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Journal of Vocational, Informatics and Computer Education
Published by PT. Lontara Digitech Indonesia
ISSN : 29884918     EISSN : 29886325     DOI : http://doi.org/10.61220/voice
Core Subject : Science, Education,
Computer Science & IT Education Other
Journal of Vocational, Informatics and Computer Education (VOICE) is a journal that publishes high-quality scientific articles in Indonesian or English in the form of research results (top priority) and or reviews in the field of computers and Informatics in the scope of vocational and early childhood education to higher education both in terms of technological development and education. The field of computers and informatics refers to software and hardware innovations, and more broadly, the application of technological processes in education. This journal covers, but is not limited to the following areas: 1. Vocational Education in the field of Informatics and Computer 2. Technology in computers and informatics 3. Computer and informatics learning 4. Education Method Innovation 5. Education Technology Innovation 6. Learning Innovation 7. Teaching Innovation VOICE publishes original empirical research (e.g., quantitative, qualitative, and mixed methods designs), project descriptions/evaluations, systematic literature reviews (including meta-analyses), methodological reviews, policy analyses, and theoretical or conceptual positions related to the use and innovative applications of technology in education. VOICE publishes the latest research and critical analysis on technology in education for educational theorists, researchers, practitioners, and policy makers.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 6 Documents
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Search results for , issue "Vol 3, No 2 (2025): Desember 2025" : 6 Documents clear
The Role of Anthropomorphism in Shaping Students’ Emotional Attachment to AIED: A Triangular Theory of Love Approach and PLS-SEM Analysis in Makassar Universities Asmi Ulfiah; Mappaita, Al Haytsam; Aprilianti Nirmala S; Pramudya Asoka Syukur; Andi Baso Kaswar; Riyama Ambarwati
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): Desember 2025
Publisher : PT. Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/pkd1h154

Abstract

In the digital learning era, Artificial Intelligence in Education (AIED) functions not only as an academic support tool but is also becoming an object of emotional attachment among students. While such attachment may enhance learning motivation, it also raises concerns about emotional dependence and its implications for students’ social and emotional well-being. This study investigates the effects of commitment, enthusiasm, emotional closeness, and anthropomorphic perceptions on students’ emotional dependence on AIED. A quantitative cross-sectional survey was conducted with 109 university students in Makassar using a 1–5 Likert-scale questionnaire. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The structural model explained 62.7% of the variance in emotional dependence on AI (R² = 0.627), indicating moderate to strong explanatory power. Emotional closeness (β = 0.324; t = 2.893; p = 0.004) and anthropomorphic perception (β = 0.440; t = 4.871; p < 0.001) significantly increased emotional dependence, whereas commitment to continued AI use (β = 0.092; t = 0.883; p = 0.377) and enthusiasm toward AI (β = 0.081; t = 0.901; p = 0.367) were not significant predictors. These findings suggest that emotional dependence is driven more by affective engagement and the perception of AI as socially human-like than by cognitive motivation or usage intention. AIED interaction therefore extends beyond functional support into a relational experience resembling interpersonal connection. Given the limited geographic scope, future studies should involve broader populations and employ mixed-method approaches to deepen understanding of emotional dynamics in AIED use.
Student Resistance to ChatGPT in Indonesia: Extended IRT with PLS-SEM Analysis Faiz Iqbal, Andi Muhammad; Nurul Hasmi; Devi Miftahul Jannah; Rizki Wahyu Hunian Putra
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): Desember 2025
Publisher : PT. Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/qnt8ve48

Abstract

The integration of Artificial Intelligence (AI) in higher education is growing, including the use of ChatGPT as a tool to assist students academically by improving access to information and promoting independent learning. Nonetheless, some students have shown reluctance due to worries about its reliability, academic morals, and changes in conventional learning principles. This research intends to explore how various barriers, such as usage barrier, value barrier, risk barrier, tradition barrier, image barrier, perceived cost barrier, and ethical considerations, contribute to student hesitance regarding ChatGPT. A quantitative method was utilized through Partial Least Squares Structural Equation Modeling (PLS-SEM), gathering data from an online survey of 77 students from Universitas Negeri Makassar. Findings reveal that only the risk barrier (β = 0. 417; p = 0. 006) and the tradition barrier (β = −0. 400; p = 0. 029) have a significant impact on resistance, with the risk barrier being the most influential, while the other factors showed no notable effects. These results suggest that psychological and cultural factors are more significant than practical obstacles in influencing resistance to generative AI and broaden the Innovation Resistance Theory (IRT) by factoring in ethical issues. The study advises creating teaching strategies that find a balance between using technology and maintaining academic honesty, while also promoting further research through multigroup and longitudinal methods.
Digital Ethics and Learning Autonomy in Artificial Intelligence in Education: The Mediating Role of Trust in AI Nabilah Rahman; Elsa Natasya; Andi Dio Nurul Awalia; Muh. Yusril Anam; Della Fadhilatunisa
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): Desember 2025
Publisher : PT. Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/s6eema73

Abstract

The rapid advancement of Artificial Intelligence in Education (AIED) has transformed digital learning practices while simultaneously raising critical concerns related to ethics, privacy, and user trust, which increasingly influence students’ ability to develop autonomous learning behaviors in AI-driven environments. This study aims to examine the relationships among Technology Readiness, Digital Learning Motivation, Digital Privacy Awareness, and Digital Ethics on Learning Autonomy, with Trust in AI serving as a mediating variable. A quantitative cross-sectional research design was employed involving 105 undergraduate students from Universitas Negeri Makassar, and data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that the proposed model explains 78.8% of the variance in Trust in AI and 84.3% of the variance in Learning Autonomy. Digital Learning Motivation shows a significant positive effect on Trust in AI and Learning Autonomy, while Digital Ethics also significantly influences both constructs; however, Technology Readiness and Digital Privacy Awareness do not significantly predict Trust in AI. Mediation analysis reveals that Trust in AI partially mediates the relationships between Digital Learning Motivation and Digital Ethics with Learning Autonomy. These findings demonstrate that psychological and ethical factors play a more decisive role than technical readiness in fostering trust and supporting autonomous learning in AIED contexts, highlighting the practical importance of integrating digital ethics education and motivational support into AI-based learning systems. Future research should employ longitudinal designs, broader samples, and additional variables such as AI literacy to further explore learning autonomy in AI-driven education.
Analysis of the Impact of Artificial Intelligence Technology on the Development of Students’ Academic Writing Skills in the Digital Learning Era Hidayat, Nur; Muafan, Wildan; Nurjannah, Elma; Akhmad Affandi; Rosidah
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): Desember 2025
Publisher : PT. Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/csm13a29

Abstract

The rapid advancement of Artificial Intelligence (AI) has transformed academic practices, particularly in supporting the development of students’ academic writing. However, empirical evidence explaining how AI utilization, automatic feedback, and personalized learning contribute to writing performance in higher education remains limited. This study examines the effects of AI utilization, AI-based automatic feedback, and AI-driven personalized learning on Students’ Academic Writing Skills (SAWS). Using an explanatory quantitative approach with a cross-sectional design, data were collected from 88 Indonesian university students through purposive sampling. Partial Least Squares–Structural Equation Modeling (PLS-SEM) was employed to evaluate the measurement and structural models. The findings show that Automatic Feedback Based on AI (AFBAI) is the strongest predictor of SAWS (β = 0.531; p = 0.000). The Utilization of AI Technology (UAIT) also has a significant positive effect (β = 0.290; p = 0.007), indicating that frequent use of AI tools contributes to improved writing skills. Conversely, Personalized Learning Based on AI (PLBAI) has no significant direct effect (β = 0.053; p = 0.350). The structural model demonstrates substantial predictive power with an R² value of 0.660. AI technologies play an essential role in enhancing academic writing performance, especially through automated feedback and consistent utilization. However, AI-driven personalized learning systems still require further optimization and deeper user engagement to meaningfully support the development of complex writing competencies.
Determinants of AI Trust in Education: The Role of Ethical Awareness, Ethical Risk, and Human-Centered Orientation Alam, Abil; Adrian, Nur Wahyu; Agusnaya, Nurrahmah; Saipul Abbas; Santi Widyawati
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): Desember 2025
Publisher : PT. Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/2sd9xt19

Abstract

The development of Artificial Intelligence in Education (AIED) is increasingly being used by university students in Indonesia, particularly through generative chatbots and AI-based learning systems to support assignment writing, reference searches, and material comprehension. Although offering efficiency and academic support, the use of AIED also raises ethical issues such as academic integrity, data security, bias, transparency, and responsibility, indicating that student trust is not only determined by the benefits of technology, but also by ethical awareness and human-centered orientation of use. This study aims to analyze the influence of AI Ethical Awareness, Perceived Ethical Risk, Perceived Usefulness, and Human-Centered Orientation on AI Trust, as well as the role of AI Trust in shaping Ethical Awareness in AIED among university students in Indonesia. The study used a quantitative approach with a cross-sectional survey design. Data were collected using a Likert scale questionnaire that measured six main constructs, then analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) to test the validity, reliability, and structural relationships between variables. The results showed that perceptions of the benefits of AIED, human-centered orientation, and ethical awareness contributed positively to the formation of students' trust in AIED, while perceptions of ethical risks tended to weaken that trust. Furthermore, trust in AIED plays an important role in increasing students' ethical awareness in the use of AI in academic environments. These findings emphasize the importance of strengthening AI ethics literacy and applying human-centered principles in AIED policies and designs to encourage more responsible use of AI in higher education.
Recognizing the Pluriversal Indigenous Ontologies for the Adoption of Gen AI in Glocal EFL Education: A Theoretical Reflection Alam, Md. Saiful; Asmawi, Adelina
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): Desember 2025
Publisher : PT. Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/rheq3849

Abstract

Since the emergence of generative artificial intelligence (hence, Gen AI), a newly created discursive wave has been pushing for the integration of the novel, non-human tool as both an inevitable and universally desirable ontology of technology-integrated language education. However, noticeably, this superficial celebratory narrative often overlooks locally valued pedagogical ontologies where Gen AI may appear as culturally foreign, pedagogically misaligned, and technologically impractical. Positing it within this ontological potential, the present paper takes a critical view on the universalist assumption of Gen AI-driven EFL teaching. By applying the method of theoretical reflections, the paper then argues for a “pluriversal” perspective that acknowledges localized epistemologies, classical pedagogies, and human-centered teaching traditions. In doing so, the paper draws on the key concepts, including glocalism, digital divides, technological foreignness, the value of pluriversality, contextualism and cultural-philosophical relativism. By highlighting these concepts, the paper contends that there are some legitimate antecedents for which some global South contexts may resist or remain unprepared or reluctant about the integration of GenAI in EFL practices. The discussion in this paper underscores that GenAI cannot be a one-size-fits-all solution. Otherwise, GenAI tooling of EFL education in indigenous lands may be positioned as a conflicting paradigm threatening the classical, humanist, unique pedagogical rhythm. Therefore, the paper calls for a localized theorization of Gen AI-integrated EFL education.

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