only significant predictor of behavioral intention to use AI (β = 0.269, p = 0.002). Behavioral intention, in turn, showed a strong positive association with brain rot tendencies (β = 0.817, p < 0.001), explaining 66.7% of the variance (R² = 0.667). Other UTAUT constructs, including performance expectancy and effort expectancy, were not significant predictors. However, given the cross-sectional design, these findings reflect statistical associations rather than causal relationships.Research Implication : Socially driven AI adoption is strongly linked to cognitive passivity, highlighting the need to extend UTAUT with cognitive risk factors and rethink how technology use impacts higher-order thinking.Conclusion – This study indicates that the adoption of AI among preservice teachers is associated with perceptions of declining cognitive abilities. These findings highlight the importance of promoting critical AI literacy and developing assessment approaches that emphasize deep cognitive engagement. Future research is recommended to employ longitudinal designs or incorporate control variables such as digital self-efficacy.
Co-Authors Asrafah Halim Soebahar Masna, Ummul Khaeri Moch. Chotib Muhammad Noor Harisudin Mushaf Nur Jannah Risha Febrianti Sul Fitriana Udin Sidik Sidin