Sep 2026· Social Sciences & Humanities Open· Vol 14, pp. 103680· 81 references
Abstract
This study examines how higher education educators perceive artificial intelligence (AI) as both a coping resource and a catalyst for career development. Grounded in the Stimulus-Organism-Response (S-O-R) framework and integrating Social Cognitive Career Theory (SCCT) with Lazarus and Folkman's Stress Theory (L&FST), the study explores how psychological and motivational factors shape career adaptability. Data were collected from 155 educators using structured surveys. Reliability and validity were established, and relationships among variables were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM), a variance-based technique used to assess complex relationships between latent constructs. Results show that self-efficacy, resilience, and social support positively influence engagement with AI, while symptom distress shapes coping responses. The findings demonstrate that AI simultaneously enables professional growth and induces adjustment challenges. This study contributes an integrated framework explaining educators' career responses to technological change.
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
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
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
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
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