Electronic Participation (eParticipation) enables citizens to engage in political and decision-making processes using information and communication technologies. As in many other fields, Artificial Intelligence (AI) has recently started to dictate some of the realities of eParticipation. As a result, an increasing number of studies are investigating the use of AI in eParticipation. The aim of this paper is to map current research on the use of AI in eParticipation. Following PRISMA methodology, the authors identified 235 relevant papers in Web of Science and Scopus and selected 46 studies for review. For analysis purposes, an analysis framework was constructed that combined eParticipation elements (namely actors, activities, effects, contextual factors, and evaluation) with AI elements (namely areas, algorithms, and algorithm evaluation). The results suggest that certain eParticipation actors and activities, as well as AI areas and algorithms, have attracted significant attention from researchers. However, many more remain largely unexplored. The findings can be of value to both academics looking for unexplored research fields and practitioners looking for empirical evidence on what works and what does not.

Modeling of Public Services (PSs) is used by a large number of public authorities worldwide to improve their quality. The resulting diagrams are useful in the case of simple PSs however they can become very complex in the case of complex PSs. To address process complexity, the integration of Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) has been proposed. DMN is applied to model the decision logic of a process and has proven to provide promising results in the private sector. However, the integration of BPMN and DMN has not been studied in the case of PSs. The aim of this paper is twofold. First, to investigate the integration of BPMN and DMN when applied to complex PSs. Second, to identify a set of steps and principles for integrating BPMN and DMN to model complex PSs. For this purpose, a literature review is conducted leading to the result that there is limited research on the use of BPMN to model complex PS while research on the use of DMN is missing. In addition, a PS is selected and modeled based on selected criteria and finally, a set of steps and principles is proposed.

The provision of high-quality Public Services (PSs) constitutes a core activity of the public sector. Consequently, PS modeling has received considerable attention. The Business Process Model and Notation (BPMN) is a standard often employed in PS modeling. BPMN diagrams are clear and understandable in the case of simple PSs. However, this is not true when modeling complex PSs i.e., those including a large number of versions. Different versions exist in a complex PS since, for example, different groups of citizens may have to submit different supporting documents with their application based on their financial or civil status. In those cases, the relevant BPMN diagrams often have numerous gateways thus becoming very complex, which hinders their applicability and usefulness. In the last few years, the Decision Model and Notation (DMN) has been introduced and its integration with BPMN is used for modeling complicated business processes in the private sector. However, its effectiveness has not been investigated in the case of complex PSs. The aim of this research is to investigate the benefits and challenges of modeling complex PSs using BPMN and DMN. For this purpose, the Greek PS “Getting a Transportation Card for Disabled” is analyzed and BPMN is used to model the PS in two different ways, one with and one without the use of DMN. The results suggest that DMN models provide useful insights into the different PS versions while BPMN diagrams become simpler and more understandable. On the other hand, the public sector needs to accommodate yet another modeling notation which increases the required human capital needed for PS modeling.

The rise of Artificial Intelligence (AI)—especially Generative AI—has transformed software engineering (SE) practices. Yet, SE remains fundamentally human-centered, requiring attention to behavioral, cognitive, and social factors throughout the development life cycle. Despite this, most industrial and academic efforts towards AI integration emphasize technical aspects, hindering practical success. This Ph.D. project aims to develop a scientific understanding of how AI tools, such as Large Language Models, can be integrated into the SE life cycle without compromising software quality or practitioners’ well-being. It investigates the enablers and barriers of AI adoption and their effects on workflow adaptation, particularly within the emerging paradigm of Human-AI teaming. The results will guide practitioners in adopting AI ethically and sustainably, fostering both productivity and well-being.