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All Journal Vokatek : Jurnal Pengabdian Masyarakat Journal of Embedded Systems, Security and Intelligent Systems Artificial Intelligence in Lifelong and Life-Course Education Artificial Intelligence in Educational Decision Sciences Journal of Vocational, Informatics and Computer Education Journal of Applied Artificial Intelligence in Education Journal of Education for Creativity and Innovation
Annajmi Rauf
Universitas Negeri Makassar
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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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.
Analisis Tingkat Penerimaan Mahasiswa terhadap Pembelajaran Online berbasis Gamifikasi Pada Perguruan Tinggi Petrus Jacob Pattiasina; Ilma Wulansari Hasdiansa; Annajmi Rauf; Nur Alamsyah; Sulpiani; Siti Auliyani Saleh
Journal of Vocational, Informatics and Computer Education Vol 2, No 1 (2024): Juni 2024
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/voice.v2i1.24

Abstract

Pembelajaran daring sering kali menghadapi tantangan terkait motivasi dan keterlibatan mahasiswa. Tujuan penelitian ini adalah untuk menganalisis tingkat penerimaan mahasiswa terhadap pembelajaran daring berbasis gamifikasi. Penelitian ini menggunakan metode kuantitatif dengan desain cross-sectional, mengumpulkan data melalui kuesioner dari 64 responden. Hasil penelitian menunjukkan bahwa elemen gamifikasi, khususnya aspek philanthropist dan socializer, memberikan dampak terbesar terhadap motivasi dan partisipasi mahasiswa dengan nilai rata-rata 3,96 dan 3,9. Sebaliknya, aspek achiever dan explorer memiliki pengaruh yang lebih kecil. Penelitian ini menyimpulkan bahwa penerapan gamifikasi dapat meningkatkan keterlibatan mahasiswa dalam pembelajaran daring, dan menyarankan penelitian lebih lanjut untuk mengeksplorasi faktor-faktor individu yang mempengaruhi penerimaan gamifikasi.
Analisis Pengaruh Chatbot AI terhadap Pengalaman Mahasiswa Menggunakan E-commerce Israwati Hamsar; Nur Febrianti; Amelia Uswatun Khasanah; Annajmi Rauf; Elma Nurjannah
Journal of Vocational, Informatics and Computer Education Vol 2, No 2 (2024): December 2024
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/voice.v2i2.20247

Abstract

As e-commerce platforms increasingly adopt AI technologies, the effectiveness of chatbot integration in enhancing user experience among students remains underexplored. This study aims to analyze the impact of AI-powered chatbots on the shopping experience of university students in Makassar. Using a quantitative approach, data were collected via structured questionnaires from 88 student respondents and analyzed through descriptive and inferential methods. The findings reveal that students perceived the chatbot as highly capable of solving complex inquiries, offering relevant solutions, and delivering efficient service. The chatbot's responsiveness and ease of use received high average scores, indicating strong user satisfaction. Furthermore, the chatbot positively influenced customer satisfaction, including increased purchase intention and likelihood to recommend. These results suggest that AI chatbots significantly contribute to enhancing service quality in e-commerce and should be strategically utilized to meet the expectations of young digital consumers.
Dukungan Sistem dan Model Gamifikasi Untuk Meningkatkan Partisipasi Actor dalam Lingkungan E-learning Annisa Soedali Putri; Diva Armelia; Putwal Arjuna; Annajmi Rauf
Journal of Education for Creativity and Innovation Vol. 1 No. 1 (2025): Agustus
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kemajuan teknologi digital telah mendorong perubahan signifikan dalam pendidikan, termasuk pemanfaatan gamifikasi untuk meningkatkan partisipasi dalam e-learning. Penelitian ini bertujuan untuk menganalisis peran sistem dan model gamifikasi dalam mendorong motivasi serta keterlibatan mahasiswa pada lingkungan pembelajaran digital. Metode yang digunakan adalah penelitian kuantitatif dengan desain cross-sectional, melibatkan 35 mahasiswa Jurusan Teknik Informatika yang dipilih sebagai responden melalui kuesioner. Hasil penelitian menunjukkan bahwa penerapan gamifikasi melalui elemen-elemen seperti poin, lencana, dan leaderboard mampu meningkatkan motivasi belajar, partisipasi aktif, serta keterlibatan mahasiswa dalam proses pembelajaran. Kesimpulannya, dukungan sistem e-learning yang terintegrasi dengan gamifikasi memberikan kontribusi penting terhadap efektivitas pembelajaran, serta dapat menjadi strategi yang relevan untuk menggeser pendekatan belajar dari teacher-centered ke student-centered learning.
Development of Cloud-Based Taskify Application For Time Management Nur Fadhylah As; Muh. Rahmat Wahyudi JY; Annajmi Rauf; Pramudya Asoka Syukur; Andi Dio Nurul Awalia; M. Miftach Fakhri
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 2 (2024): July 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i2.5041

Abstract

In the 21st century digital era, advances in technology and information have developed rapidly, one of which is cloud computing. This study aims to design a cloud-based task management application called TaskIfy using firebase technology and agile methods. TaskIfy was designed to help users manage their time and daily activities more effectively. The Agile method was used in two sprint cycles to ensure iterative development and responsiveness to user feedback. The main features implemented include authentication, task management, search, and calendars. Black box testing was conducted to ensure the functionality of the application. The results showed that TaskIfy successfully improved user efficiency and productivity in managing schedules and completing tasks. However, some additional features have not been developed, such as better calendar integration and collaboration features. Future research can focus on developing these features to optimize the user experience. The main contribution of this research is the implementation of TaskIfy as a practical tool for effective time management, combining cloud computing technology and agile methodology to improve efficiency in everyday life.
StudySync Mobile Application Design for Student Academic Activity Management Based on SQLite Database Arsyanda; M. Miftach Fakhri; Pramudya Asoka Syukur; Devi Miftahul Jannah; Elma Nur Jannah; Annajmi Rauf
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 3 (2024): November 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i3.5042

Abstract

The development of information and communication technology has made significant contributions in various sectors, including education. Mobile applications have become an effective solution in improving time management and reducing academic procrastination among students. This research aims to design and develop a mobile application called StudySync that utilizes SQLite database to assist students in organizing their academic activities. The development method used is the Agile method with three sprint cycles, ensuring incremental improvements and continuous validation of features. The application offers task management features, note-taking, reminders, and a search system to facilitate the management of academic information. SQLite was chosen as the main database due to its self-contained, serverless, zero-configuration, and transactional nature, suitable for mobile applications that require fast and reliable database access. The test results show that the StudySync application successfully meets the needs of users in organizing academic assignments and notes and improving student time management.
Artificial Intelligence Interaction in Higher Education: A Life-Course Perspective on Digital Well-Being, Learning Outcomes, Motivation, and Ethical Awareness Ikrananda; Indah Amaliah; Annajmi Rauf; Muh. Yusril Anam; Irwansyah Suwahyu
Artificial Intelligence in Lifelong and Life-Course Education Vol 1 No 1 (2026): Artificial Intelligence in Lifelong and Life-Course Education
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aillce.v1i1.2

Abstract

Purpose – The increasing integration of artificial intelligence (AI) in higher education offers significant opportunities to enhance learning effectiveness, yet it also raises concerns related to digital well-being, learner motivation, and ethical awareness. From a life-course education perspective, early adulthood represents a critical transitional phase in which patterns of interaction with AI may shape long-term learning habits and readiness for lifelong learning. However, empirical evidence examining how AI interaction influences learning outcomes through psychological and instructional mechanisms remains limited. This study examines the effects of student interaction with AI on learning outcomes, learning motivation, and ethical awareness, with digital well-being and instructional design quality positioned as mediating variables.Design/methods/approach – A quantitative cross-sectional survey was conducted with 145 undergraduate students at a public university in Indonesia. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine direct and mediating relationships among the proposed constructs.Findings – The results indicate that student interaction with AI has a significant positive effect on digital well-being, instructional design quality, learning motivation, and learning outcomes. Digital well-being and instructional design quality serve as important mediating mechanisms through which AI interaction enhances motivation and academic achievement. However, interaction with AI does not directly improve students’ ethical awareness, suggesting that ethical sensitivity does not emerge automatically through AI use without explicit pedagogical intervention.Research implications/limitations – These findings underscore the importance of designing AI-supported learning environments that promote cognitive engagement, digital well-being, and pedagogical quality while deliberately integrating ethical instruction. The study is limited by its cross-sectional design, single-institution context, and reliance on self-reported data.Originality/value – This study contributes to the literature on artificial intelligence in education by integrating digital well-being and instructional design quality as mediating mechanisms within a life-course framework, offering insights into how AI interaction during early adulthood may influence sustainable and responsible lifelong learning.
Academic Dependency, AI Literacy, and Cognitive Offloading Predict Students’ Cognitive Ability in Generative AI Learning Andini Noviyanti Fitriani; Rezky Risaldy; Annajmi Rauf; Shera Afidatunisa
Artificial Intelligence in Lifelong and Life-Course Education Vol 1 No 2 (2026): Artificial Intelligence in Lifelong and Life-Course Education
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aillce.v1i2.18

Abstract

Purpose – This study examines the cognitive effects of generative artificial intelligence use in higher education by testing whether academic dependency, AI literacy, and cognitive offloading predict students’ cognitive ability.Design/methods/approach – A quantitative cross-sectional survey was conducted with 93 undergraduate students at Universitas Negeri Makassar who actively use generative AI tools for academic purposes. Data were collected through a structured online questionnaire and analyzed using partial least squares structural equation modeling to evaluate measurement reliability and validity and to test structural relationships among academic dependency, AI literacy, cognitive offloading, and student cognitive ability.Findings – The structural model shows that academic dependency, AI literacy, and cognitive offloading positively and significantly predict student cognitive ability. AI literacy is the strongest predictor, indicating that students’ capacity to understand, evaluate, and use AI outputs critically is central to cognitive development. The findings also suggest that adaptive dependency can function as productive scaffolding, while strategic cognitive offloading may support higher-order thinking by reallocating limited cognitive resources.Research implications/limitations – The cross-sectional design limits causal inference, self-reported measures may introduce bias, and a single-institution context limits generalizability.Originality/value – This study provides integrated empirical evidence on the cognitive impact of generative AI use by jointly modeling academic dependency, AI literacy, and cognitive offloading, informing balanced AI literacy interventions and responsible AI governance in higher education.
AI Hallucinations in AI-Assisted Educational Decision-Making and Academic Honesty Intentions Among Undergraduates 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 : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v1i2.9

Abstract

Artificial Intelligence in Education (AIED) is increasingly used to improve learning efficiency, personalization, and academic productivity. however, persistent risks such as AI hallucinations, algorithmic bias, and limited transparency can undermine the reliability of AI outputs and create ethical vulnerabilities that threaten academic integrity. This study aims to examine how students’ perceptions of algorithmic bias, perceived transparency of AI systems, and digital literacy influence their intentions to behave honestly when using AI for academic purposes. A quantitative cross-sectional survey was administered to 97 undergraduate students with experience using generative AI tools, and the proposed relationships were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results indicate that algorithmic bias (β = 0.248; t = 2.420; p = 0.008), transparency (β = 0.188; t = 1.920; p < 0.001), and digital literacy (β = 0.499; t = 5.457; p = 0.027) each have positive and significant effects on honest behavior intentions, with digital literacy emerging as the strongest predictor. These findings imply that strengthening students’ digital literacy together with institutional efforts to promote transparent and fairness-aware AI use can reduce unethical practices and foster a more integrity-centered academic environment in AI-assisted learning, while also informing ethical behavior frameworks for AIED implementation in higher education.
Pelatihan SmartPLS Berbasis Online sebagai Upaya Peningkatan Kompetensi Analisis Data Mahasiswa Annajmi Rauf; Andi Dio Nurul Awalia; Rosidah; Irwansyah Suwahyu; Asham Bin Jamaluddin; M. Miftach Fakhri
Vokatek: Jurnal Pengabdian Masyarakat Volume 3: Issue 3 (Oktober 2025)
Publisher : Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Percepatan transformasi digital di perguruan tinggi menuntut kesiapan infrastruktur dan ketersediaan materi pembelajaran yang relevan untuk mendukung proses belajar berbasis teknologi. Namun, berbagai laporan menunjukkan masih adanya kesenjangan akses teknologi dan materi digital yang memadai, sehingga berdampak pada rendahnya keterlibatan dan kepercayaan diri mahasiswa dalam mengikuti pelatihan online, termasuk pelatihan analisis data menggunakan software SmartPLS. Kondisi ini menegaskan perlunya kajian empiris mengenai peran Availability of Learning Materials (ALM) dan Accessibility and Connectivity (AC) terhadap tingkat Participant Engagement (PE) dengan Confidence (C) sebagai variabel psikologis yang berpengaruh. Penelitian ini menggunakan metode pre-experimental dengan desain One-Shot Case Study (X → O), di mana 62 mahasiswa menerima perlakuan berupa pelatihan SmartPLS dan kemudian mengisi kuesioner posttest. Hasil analisis deskriptif menunjukkan bahwa ALM berada pada kategori sangat baik, AC berada pada kategori baik, C meningkat meskipun belum optimal, dan PE berada pada kategori baik. Temuan ini menegaskan bahwa kualitas materi serta akses teknologi yang memadai berperan penting dalam membangun self-efficacy mahasiswa dan meningkatkan keterlibatan peserta pada pelatihan daring. Penelitian ini memberikan implikasi praktis bagi pengembangan pelatihan teknis berbasis teknologi, terutama pentingnya menyediakan materi komprehensif, memastikan dukungan konektivitas, serta menambah sesi praktik mandiri guna meningkatkan efektivitas pelatihan di masa mendatang.
Co-Authors Amelia Uswatun Khasanah Andi Baso Kaswar Andi Dio Nurul Awalia Andini Noviyanti Fitriani Annisa Soedali Putri Arsyanda Asham Bin Jamaluddin Desitha Cahya Devi Miftahul Jannah Diva Armelia Elma Nur Jannah Elma Nurjannah Fredi Ganda Putra Ikrananda Ilma Wulansari Hasdiansa Indah Amaliah Irwansyah Suwahyu Irwansyah Suwahyu Israwati Hamsar M Miftach Fakhri M. Miftach Fakhri Muh. Rahmat Wahyudi JY Muh. Yusril Anam Nur Alamsyah Nur Fadhylah As Nur Febrianti Petrus Jacob Pattiasina Pramudya Asoka Syukur Putri Ramdani Putwal Arjuna Rezky Risaldy Rosidah Rosidah Saipul Abbas Shera Afidatunisa Siti Auliyani Saleh Sulpiani