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All Journal Artificial Intelligence in Educational Decision Sciences Teladan: Jurnal Pendidikan Umum dan Karakter Journal of Vocational, Informatics and Computer Education Journal of Applied Artificial Intelligence in Education
Muh. Yusril Anam
Necmettin Erbakan University
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Computer Vision-Driven Classroom Analytics: Real-Time Attendance Verification and Student Focus Monitoring for Data-Informed Teaching Decisions Nurhikma; Aril; Mushaf; Muh. Yusril Anam
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.v1i1.7

Abstract

Purpose – Student attendance and learning activity monitoring are essential for ensuring instructional quality and academic accountability. However, conventional attendance methods remain inefficient, error-prone, and vulnerable to manipulation, while existing Computer Vision-based solutions often require high computational resources and focus on attendance or engagement separately. This study aims to develop an integrated, lightweight Computer Vision-based system for automatic student attendance recording and real-time focus monitoring suitable for resource-limited educational environments.Methods – This study employs a classical Computer Vision approach integrating Haar Cascade for face detection, Local Binary Patterns Histogram (LBPH) for face recognition, and rule-based eye detection for focus classification. The system automatically records attendance, tracks focus duration, and generates real-time digital reports. System performance was evaluated under controlled classroom conditions using accuracy, precision, recall, and F1-score.Findings – Experimental results demonstrate that the proposed system achieves high recognition reliability, with face detection and recognition accuracy reaching 100% in small-scale testing. The system operates efficiently with low latency and minimal computational requirements, while successfully monitoring multiple students simultaneously and generating structured attendance and focus duration reports in real time. Research limitations – The evaluation was conducted on a limited number of students under controlled conditions, which may restrict generalisability. Further testing in larger, more diverse classroom settings is required to validate system robustness.Originality – This study presents a unified and resource-efficient solution that integrates attendance validation and real-time focus monitoring within a single platform, offering practical value for schools seeking scalable and affordable learning analytics systems.
Efektifitas Kurikulum Cinta Dalam Pembelajaran Pendidikan Agama Islam Untuk Membentuk Karakter Moderasi Beragama Di Perguruan Tinggi Umum Nurhilaliyah; Muh. Yusril Anam
Teladan: Jurnal Pendidikan Umum dan Karakter Vol. 1 No. 2 (2025): DESEMBER
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jpuk.v1i2.40

Abstract

Studi ini bertujuan untuk melakukan pencarian mendalam mengenai kurikulum berbasis cinta dalam pembelajaran Pendidikan Agama Islam di perguruan tinggi umum agar dapat membentuk karakter moderisasi dalam beragama. Moderasi beragama sendiri adalah pendekatan keagamaan berupa penekanan sikap tengah, toleransi, dan penghargaan terhadap perbedaan yang menegaskan bahwa dalam dunia pendidikan, pendekatan merupakan aspek penting untuk membentuk karakter peserta didik yang cinta damai secara menyeluruh. Maka dari itu, kurikulum ini dibuat dengan tujuan untuk menanamkan nilai cinta kepada Tuhan, sesama manusia, lingkungan, dan bangsa sejak usia dini. Terdapat empat aspek utama dalam kurikulum ini, salah satunya adalah membangun cinta kepada Tuhan (Hablum Minallah). Kementerian Agama melalui Ditjen Pendidikan Islam telah menyiapkan buku panduan yang akan menjadi acuan bagi para pendidik dalam menyisipkan nilai- nilai cinta, toleransi, dan spiritualitas ke dalam pembelajaran. Metode yang digunakan dalam penelitian ini adalah tinjauan literatur terhadap kebijakan pendidikan, tulisan keagamaan, dan hasil penelitian akademik untuk menganalisis bagaimana Kurikulum Cinta dapat membentuk karakter moderasi beragama. Hasil penelitian menunjukkan bahwa kurikulum berbasis cinta berhasil membentuk karakter mahasiswa agar lebih menghargai perbedaan dan mendorong sikap saling menghormati.
Student Perceptions of AI in Learning: The Role of Credibility and Emo-tional Well-Being in Supporting Critical Thinking Skills Ummul Khaeri Masna; Arum Putri Rahayu; Sakinah Mawaddah; Nurrahmah Agusnaya; Muh. Yusril Anam
Journal of Applied Artificial Intelligence in Education Vol 1, No 1 (2025): July 2025
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v1i1.3

Abstract

The growing use of artificial intelligence (AI) tools (e.g., ChatGPT, Grammarly) in higher education is often claimed to enhance students’ critical thinking, yet perceived benefits remain inconsistent and may depend more on trust and affective experience than on technical features alone. This study aimed to examine students’ perceptions of AI for supporting critical thinking by testing five predictors—perceived AI credibility, AI quality, cognitive absorption, emotional well-being, and satisfaction—and their effects on overall AI perception. A quantitative cross-sectional survey was administered to 90 Indonesian university students (purposive sampling; ages 18–25) using 26 closed-ended Likert items (5-point scale) and three open-ended questions; data were analyzed in Jamovi using descriptive statistics, Pearson correlations, and multiple linear regression. The results indicated generally moderate perceptions of AI (item means ≈2.2–2.8), significant positive correlations among all variables (p < .001), and strong explanatory power of the regression model (R² = 0.737; adjusted R² = 0.720). In the multivariate model, emotional well-being (β_std = 0.267, p = 0.016) and AI credibility (β_std = 0.196, p = 0.043) were the only significant predictors, whereas AI quality, cognitive absorption, and satisfaction showed positive but non-significant effects. These findings imply that AI-supported learning interventions should prioritize credible, trustworthy AI outputs and pedagogical designs that promote positive emotional experiences (e.g., comfort, reduced stress, motivation) to strengthen perceived critical-thinking benefits; overall, affective and trust-related factors appear to be central drivers of students’ positive AI perceptions, warranting validation in larger and longitudinal studies
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.
Co-Authors Andi Dio Nurul Awalia Aril Arum Putri Rahayu Della Fadhilatunisa Elsa Natasya Mushaf Nabilah Rahman Nurhikma Nurhilaliyah Nurrahmah Agusnaya Sakinah Mawaddah Ummul Khaeri Masna