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All Journal Journal of Embedded Systems, Security and Intelligent Systems Artificial Intelligence in Educational Decision Sciences Journal of Vocational, Informatics and Computer Education Journal of Applied Artificial Intelligence in Education
Elma Nurjannah
Universitas Negeri Makassar
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.
Investigasi Persepsi Mahasiswa terhadap ChatGPT dalam Model Blended Learning pada Pembelajaran Matematika Hersiyati Palayukan; Hajar Dewantara; Elma Nurjannah; Offiler Pebrian; Sarmila; Thariq Al Ayyubi
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.v2i1.25

Abstract

Pengembangan teknologi kecerdasan buatan, terutama pemanfaatan ChatGPT, menciptakan dampak signifikan dalam dunia akademik dan pendidikan. Penelitian ini bertujuan untuk menginvestigasi persepsi mahasiswa terhdap penggunaan chatGPT pada pembelajaran matematika terutama dalam model blended learning. Penelitian berfokus pada bagaimana penggunaan alat kecerdasan buatan ini dapat mempengaruhi perolehan keterampilan berpikir kritis, pemecahan masalah, dan kerja kelompok di kalangan siswa, serta mengetahui tentang keandalan, dan pentingnya alat ini di dunia akademis. Penelitian ini menggunakan metode kuantitatif dengan pendekatan cross-sectional, dengan 91 responden pada kuesioner sebagai instrumen pengumpulan data. Hasil analisis deskriptif, termasuk presentasi statistik seperti mean, median, modus, sum, max, dan min, menunjukkan bahwa rata-rata 3,34 berpendapat sebagian mahasiswa terhadap ChatGPT sebagai alat yang andal untuk jawaban teori matematika, meskipun ada keraguan terkait kemampuannya dalam menangani perhitungan numerik yang kompleks. Sementara itu, sebagian mahasiswa lainnya, dengan rata-rata 3,68, mengekspresikan pandangan positif terhadap potensi ChatGPT sebagai alat yang penting dalam dunia akademis. Dapat disimpulkan bahwa pemanfaatan ChatGPT dalam pembelajaran matematika, terutama melalui model blended learning, memberikan dampak positif yang signifikan terhadap efektivitas pembelajaran. Rekomendasi utama fokus pada pengembangan kemampuan ChatGPT agar lebih adaptif terhadap kebutuhan pembelajaran, sambil tetap mengakui peran penting pengajar dan pengalaman langsung, sehingga menjadi pedoman terhadap kemajuan teknologi, khususnya di Indonesia.
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.
How AI Personalization and Feedback Shape Student Engagement: The Mediating Role of Technology Engagement 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 : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v1i2.8

Abstract

Higher education is rapidly adopting AI-supported learning systems, yet the effectiveness of these tools depends on how students engage with them psychologically, not merely on their availability. However, mere access to AI tools does not automatically translate into meaningful student engagement, indicating a psychological “adoption gap” between technology availability and learners’ active involvement. This study aims to test how key AI features AI usage, personalization/adaptivity, and feedback/analytics relate to student engagement, while examining technology engagement as a mediating mechanism that explains how AI features become educationally effective. Using a quantitative, non-experimental cross-sectional survey of 71 undergraduate students in Eastern Indonesia, the proposed model was analyzed using PLS-SEM (SmartPLS 4) to estimate direct and indirect effects. The model demonstrated strong predictive power, explaining 74.4% of the variance in technology engagement (R² = 0.744) and 66.4% in student engagement (R² = 0.664). AI personalization/adaptivity emerged as the strongest driver, significantly predicting technology engagement (β = 0.516, p < 0.001) and also exerting a significant direct effect on student engagement (β = 0.310, p = 0.010), whereas AI usage and feedback did not show significant direct effects on student engagement but exhibited significant indirect effects through full mediation by technology engagement. These findings imply that technology engagement functions as a “gatekeeper”: institutions should prioritize adaptive personalization and deliberately cultivate students’ sense of control, competence, and psychological involvement with AI systems, rather than relying on high usage intensity or automated feedback alone to drive engagement.
Improving Literacy Through Technology: An Agile Approach in the Development of the Redify Digital Reading Platform Muhammad Fikri Aqil; Rosidah; Muh Galang Nusantara; Siti Fatimah Azzahra Namar; Rafiqah Ameliah Kasim; Elma Nurjannah
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.v5i2.5312

Abstract

In the digital age, reading has evolved from a traditional physical book to a more dynamic and technology-integrated activity through e-books and other digital platforms. However, this progress also presents new challenges, such as a significant decline in reading interest in Indonesia, especially among the younger generation. Redify, an innovation in the digital reading ecosystem, addresses this challenge by offering a more engaging reading experience and incorporating a strong community approach. This article explores how Redify leverages the latest technologies and agile development methods to quickly adapt its features to meet readers' needs and preferences. By encouraging social interaction and discussion among readers, Redify aims to revive reading habits and improve the overall reading experience. The study also assesses the feasibility and effectiveness of Redify's strategy in overcoming the decline in reading interest among the younger generation in Indonesia.
Analysis of the Impact of Artificial Intelligence Technology on the Development of Students’ Academic Writing Skills in the Digital Learning Era Nur Hidayat; Wildan Muafan; Elma Nurjannah; Akhmad Affandi; Rosidah
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.v3i2.261

Abstract

The rapid advancement of Artificial Intelligence (AI) has transformed academic practices, particularly in supporting the development of students’ academic writing. However, empirical evidence explaining how AI utilization, automatic feedback, and personalized learning contribute to writing performance in higher education remains limited. This study examines the effects of AI utilization, AI-based automatic feedback, and AI-driven personalized learning on Students’ Academic Writing Skills (SAWS). Using an explanatory quantitative approach with a cross-sectional design, data were collected from 88 Indonesian university students through purposive sampling. Partial Least Squares–Structural Equation Modeling (PLS-SEM) was employed to evaluate the measurement and structural models. The findings show that Automatic Feedback Based on AI (AFBAI) is the strongest predictor of SAWS (β = 0.531; p = 0.000). The Utilization of AI Technology (UAIT) also has a significant positive effect (β = 0.290; p = 0.007), indicating that frequent use of AI tools contributes to improved writing skills. Conversely, Personalized Learning Based on AI (PLBAI) has no significant direct effect (β = 0.053; p = 0.350). The structural model demonstrates substantial predictive power with an R² value of 0.660. AI technologies play an essential role in enhancing academic writing performance, especially through automated feedback and consistent utilization. However, AI-driven personalized learning systems still require further optimization and deeper user engagement to meaningfully support the development of complex writing competencies.
Co-Authors Ahmad Abdullah Aswad Akhmad Affandi Amelia Uswatun Khasanah Annajmi Rauf Fredi Ganda Putra Hajar Dewantara Hersiyati Palayukan Israwati Hamsar Muh Galang Nusantara Muh. Akbar Muhammad Fikri Aqil Nur Febrianti Nur Hidayat Offiler Pebrian Rafiqah Ameliah Kasim Rosidah Rosidah Saipul Abbas Sarmila Siti Fatimah Azzahra Namar Tegar Angbirah Parerungan Thariq Al Ayyubi Wildan Muafan