Objective: To establish a standardized methodological protocol for systematically mapping and characterizing the available scientific evidence on changes in cognitive abilities, pedagogical barriers, and guidelines for the use of artificial intelligence (AI) in formal education. Introduction: The emergence of generative AI models creates a dialectical tension between cognitive offloading and critical thinking, confronting teachers with a profound crisis in authentic assessment and curricular mismatches. Inclusion criteria: Studies in formal education (Population: higher education and secondary education) focused on AI (Concept) that address cognitive skills, pedagogical barriers, or usage guidelines (disjunctive logic), in face-to-face, hybrid, or virtual settings (Context). Time period: 2021–2026; Spanish and English. Methods: The review will be conducted in accordance with the JBI 2024 Manual for systematic reviews and will be reported using the PRISMA-ScR extension. Scopus, Web of Science, SciELO, ERIC, IEEE Xplore, and Google Scholar will be consulted. The process will include a two-phase screening (title/abstract and full text) and data extraction using a standardized form with 12 variables. Keywords: Artificial intelligence; higher education; cognitive skills; teacher barriers; ethical guidelines; scoping review.
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
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026