Sep 2026· Journal of Workplace Learning· 58 references
Emotional Intelligence and Performance
Abstract
Purpose Team leaders in high-pressure contact centres must balance operational demands with people management, requiring high emotional intelligence (EQ). While traditional coaching can support EQ development, it is often costly and inaccessible. Generative artificial intelligence (AI)-enabled coaching offers a potential alternative, yet its role and underlying mechanisms in fostering EQ remain underexplored. This study aims to examine which EQ competencies participants became aware of through AI coaching and which coaching competencies the AI used to support this awareness. Design/methodology/approach Ten team leaders used an AI coaching chatbot over four weeks to work on EQ-related development goals through reflective coaching conversations and weekly check-ins. Semi-structured interviews explored participants’ experiences and perceived EQ-related learning. Deductive, summative content analysis was applied using established EQ and coaching competency frameworks. Findings Participants reported increased awareness of eight EQ competencies, most notably emotional self-awareness, emotional self-control and achievement orientation, alongside social competencies such as empathy, influence and developing others. Analysis identified 169 instances of EQ awareness and 267 instances of International Coaching Federation (ICF)-aligned coaching competencies. The competencies that evokes awareness and facilitates client growth accounted for three-quarters of all coded coaching behaviours, suggesting that reflective questioning, insight generation and goal-focused action were key mechanisms through which AI coaching promoted EQ awareness. Participants also described the AI coach as a psychologically safe space for reflection and self-disclosure. Originality/value This study provides early empirical evidence that AI coaches can emulate core coaching competencies and facilitate EQ awareness through reflective, goal-directed conversations in demanding workplace environments.
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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