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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 5 Documents
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Search results for , issue "Vol 1, No 2 (2026): January 2026" : 5 Documents clear
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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