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M. Miftach Fakhri
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Journal of Applied Artificial Intelligence in Education
Published by PT. Lontara Digitech Indonesia
ISSN : -     EISSN : 31097081     DOI : -
Core Subject : Science, Education, Social,
Computer Science & IT Education Social Sciences Other
The Journal of Applied Artificial Intelligence in Education (JAAIE) is an open-access scholarly journal focusing on the practical applications of Artificial Intelligence (AI) in educational settings. It welcomes original contributions that explore the real-world implementation of AI to improve teaching, learning, and educational systems. Areas of interest include, but are not limited to: 1. Applied AI in Classroom Practice: Exploring practical AI applications in classroom teaching, including smart content delivery, virtual assistants, and automated support tools. 2. Intelligent Tutoring Systems: Investigating AI-driven systems that adapt to learners’ individual needs and provide personalized instructional support. 3. AI-Based Assessment and Feedback: Examining automated grading systems, formative assessment tools, and feedback mechanisms powered by AI. 4. Learning Analytics and Educational Data Mining: Investigating the use of AI-driven analytics and data mining techniques to analyze student learning behaviors, predict academic performance, and improve pedagogical strategies. 5. Adaptive and Personalized Learning Environments: Designing learning systems that adapt in real time based on student interaction, behavior, and performance. 6. Natural Language Processing in Education: Applying NLP techniques for language learning, automated writing evaluation, and intelligent conversational agents. 7. AI for Inclusive and Accessible Education: Leveraging AI to support diverse learners, including students with disabilities and those in underserved communities. 8. Ethics and Governance of AI in Education: Addressing issues of fairness, transparency, accountability, and data privacy in educational AI use. 9. AI-Enhanced Educational Technology Development: Innovations in EdTech tools and platforms that integrate AI for smarter learning solutions. 10. Policy, Strategy, and Implementation of AI in Education: Research on institutional frameworks, national strategies, and best practices for deploying AI in education systems.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 10 Documents
 1 
Exploring Factors Influencing MOOCs Usage Behavior and Technology Acceptance in Higher Education: An Analysis Using the UTAUT Model Kiki Awaliyah; Arum Putri Rahayu; Putri Olivia; Muh Ma’ruf Asya Perdana
Journal of Applied Artificial Intelligence in Education Vol 1, No 1 (2025): July 2025
Publisher : Lontara Digitech Indonesia

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Abstract

This study aims to analyze the acceptance of Massive Open Online Courses (MOOCs) by students at Makassar State University using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. This research uses a descriptive quantitative approach with data collection methods through online questionnaires. The research instrument was developed to measure eight dimensions of UTAUT: performance expectancy, effort expectancy, social influence, facilitating conditions, computer self-efficacy, attitude towards technology, behavioral intention, and actual use. The results showed that students have a fairly positive perception of the use of MOOCs as a digital learning medium. They considered MOOCs useful in increasing learning productivity, supporting online group discussions, and facilitating access to learning materials. However, some obstacles are still felt, especially limited technical knowledge, usage experience, and social support from the surrounding environment. These findings indicate the importance of strengthening digital literacy, technical training, and sustainable supporting policies to optimize the utilization of MOOCs in the learning process. This research is expected to contribute to the development of digital learning strategies in higher education, especially in integrating technology effectively into the teaching and learning process in the digital era.
Analyzing the Continuance Intention to Use AI News Anchors for Daily Information Needs: An Expectation Confirmation Theory Approach Rahayu, Alyah; Andika Isma; Ramadani, Fitra
Journal of Applied Artificial Intelligence in Education Vol 1, No 1 (2025): July 2025
Publisher : Lontara Digitech Indonesia

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Abstract

The development of artificial intelligence (AI) technology has brought significant changes in the broadcasting world, one of which is the emergence of AI-based news anchors. This technology allows news delivery by virtual characters capable of conveying information with voice and expressions almost identical to humans. Although it provides efficiency and consistency in news presentation, its impact on the emotional connection between the audience and the news anchor, as well as the ethical and legal issues that arise, has not been widely studied. This research aims to assess how audiences receive the use of AI as news anchors and measure its influence on audience satisfaction and intention to use the technology. Using a quantitative approach, this study explores audience perceptions regarding trust, information quality, and innovation in the context of news broadcasting. It is expected that the results of this research will provide deeper understanding about the acceptance and challenges faced by AI news anchors in the future media industry
Enhancing Educator Quality and National Education Success: The Roles of Competence, Innovation, and Sustainable Support Awalaikal, Indal Awalaikal; Awalaikal, Indal; Andro Ruben Runtu; Surahmadani
Journal of Applied Artificial Intelligence in Education Vol 1, No 1 (2025): July 2025
Publisher : Lontara Digitech Indonesia

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Abstract

This study aims to analyze the contribution of competence, innovation, and sustainable support to the success of national education. The research employed a quantitative approach with a cross-sectional design, involving 106 undergraduate students as respondents through a 5-point Likert-scale questionnaire. Descriptive analysis was conducted using Jamovi and Microsoft Excel to evaluate aspects of educator competence, pedagogical innovation, and sustainable support. The results show that educator competence and sustainable support were rated as very good by the respondents, while pedagogical innovation was categorized as good but requires further development. This study highlights the importance of synergy between educator competence, pedagogical innovation, and sustainable support in achieving an inclusive and adaptive national education system
Student Perceptions of AI in Learning: The Role of Credibility and Emotional Well-Being in Supporting Critical Thinking Skills Masna, Ummul Khaeri; Arum Putri Rahayu; Mawaddah, Sakinah; Nurrahmah Agusnaya; Muh. Yusril Anam
Journal of Applied Artificial Intelligence in Education Vol 1, No 1 (2025): July 2025
Publisher : Lontara Digitech Indonesia

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Abstract

This study explores university students’ perceptions of artificial intelligence (AI) in enhancing critical thinking skills within higher education. Using a quantitative cross-sectional design, data were collected from 90 Indonesian students who had used AI tools such as ChatGPT or Grammarly in academic contexts. The study examined five independent variables: perceived credibility of AI, AI quality, cognitive absorption, emotional well-being, and user satisfaction, and their relationship to students’ overall perception of AI benefits. Descriptive statistics revealed that students’ perceptions were generally moderate, with emotional well-being and perceived credibility emerging as significant predictors of positive perceptions. Multiple linear regression showed that emotional well-being had the strongest influence, followed by credibility. These findings emphasize the importance of affective experiences and trust in shaping acceptance and effective use of AI in learning. This research contributes to a deeper understanding of how AI integration can support 21st-century skills development, and suggests the need for emotionally engaging and trustworthy AI systems in educational environments
Effects of Artificial Intelligence Integration on Design Mindset, Creativity, and Reflection Amri, Khaerul; Intan Novita Kowaas; Andro Ruben Runtu; Muhajji, Rifky
Journal of Applied Artificial Intelligence in Education Vol 1, No 1 (2025): July 2025
Publisher : Lontara Digitech Indonesia

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Abstract

Artificial intelligence (AI) improves the efficiency of the design process, helps with fast and accurate data analysis, and aids creativity with innovative ideas. Using a quantitative approach with a cross-sectional design, this study looked at how the incorporation of AI impacts students' creativity, design mindset, and reflection. 96 people responded using an online questionnaire. The results showed that artificial intelligence had a moderate positive effect on design mindset, especially in terms of concept building, problem finding, and design iteration. AI also helped students become more creative, develop imagination, and become more productive. Critical analysis, learning from mistakes, and a deeper understanding of the creative process are enabled by AI. The study found that AI integration can enhance human-machine collaboration to produce more innovative and reflective design outcomes. However, to implement it successfully, a balance between AI automation and human control is required. This study provides insights for educational institutions on how best to utilize AI in learning design and creativity
AI Hallucinations in AIED and Their Impact on Students' Intentions to Behave Honestly: A PLS-SEM Analysis of JTIK UNM Students Desitha Cahya; Putri Ramdani; Annajmi Rauf; Andi Baso Kaswar; M Miftach Fakhri
Journal of Applied Artificial Intelligence in Education Vol 1, No 2 (2026): January 2026
Publisher : Lontara Digitech Indonesia

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Abstract

Artificial Intelligence in Education (AIED) is increasingly used to support learning efficiency, personalization, and academic productivity. However, issues such as AI hallucination, algorithmic bias, limited system Transparency, and variations in students’ Digital Literacy present ethical risks that may undermine academic integrity. These challenges indicate a gap between the ideal function of AI as a learning assistant and its practical use, which remains prone to plagiarism and misuse. This study aims to analyze how students’ perceptions of algorithmic bias, Transparency in AI systems, and Digital Literacy influence their Honest Behavior when using AI for academic purposes. A quantitative research method was employed using a survey design, and data were analyzed through Partial Least Squares Structural Equation Modeling to empirically examine the relationships among variables. The results show that algorithmic bias, Transparency, and Digital Literacy each have a positive effect on honest behavior, with Digital Literacy emerging as the strongest predictor. These findings suggest that students with better digital skills and awareness of AI mechanisms are more capable of using AI responsibly and ethically. This study concludes that higher education institutions need to strengthen policies related to ethical AI use and enhance students’ Digital Literacy to foster an academically honest environment. The study contributes to the development of ethical behavior frameworks in the AIED context and provides considerations for institutions to improve integrity in AI-assisted learning.
How Does AI Literacy Redefine Social Responsibility? Exploring the Interplay Between Digital Literacy and Ethical Awareness in Shaping Digital Citizenship (PLS-SEM Approach) Misbahuljannah; Riqqah Dhian Shefira; Devi Miftahul Jannah; Muh. Yusril Anam; Rosidah
Journal of Applied Artificial Intelligence in Education Vol 1, No 2 (2026): January 2026
Publisher : Lontara Digitech Indonesia

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Abstract

The rapid integration of artificial intelligence (AI) into digital learning environments has increased the demand for competencies that support critical, ethical, and responsible technology use. This study examines the influence of AI Literacy, Digital Literacy, and Ethical Awareness on university students’ Social Responsibility. Using a quantitative cross-sectional survey, data were collected from 100 students in the Informatics and Computer Education program. The analysis employed Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results reveal that Digital Literacy (β = 0.397; p = 0.001) and Ethical Awareness (β = 0.615; p = 0.000) positively and significantly affect Social Responsibility, whereas AI Literacy demonstrates a negative but significant effect (β = –0.151; p = 0.022). These findings highlight the need for balanced technological and ethical competencies to cultivate responsible digital citizenship. The study suggests integrating ethical and digital literacy training into higher education curricula and encourages future research involving broader samples and longitudinal designs.
The Impact of Career Anxiety, Dehumanization, and Perceived Algorithmic Fairness on AI Anxiety among Indonesian University Students: A PLS-SEM Study Mustamin; Ahmad Syarif Hidayatullah; Putri Nirmala; Akhmad Affandi; Della Fadhilatunisa
Journal of Applied Artificial Intelligence in Education Vol 1, No 2 (2026): January 2026
Publisher : Lontara Digitech Indonesia

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Abstract

The rapid integration of artificial intelligence (AI) in higher education has raised concerns about students’ psychological readiness, particularly regarding AI Anxiety. This study examines the influence of Career Anxiety, Dehumanization, and Perceived Algorithmic Fairness on AI Anxiety among Indonesian university students. Using an explanatory survey design, data were collected from 70 students who actively use AI-based learning tools. The analysis employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the measurement and structural models. The results show that Career Anxiety positively affects AI Anxiety (β = 0.234, t = 1.691), while Dehumanization emerges as the strongest predictor (β = 0.415, t = 2.958). In contrast, Perceived Algorithmic Fairness has no significant effect (β = 0.103, t = 0.740). The model explains a substantial portion of variance in AI Anxiety with an R² value of 0.482. These findings highlight that emotional and identity-related factors are more influential than evaluative perceptions of fairness in shaping AI Anxiety. The study emphasizes the need for human-centered AI integration, improved AI literacy, and targeted support to mitigate student anxiety in AI-supported learning environments
Affective Dynamics and Ethics of AI Use among Higher Education Students: A PLS-SEM Study Nabilah Auliah Rahman; Melda Auliyah Zakina; Aprilianti Nirmala S; Saipul Abbas
Journal of Applied Artificial Intelligence in Education Vol 1, No 2 (2026): January 2026
Publisher : Lontara Digitech Indonesia

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Abstract

The use of artificial intelligence (AI) in higher education is increasing rapidly, raising questions about how emotional well-being, AI credibility, and AI interaction quality shape students’ affective engagement and ethical awareness. This study employs a quantitative cross-sectional design and analyzes data using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that emotional well-being (β = 0.549, p < 0.001) and AI interaction quality (β = 0.420, p < 0.001) significantly affect affective engagement, while AI credibility has no significant effect (β = –0.045, p = 0.342). Affective engagement significantly influences ethical awareness (β = 0.597, p < 0.001) and mediates the effects of emotional well-being and interaction quality. The model explains substantial variance in affective engagement (R² = 0.561) and moderate variance in ethical awareness (R² = 0.357). These findings highlight the importance of emotional and interactional factors in fostering ethical awareness and support the need for human-centered and ethically grounded AI integration in education.
The Influence of AI Personalization, Feedback, and Usage on Student Engagement: A PLS-SEM Study on the Mediating Role of Technology Engagement in Indonesian Higher Education Ahmad Abdullah Aswad; Tegar Angbirah Parerungan; Elma Nurjannah; Muh. Akbar
Journal of Applied Artificial Intelligence in Education Vol 1, No 2 (2026): January 2026
Publisher : Lontara Digitech Indonesia

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Abstract

The rapid integration of Artificial Intelligence (AI) in higher education has the potential to transform learning, yet access to technology does not guarantee active student participation2. Concrete evidence regarding the specific impact of AI features on psychological engagement remains limited. This study aims to examine the structural relationship between AI features (Usage, Personalization, and Feedback) and Student Engagement, specifically investigating the mediating role of Technology Engagement3. Methods: This study employed a quantitative approach with a non-experimental cross-sectional design4. Data were collected from 71 undergraduate students in Eastern Indonesia, predominantly from information technology majors5. The structural model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4 software to test direct and indirect effects6. Results: The analysis revealed that the model possesses substantial predictive power, explaining 74.4% of the variance in Technology Engagement (R^2=0.744) and 66.4% in Student Engagement (R^2=0.664). AI Personalization & Adaptivity emerged as the most dominant predictor, significantly influencing Technology Engagement (β =0.516, p < 0.001) and Student Engagement directly (β=0.310, p =0.010). Conversely, AI Usage and Feedback showed no significant direct effects on Student Engagement but demonstrated significant positive indirect effects through Full Mediation of Technology Engagement99. Conclusion: The findings confirm that Technology Engagement acts as a critical "gatekeeper" mechanism. The intensity of AI usage and automatic feedback alone is insufficient to drive academic engagement unless students first establish a strong sense of control and psychological engagement with the technology. Thus, educational strategies should prioritize adaptive personalization over mere instrumental use.

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