Sep 2026· Frontiers in Education· 0 citations· 50 references
Abstract
Generative artificial intelligence (GenAI) is reshaping higher education by offering new forms of learning support while also raising concerns about over-reliance, reduced verification, and the weakening of independent cognitive engagement. Drawing on cognitive load theory, cognitive offloading, habit formation, and the augmentation–automation distinction, this study examines the associations between GenAI use, GenAI dependence, self-reported critical-thinking disposition, and self-perceived employability among undergraduate business students in Oman. Cross-sectional survey data from 412 students were analysed using partial least squares structural equation modelling, with covariance-based SEM used as a robustness check. The findings show that GenAI use is positively associated with GenAI dependence and self-perceived employability. Its positive association with self-reported critical-thinking disposition emerges in the adjusted structural model after accounting for GenAI dependence and the interaction between GenAI use and GenAI dependence, rather than as a strong zero-order relationship. GenAI dependence is negatively associated with both student outcomes and reduces the strength of the positive associations between GenAI use and both outcomes. The results suggest that GenAI use may support students' perceived learning and career readiness when it functions as a reflective augmentation tool involving verification, comparison, and independent judgement, while higher dependence is associated with weaker perceived learning and employability-related outcomes.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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