Sep 2026· Linguanusa : Social Humanities, Education and Linguistic· 0 citations
TL;DR
The findings show that AI offers transformative opportunities in facilitating personalized learning, real-time feedback, the automation of formative assessment, and efficiency in designing educational content, and faces substantial ethical and pedagogical challenges.
Abstract
Introduction to the Problem: The rapid development of artificial intelligence, and of generative AI and intelligent tutoring systems in particular, has triggered a revolution of paradigm in the landscape of modern education. AI is no longer merely an instrument of administrative automation but has been transformed into a cognitive partner reconfiguring the pedagogical interaction between educator and learner. Purpose: This article aims to analyse comprehensively the spectrum of opportunities and challenges in the use of AI in the learning process in the digital era. Design/methods/approach: Using a descriptive-analytical qualitative method based on critical library research, the study explores the most recent literature on artificial intelligence in education and on frameworks for the regulation of technological ethics. Findings: The findings show that AI offers transformative opportunities in facilitating personalized learning, real-time feedback, the automation of formative assessment, and efficiency in designing educational content. Its adoption nonetheless faces substantial ethical and pedagogical challenges, including the degradation of learners’ critical reasoning through excessive dependence, threats to academic integrity, the problem of algorithmic bias, and the protection of students’ personal data. Originality/value: The study recommends the formulation of an integrated framework of AI literacy and of a human-in-the-loop pedagogy of human–AI collaboration for educators and for educational developers, so as to ensure that the integration of AI remains grounded in the values of humanism, justice, and intellectual emancipation.
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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