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M. Miftach Fakhri
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Journal of Vocational, Informatics and Computer Education
Published by PT. Academic Bright Collaboration
ISSN : 29884918     EISSN : 29886325     DOI : https://doi.org/10.66053/voice
Core Subject : Science, Education,
Computer Science & IT Education
1. Informatics and Computing Research addressing the design, development, implementation, and evaluation of computing technologies relevant to educational, professional, and digital learning environments, including but not limited to: Artificial Intelligence and Machine Learning Deep Learning and Neural Networks Data Science, Big Data, and Data Analytics Software Engineering and Software Development Computer Networks and Internet Technologies Cloud Computing and Distributed Computing Systems Internet of Things (IoT) and Smart Systems Human–Computer Interaction (HCI) and User Experience (UX) Intelligent Systems and Decision Support Systems Natural Language Processing and Computational Applications Cybersecurity and Information Security Emerging Computing Technologies and Digital Systems 2. Information Technology in Education Studies focusing on the design, integration, implementation, and evaluation of digital technologies in teaching and learning environments, including: Computer Science Education and Programming Education Artificial Intelligence in Education (AIED) Educational Data Mining and Learning Analytics Intelligent Tutoring Systems and Adaptive Learning Systems Digital Learning Environments and Online Learning Systems Learning Management Systems (LMS) and E-learning Platforms Immersive Learning Technologies (Virtual Reality, Augmented Reality, Extended Reality) Mobile Learning and Ubiquitous Learning Environments Technology-Enhanced Learning (TEL) and Digital Pedagogy Educational Software and Learning System Development Digital Assessment and Technology-Based Evaluation Systems Computational Thinking, AI Literacy, and Digital Literacy in Education 3. Vocational Technology Education Research examining the integration of computing technologies and digital innovation in vocational, technical, and professional education, including: Curriculum Development in Informatics and Computing Education Competency-Based Training and Digital Skill Development Teaching Factory and Industry 4.0 Learning Environments Smart Learning Environments for Technical and Vocational Education Work-Process Knowledge and Workplace Learning Work-Based Learning and Apprenticeship Systems Industry–Education Collaboration in Computing and Technology Fields Workforce Preparation for Digital and Technology-Driven Industries Digital Literacy and Cybersecurity Education in Vocational Contexts Professional Skills Development for the Digital Economy 4. Innovative Digital Learning and Educational Innovation Research exploring innovative pedagogical approaches, emerging technologies, and new learning ecosystems in digital and technology-enhanced education, including: Innovative Digital Pedagogy and Instructional Design Gamification and Game-Based Learning in Computing and Technology Education Project-Based Learning and Problem-Based Learning Supported by Technology Learning Innovation Using Artificial Intelligence and Intelligent Systems Automation and Smart Learning Technologies in Education Digital Transformation in Education and Training Institutions Emerging Educational Technologies and Future Learning Environments Smart Education Ecosystems and Data-Driven Learning Systems Educational Innovation for Developing Digital Competencies and Future Skills
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 8 Documents
 1 
Search results for , issue "Vol 3, No 2 (2025): December 2025" : 8 Documents clear
Recognizing the Pluriversal Indigenous Ontologies for the Adoption of Gen AI in Glocal EFL Education: A Theoretical Reflection Md. Saiful Alam; Adelina Asmawi
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): December 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v3i2.266

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.
Determinants of AI Trust in Education: The Role of Ethical Awareness, Ethical Risk, and Human-Centered Orientation Abil Alam; Nur Wahyu Adrian; Nurrahmah Agusnaya; Saipul Abbas; Santi Widyawati
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): December 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v3i2.265

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.
Analysis of the Impact of Artificial Intelligence Technology on the Development of Students’ Academic Writing Skills in the Digital Learning Era Nur Hidayat; Wildan Muafan; Elma Nurjannah; Akhmad Affandi; Rosidah
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): December 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v3i2.261

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.
Student Resistance to ChatGPT in Indonesia: Extended IRT with PLS-SEM Analysis Andi Muhammad Faiz Iqbal; Nurul Hasmi; Devi Miftahul Jannah; Rizki Wahyu Hunian Putra
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): December 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v3i2.264

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): December 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v3i2.262

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.
The Role of Anthropomorphism in Shaping Students’ Emotional Attachment to AIED: A Triangular Theory of Love Approach Asmi Ulfiah; Al Haytsam Mappaita; Aprilianti Nirmala S; Pramudya Asoka Syukur; Andi Baso Kaswar; Riyama Ambarwati
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): December 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v3i2.263

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.
Balancing Benefits and Risks of ChatGPT: Role of AI Ethics, Usage Habits, and Memory Loss in Learning Motivation and Performance Ridwan Daud Mahande; Rosidah
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): December 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v3i2.445

Abstract

This study investigates how AI ethics, ChatGPT usage habits, and memory loss influence learning motivation and learning performance in higher education. The research addresses growing concerns about cognitive and ethical implications of AI tool usage among students, especially in relation to motivation decline and learning outcomes. Although previous studies highlight the benefits of AI tools in enhancing learning, few have explored the negative cognitive and ethical consequences of overuse. This paper fills the gap by examining how learning motivation mediates the relationship between AI-related factors and learning performance through the lens of Self-Determination Theory (SDT). A quantitative, cross-sectional design was employed using purposive sampling. A total of 539 university students who have experience using ChatGPT in academic contexts participated by completing an online questionnaire. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This study contributes to the literature by integrating AI ethics, usage patterns, and cognitive outcomes into a unified model of academic motivation and performance. It extends Self-Determination Theory into AI-assisted learning environments and emphasizes learning motivation as a psychological bridge linking ethical and cognitive concerns to learning success. AI ethics, ChatGPT usage habits, and memory loss each have a significant im-pact on both learning motivation and learning performance. Learning motiva-tion serves as a mediating variable in the relationship between all three predic-tors and academic outcomes. Among the predictors, ChatGPT usage habits emerged as the strongest positive influence, whereas memory loss exhibited the most pronounced negative effect. Ethical concerns demonstrated a modest yet statistically significant positive effect, particularly when internalized as responsi-ble academic conduct. Future research should explore longitudinal effects of AI tool use on motivation and cognition, test other theoretical frameworks such as TAM or Cognitive Load Theory, and examine new variables like AI literacy, digital well-being, and academic resilience.
LAMACCA: Learning Analytics–Driven Adaptive Modular LMS Architecture for Collaborative Cyber Classrooms in Higher Education Rahmaniar; Sapto Haryoko; Muhammad Rais; Supriadi
Journal of Vocational, Informatics and Computer Education Vol 3, No 2 (2025): December 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v3i2.468

Abstract

This study addresses limitations of many Learning Management Systems (LMS) that operate primarily as content management and administrative platforms with minimal integration of learning analytics, adaptive learning, and collaborative cyber classroom environments. Despite advances in artificial intelligence, educational data mining, and immersive technologies, these components are often implemented as separate tools rather than parts of an integrated system. This fragmentation limits the ability of institutions to transform LMS into intelligent learning ecosystems that are data informed, adaptive, modular, and collaborative. This study aims to propose LAMACCA (Learning Analytics–Driven Adaptive Modular LMS Architecture for Collaborative Cyber Classroom) as a conceptual framework that integrates learning analytics, adaptive mechanisms, modular learning design, and collaborative cyber classroom interaction into a unified LMS architecture capable of supporting data informed pedagogy in higher education. A conceptual research design was conducted through systematic literature synthesis across four domains: learning analytics, adaptive learning systems, modular architectures, and collaborative virtual learning environments. Studies were examined using criteria of architectural relevance, pedagogical alignment, and cyber classroom integration to identify core principles and model their integration into a unified LMS structure. The study produces a four layer architectural model consisting of (1) a learning analytics engine as the intelligence core, (2) adaptive mechanisms for personalized learning pathways, (3) modular learning components for flexible instructional design, and (4) a collaborative cyber classroom environment that enables real time interaction supported by analytics based monitoring. This integration reconceptualizes the LMS from a passive content management platform into an adaptive, collaborative, and analytics driven cyber learning ecosystem.

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