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All Journal Journal of Vocational, Informatics and Computer Education Journal of Applied Artificial Intelligence in Education
Stephen Amukune
Kenyatta university
Computer Science & IT Education

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Enhancing Educator Quality and National Education Success: The Roles of Competence, Innovation, and Sustainable Support Indal Awalaikal; Andro Ruben Runtu; Surahmadani; Stephen Amukune
Journal of Applied Artificial Intelligence in Education Vol 1, No 1 (2025): July 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v1i1.5

Abstract

Persistent disparities in education quality in Indonesia shaped by uneven teacher capacity, limited innovation in technology-enabled pedagogy, and inconsistent long-term support continue to hinder the achievement of national education goals. This study aimed to examine how educator competence, pedagogical innovation, and sustainable support are perceived as key contributors to revitalizing educator quality as a foundation for national education success. A quantitative cross-sectional approach was used, collecting data from 106 undergraduate students across Indonesia through an online questionnaire (Google Forms) using convenience sampling. The instrument consisted of 25 Likert-scale items, covering educator competence (8 items), pedagogical innovation (9 items), and sustainable support (8 items); responses were analyzed descriptively using mean scores, dispersion, and categorical interpretation. The results indicate that participants perceived educator competence as Very Good (M = 1.78; SD = 0.564) and sustainable support as Very Good (M = 1.78; SD = 0.519), while pedagogical innovation was rated Good (M = 1.81; SD = 0.505), suggesting strong perceived readiness in competence and support but relatively slower progress in innovation practices. Respondents were predominantly female (62.3%) and mainly aged 21–23 (56.4%), with more than half in higher semesters (52.8), reflecting perspectives from students with substantial academic exposure. These findings imply that national education improvement requires sustaining competence development and strengthening durable institutional and policy support while accelerating equitable pedagogical innovation—especially effective technology integration in underserved areas. Overall, the study concludes that synergy among competence, innovation, and sustained support is essential for improving educator quality and advancing more inclusive outcomes
The Concern Over Brain Rot from Generative AI Use Among Preservice Teachers: A UTAUT Approach Ummul Khaeri Masna; Udin Sidik Sidin; 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 Andro Ruben Runtu Indal Awalaikal Mushaf Surahmadani Udin Sidik Sidin Ummul Khaeri Masna