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Development and Usability Perceptions of Spectral Crime Analysis Network Game with Augmented Reality for Ultraviolet-Visible Light Spectroscopy Hussain, Abbas Ali Iftikhar; Kamari, Azlan; Syamsudin, Frendi Ihwan; Harahap, Lenni Khotimah; Saputro, Sulistyo; Wiyarsi, Antuni; Sukkaew, Adulsman
AMPLITUDO : Journal of Science and Technology Innovation Vol. 5 No. 1 (2026): February
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/amplitudo.v5i1.517

Abstract

The Spectral Crime Analysis Network (SCAN) game was developed using the ADDIE model, used a hybrid augmented reality embedded game-based learning approach and aligned with the learning standards of ultraviolet–visible (UV–Vis) spectroscopy. This game is developed as a learning and reinforcement tool in analytical chemistry instrumentation for undergraduate chemistry students. Based on a survey study conducted on 265 respondents, it was found that the SCAN game demonstrated high usability with mean scores of 3.49 (SD = 0.489) for usefulness, 3.49 (SD = 0.474) for ease of use, 3.48 (SD = 0.504) for ease of learning and 3.53 (SD = 0.476) for satisfaction. Overall, SCAN supports cooperative, technology-enhanced game-based learning, fostering greater interactivity and engagement in chemistry education. This innovation is aligned with the United Nations' Sustainable Development Goal number 4 (SDG4), which is quality education that utilises technology and interactive 21st-century learning.
Enhancing Teacher Competence in Deep Learning–Oriented Instructional Design: A ChatGPT–Supported Training at SMPN 5 Indralaya Utara in the Era of Artificial Intelligence Ismet, Ismet; Ritonga, Ahmad Fitra; Pribadi, Imam Arif; Syamsudin, Frendi Ihwan
Unram Journal of Community Service Vol. 7 No. 1 (2026): March
Publisher : Pascasarjana Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ujcs.v7i1.1324

Abstract

The integration of Artificial Intelligence (AI) in education presents opportunities to enhance teacher competence and promote Deep Learning–oriented pedagogy. This study reports on a community service program conducted at SMP Negeri 5 Indralaya Utara, aimed at improving teachers’ skills in designing lesson plans that foster higher-order thinking and meaningful learning experiences with the support of ChatGPT. Ten teachers participated in a structured training program, which included pre-assessment, theoretical orientation, hands-on workshops, collaborative lesson plan development, mentoring, and post-assessment. Teachers’ understanding and application of Deep Learning principles were evaluated using pretest and posttest instruments, and the magnitude of improvement was measured using Normalized Gain (N-Gain). Results indicated an average N-Gain of 0.53, categorized as medium, demonstrating significant enhancement in formulating higher-order learning objectives, designing reflective and analytical activities, and utilizing AI to generate contextualized instructional materials. The findings highlight the effectiveness of practice-based, collaborative, and mentored training in improving both technological literacy and pedagogical skills. Moreover, teachers began transitioning from traditional knowledge-transfer approaches to constructivist, student-centered practices, positioning AI as a reflective pedagogical partner. This study provides evidence that structured professional development integrating AI can foster innovation, adaptability, and reflective instructional design, supporting Education 5.0 objectives and enhancing the quality of classroom learning experiences.