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All Journal Artificial Intelligence in Educational Decision Sciences Journal of Vocational, Informatics and Computer Education Journal of Applied Artificial Intelligence in Education
Saipul Abbas
Sunchon National University
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences

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AI-Based Educational Decision Analytics: K-Means Clustering of University Students’ Digital Learning Readiness Using Limited and Full Attitude Schemes Annajmi Rauf; Elma Nurjannah; Fredy Ganda Putra; Saipul Abbas
Artificial Intelligence in Educational Decision Sciences Vol 1 No 1 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i1.19

Abstract

Purpose – Advancements in digital learning require students to be adequately prepared both psychologically and technologically. However, students’ attitudes toward digital learning have not yet been systematically mapped using data-driven segmentation approaches. This study aims to classify university students based on similarities in their attitudes toward digital learning using the K-Means clustering algorithm and to identify the most influential dimensions distinguishing levels of digital readiness.Methods – This study employed an exploratory quantitative design using survey data collected from 469 university students. Clustering was conducted using the K-Means algorithm implemented in the Orange Data Mining application. Two variable schemes were compared: a limited scheme comprising four constructs (Psychological Traits, Growth Mindset, Learner Motivation & Engagement, and Digital Competence) and a full scheme including six constructs with the addition of Digital Readiness & Mindfulness and Student Satisfaction. Data were normalized using Min–Max normalization, and cluster quality was evaluated using the Silhouette Coefficient.Findings – Results indicate that both schemes consistently produced two optimal clusters representing students with high and low levels of digital learning readiness. The highest Silhouette Coefficient values were obtained at K = 2 for both schemes (0.335 for the limited scheme and 0.323 for the full scheme). Psychological Traits and Learner Motivation & Engagement emerged as the most significant differentiating dimensions between clusters, followed by Digital Competence.Research limitations – The findings are limited to self-reported data and a single institutional context, which may constrain generalizability. Additionally, the cross-sectional design does not capture changes in student attitudes over time.Originality – This study contributes a comparative clustering framework that integrates psychological, motivational, and technological dimensions to map digital learning readiness. The results provide a practical foundation for designing adaptive and personalized digital learning strategies based on student readiness profiles.
Affective Drivers and Ethical Concerns Shaping AI Use Among University Students 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 : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v1i2.6

Abstract

The rapid growth of artificial intelligence (AI) use in higher education raises concerns about how students’ emotional states and the quality of their interactions with AI shape both affective engagement and ethical awareness in academic contexts. This study aims to examine the effects of emotional well-being, AI credibility, and AI interaction quality on students’ ethical awareness, with affective engagement positioned as a mediating mechanism. A quantitative cross-sectional survey was administered to higher education students who use AI tools for academic activities, and the proposed relationships were tested using PLS-based structural modeling with bootstrapping procedures. The findings indicate that emotional well-being (β = 0.549, p < 0.001) and AI interaction quality (β = 0.420, p < 0.001) significantly enhance affective engagement, whereas AI credibility shows no significant effect (β = –0.045, p = 0.342). Affective engagement has a significant positive influence on ethical awareness (β = 0.597, p < 0.001) and significantly mediates the effects of emotional well-being and interaction quality on ethical awareness, while no indirect effect is observed for AI credibility. Overall, these results imply that ethical awareness in student AI use is fostered more strongly through emotionally supportive experiences and high-quality human–AI interactions than through credibility perceptions alone, underscoring the need for human-centered AI integration and ethics-oriented guidance in higher 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.
Co-Authors Abil Alam Annajmi Rauf Aprilianti Nirmala S Elma Nurjannah Fredi Ganda Putra Melda Auliyah Zakina Nabilah Auliah Rahman Nur Wahyu Adrian Nurrahmah Agusnaya Santi Widyawati