Sep 2026· EDUTECH Jurnal Inovasi Pendidikan Berbantuan Teknologi· Vol 6, pp. 3114-3124
Abstract
The development of Generative Artificial Intelligence (GenAI) in educational environments has provided students with convenience in accessing information, completing academic tasks, and supporting learning activities. However, excessive use of GenAI may lead to dependency that potentially reduces students’ cognitive engagement during the learning process. This study aims to analyze the effect of GenAI dependency on students’ academic achievement by considering self-efficacy as a mediating variable and AI literacy as a moderating variable. This research employed a quantitative approach using a survey method involving 200 undergraduate students at Universitas Sebelas Maret. Data were collected through a questionnaire using a 5-point Likert scale. The data were analyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM) with SmartPLS software. The results indicate that GenAI dependency has a negative effect on students’ academic achievement. Furthermore, self-efficacy plays a significant mediating role in explaining the relationship between GenAI dependency and academic achievement. AI literacy also moderates the relationship between GenAI dependency and self-efficacy as well as the relationship between GenAI dependency and academic achievement. These findings highlight that AI literacy is an important factor in enabling students to utilize GenAI effectively, allowing this technology to support learning processes without creating excessive dependency.
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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