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Journal : Information Technology Education Journal

Unraveling the Effects of AI Usage on Burnout among Programmers: An Apriori Algorithm Data Mining Approach Muhammad Fardan; Alwi, Ana Sulistiana; Darwing, Khalil Mubaraq; Surianto, Dewi Fatmarani; Putri Nirmala; Nurrahmah Agusnaya
Information Technology Education Journal Vol. 4, No. 3, August (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i3.9858

Abstract

Burnout is a growing problem across various industries, particularly among programmers who face high workloads and prolonged stress. In this digital era, the use of technologies such as AI can be a solution to reduce workloads and improve employee well-being. This study aims to identify how the use of AI can reduce burnout levels in programmers. The method used is a cross-sectional research design with data collection through a survey using the Google Form platform, and data analysis using descriptive techniques and the Apriori algorithm to find patterns in the relationship between the duration of AI use, workload, and burnout levels. The results show that the use of AI can help reduce burnout levels by lowering workloads, providing a basis for more effective interventions in the workplace.
A PLS-SEM Analysis of Basic Psychological Needs on Self-Regulation in Digital Learning: Insights from Self-Determination Theory Ahmad Faris Al Faruq; Muhammad Fardan; Awalia, Andi Dio Nurul; Nurrahmah Agusnaya; M.Miftach Fakhri
Information Technology Education Journal Vol. 4, No. 4, November (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i4.10813

Abstract

In the rapidly evolving digital age, technology-based learning has become integral to modern education, offering flexibility and accessibility while introducing challenges in student engagement and motivation. This study explores the relationship between basic psychological needs: autonomy, competence, and relatedness. Outlined in Self-Determination Theory (SDT) and self-regulated learning in digital environments. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM), data was collected from 737 students to examine how these needs impact self-regulation in digital learning. The findings reveal that fulfilling these psychological needs significantly enhances students' self-regulation, leading to improved learning outcomes. Autonomy, particularly when supported by digital tools, and competence, bolstered by immediate feedback and digital literacy, are crucial for fostering effective self-regulation. Relatedness, although less influential, remains important in maintaining motivation through social connections in online learning. The study contributes to the growing body of literature on SDT by highlighting the importance of creating digital learning environments that cater to students' psychological needs, thereby enhancing motivation and academic success.