The novel AI–Research Output (AI-RO) Model is developed and tested, which integrates the Technology Acceptance Model (TAM) and Socio-Technical Systems Theory to explain both the direct and conditional relationships between AI use and research output.
It is argued that while GenAI acts as a digital support tool, it also risks cognitive passivity that alters students' self-competence perceptions, and higher education institutions should adapt assessment paradigms and implement structured metacognitive training.
Quoc Hoang, Minh Nam Anh Nguyen· Technium Social Sciences Jou...· 0 citations
Theoretically, the current research offers a new lens through which accelerated technology adoption can be analysed and guides tailored digital sustainability interventions in Confucian models of education.
Jingyi Li, Lianyun Huang, F. Furuoka· Frontiers in Psychology· 0 citations
The results collectively suggest is that enthusiasm alone cannot sustain instructional quality in the AI era; it is technical mastery in AI Competence that converts a teacher’s positive outlook into measurable performance gains.
Muhammad Ridzik Varedho, H. Harjono, Erisa Kurniati· FINGER : Jurnal Ilmiah Tekno...· 0 citations
This qualitative study investigates researchers' perceptions of ChatGPT's influence on research creativity, efficiency, and scholarly integrity. A cross-sectional survey with six open-ended questions was administered to 112 participants across academic levels (2 post-doctoral, 20 PhD, 26 Master's, 64 diploma holders) within education and psychology. Data was analyzed using triangulated methods: SWOT analysis, thematic analysis, and systematic open coding. Findings indicate ChatGPT is perceived as a dual-edged tool that enhances productivity while posing cognitive and ethical risks. Ethical considerations are central, with participants emphasizing the need for structured guidelines. The human factor remains decisive—AI's benefits depend on researchers' methodological awareness and ethical engagement. Results suggest AI functions optimally as a cognitive scaffold, with its impact contingent upon use patterns and user experience. The findings carry implications for developing evidence-based policies and training for responsible AI integration in academic research.
M. Moussa, Medhat Mohamed Saleh, O. Al-Adamat· Journal of Digital Education...· 0 citations
With the advancement of artificial intelligence (AI) technology, AI tools like ChatGPT have gradually become a part of the life of students in universities. Previous research has mainly addressed the functional aspects of AI, such as information retrieval and academic support, but the socio-emotional and personalized aspects of AI, and how these shape users’ continued usage behaviour. Therefore, this study aims to examine the effects of academic support, emotional support, and perceived personalisation on students’ continuance intention toward ChatGPT, with perceived trust acting as a mediating mechanism. This research grounded by the Expectation-Confirmation Theory and Information Systems Continuance Model by incorporating two additional variables: perceived personalisation and academic support and emotional support as antecedents of students’ continuance intention toward ChatGPT. This quantitative study used an online survey to collect data from 263 university students. The proposed model was analysed using SmartPLS-SEM to examine both direct and indirect relationships, with perceived trust specified as a mediating variable. The structural model indicates that academic support and emotional support have significant direct effects on continuance intention. Although perceived personalisation positively influences user trust, its direct effect on continuance intention is not significant. Instead, perceived trust fully mediates this relationship, indicating that personalisation alone does not directly drive continued use unless it first builds user trust. These findings highlight the critical role of trust as a psychological mechanism through which AI personalisation translates into sustained user engagement. The study contributes to the literature on human–AI interaction by integrating cognitive, emotional, and personalisation dimensions within a unified continuance framework. Practically, the results suggest that AI developers should prioritise trust-building mechanisms when designing personalised and emotionally supportive systems to ensure long-term user adoption, particularly in educational contexts.
P. Muthurajan, Hashima Mohaini Mohammad, Nur Afni Halil et al.· International Journal of Mod...· 0 citations
Examining a four-process GenAI cycle reveals two distinct metacognitive regulation styles: Exploratory-Simplification and Systematic-Methodical, which show that students use combinations of strategies across the AI-SRL cycle.
Maria A. Perifanou, Anastasios A. Economides· Journal of educational compu...· 0 citations