Employee AI awareness (AIA) has emerged as a critical yet conceptually diffuse construct capturing how employees cognitively and affectively interpret the implications of artificial intelligence (AI) for their work. Addressing calls for psychologically grounded research on AI in organizational contexts, this study systematically reviews and synthesizes fragmented evidence to strengthen the conceptual foundations of employee AIA and develop a coherent, process-oriented understanding of the construct. A systematic literature review was conducted using a five-step identification and screening protocol, resulting in 147 peer-reviewed studies. These studies were analyzed using the Theory-Context-Characteristics-Methodology (TCCM) framework to examine theoretical foundations, contextual patterns, construct characteristics, and methodological trends. The findings clarify the conceptual boundaries of employee AIA, distinguish it from related constructs, and reconceptualize AIA as a dynamic process through which employees interpret and adapt to AI-related organizational change. Specifically, employees interpret AI-related cues through interconnected cognitive, affective, motivational, behavioural, and relational mechanisms, resulting in divergent adaptation trajectories contingent on the availability of individual (e.g., self-efficacy and AI-related capabilities) and organizational resources (e.g., leadership support, training, and psychological safety). Building on this synthesis, the review develops a process-oriented conceptual framework that explains how employee responses to AI emerge and evolve across organizational contexts, while a structured TCCM-based research agenda identifies priorities for future theoretical and empirical advancement. The review advances employee AIA scholarship by providing conceptual clarification and a process-oriented reinterpretation of fragmented evidence, offering a stronger foundation for future research and evidence-based managerial interventions that support psychologically sustainable AI adoption.
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
In Compton Lecture, the distinguished legal scholar warns of potential “loss of the Enlightenment” while suggesting ways innovations can meet our civic and cultural needs.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
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