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Journal : Information Technology Education Journal

Integrating Technology with Academic Success by Evaluating ChatGPT’s Quality Compatibility and Impact on Student Performance Irwansyah Suwahyu; Yohana Rara; Nur Syafitra Ramadhani; Rosidah; Putri Nirmala; Nurrahmah Agusnaya; Syukur, Pramudya Asoka
Information Technology Education Journal Vol. 4, No. 2, May (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i2.8532

Abstract

The use of artificial intelligence (AI) technology in higher education has become an important element to improve students' academic performance. ChatGPT, as one of the generative AI applications, offers speed, ease of use, and relevant responses to support students' learning activities. This study aims to analyze the relationship between overall technology quality, technology characteristics, technology task suitability, compatibility, and performance impact in using ChatGPT on students' academic performance. This study used a quantitative approach with a cross-sectional design and purposive sampling technique involving 182 active student respondents using ChatGPT. Data were collected through an online questionnaire using a 5-point Likert scale covering five main variables. The results of descriptive analysis show a mean value that illustrates that students generally have a positive perception of the use of ChatGPT in supporting their academic activities. These findings suggest that ChatGPT has great potential in improving student productivity and comprehension, although service quality, task suitability, and function compatibility need to be continuously improved to better suit academic needs. Thus, the implementation of ChatGPT in higher education needs to be planned adaptively and ethically in order to optimally support students' learning success in the digital era
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.