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All Journal Artificial Intelligence in Lifelong and Life-Course Education Artificial Intelligence in Educational Decision Sciences
Shera Afidatunisa
Universitas Pendidikan Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Artificial Intelligence Use and Emotional Well-Being in Higher Education: A Life-Course Perspective on Technology Acceptance and Trust Nailha Dinda Aprilia; Kartika Ratna Sari; Putri Nirmala; Rosidah; Shera Afidatunisa
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.v1i1.5

Abstract

Purpose – The growing integration of artificial intelligence (AI) in higher education has reshaped students’ cognitive and emotional learning experiences. From a life-course education perspective, higher education represents a critical phase of early adulthood in which interactions with AI may influence emotional regulation and readiness for lifelong learning. However, empirical studies examining the affective consequences of AI use through technology acceptance and trust mechanisms remain limited. This study investigates how AI usage frequency, perceived usefulness, perceived ease of use, and trust in AI influence university students’ emotional well-being.Design/methods/approach – A quantitative cross-sectional survey was administered to university students who actively used AI to support their learning activities. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the direct effects of technology acceptance factors and trust in AI on emotional well-being.Findings – The results indicate that AI usage frequency and trust in AI have significant positive effects on students’ emotional well-being. In contrast, perceived usefulness and perceived ease of use do not directly influence emotional well-being. These findings suggest that affective benefits of AI-supported learning are shaped more by familiarity and psychological trust than by technical efficiency alone.Research implications/limitations – The cross-sectional design, reliance on self-reported measures, and single-institution sample limit causal interpretation and generalizability. Future studies are encouraged to adopt longitudinal or mixed-method approaches to capture emotional dynamics across educational stages.Originality/value – This study extends the Technology Acceptance Model by positioning emotional well-being as a key outcome within a life-course framework, offering insights into how AI interaction during early adulthood may support psychological sustainability and lifelong learning readiness
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.
Cloud-Based Learning Analytics Platform for English Learning: Developing the Grammarlyze Mobile Application Using Firebase Realtime Database Nur Annafiah; Fatur Rahman; Della Fadhilatunisa; Shera Afidatunisa
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.v1i1.12

Abstract

Purpose – This study was conducted to address the growing need for a flexible and interactive English language learning platform by leveraging cloud computing technology. Specifically, it aims to develop an Android-based English learning application, Grammarlyze, and examine how Firebase can be effectively utilized to manage and store learning materials in real time, thereby improving accessibility and user experience compared to conventional learning media. Methods – The study employed the Waterfall development method, consisting of requirement analysis, system design, implementation, testing, and maintenance. The application was developed using Android Studio with Java, while Firebase Realtime Database and Firebase Storage served as the cloud backend for managing text and video learning materials. System testing was conducted using black-box testing to evaluate feature functionality.Findings – The results show that all core features of the Grammarlyze application functioned as expected. Black-box testing confirmed that 100% of the tested features, including material access, navigation, and data synchronization, were valid. Firebase enabled real-time data management, efficient storage, and seamless retrieval of learning content, contributing to a stable and responsive learning application. Research limitations – This study is limited to basic English learning materials and does not yet include automated evaluation features such as quizzes or adaptive feedback. The findings also limited to functional testing and do not measure learning outcomes quantitatively. Originality – This research provides practical evidence of Firebase implementation as a cloud platform for English learning applications, offering a scalable and efficient model that can extended in future studies to include advanced learning analytics and assessment features.
Co-Authors Andini Noviyanti Fitriani Annajmi Rauf Della Fadhilatunisa Fatur Rahman Kartika Ratna Sari Nailha Dinda Aprilia Nur Annafiah Putri Nirmala Rezky Risaldy Rosidah Rosidah