Sep 2026· Journal of Communication, Sustainability, and Empowerment· 0 citations· 10 references
Abstract
Adolescence is a sensitive developmental period marked by heightened neurobiological plasticity, identity formation, and socio-emotional learning. As generative artificial intelligence becomes embedded in adolescents’ daily lives, existing frameworks for digital harm, built around screen time and social media exposure, may not capture its distinct psychological affordances. This study aims to reframe adolescent AI overuse as a qualitatively distinct developmental and relational exposure, rather than an extension of screen time. Using a narrative synthesis of developmental science, relationship science, and documented public cases published between 2018 and 2025, the study examines how AI reliance intersects with five developmental domains: emotional regulation, social learning, identity formation, autonomy, and attachment. The synthesis identifies recurring conceptual risks externalized coping, recalibrated relational expectations, externally validated identity, deferred autonomy, and displaced attachment while noting that direct empirical evidence in adolescent samples remains sparse. The study proposes a working definition of adolescent AI overuse and an interim clinical, educational, and policy framework, offering a conceptual foundation for the empirical research this area still lacks.
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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MIT News · Artificial Intelligence· news.mit.eduOct 1, 2026
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.
Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.
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