Artificial Intelligence for Information System Development

Dear Colleagues,

This Special Issue focuses on investigations into the possibilities of including Generative AI in the context of Information System Development. The application of AI tools is to be discussed in the context of software system development projects. The AI application should be emphasized in the early stages of information system analysis and design, as well as in the exploration of the organizational aspects of information system development.

The Special Issue aims to reveal the latest AI-oriented techniques for information system analysis, design, and development, particularly for managerial decision-making. The Artificial Intelligence considered in this Special Issue is to cover machine learning, natural language processing, and large language models. In this Special Issue, a fundamentally new perspective is needed on conceptual modeling that integrates AI components with conceptual modeling.

The system analysis covers a range of problem-solving methods that disintegrate a system into its constituent parts to study how the individual parts function, and integration can be supported by AI to achieve the common goal and synergy. So far, the most important model-driven approaches are structured analysis, object-oriented analysis, and, lately, popular low-code/no-code development. Various tools/techniques/methods adopted by system analysts for information gathering, requirement elicitation, and evidence gathering include: reviewing reports, forms, and procedures; on-site observations; interviews; and questionnaires. The question is how they change and how they are applied in the AI context. Special attention is paid to the use in programming information systems and application development. Generative AI (GenAI) tools like ChatGPT and GitHub Copilot, with their unique content-generation capabilities, have transformative potential in Software Engineering by offering new ways to optimize software development processes.

In the Information System Development setting, AI can increase operational efficiency and improve activities within the organization. AI-based systems are transforming traditional software development practices. Artificial Intelligence (AI) evolves into a transformative technological capability capable of supporting data-driven management practices, automating analytical processes, and improving decision-making quality. However, integrating AI into performance management also introduces ethical and organizational challenges, including cognitive offloading, employee privacy concerns, and resistance to technological change.

This Special Issue is to collect empirical studies applying LLM-based test generation, prompt engineering strategies, and practical guidelines for prompt design and model selection.

Although Large Language Models (LLMs) can generate code modifications that improve quality, existing approaches treat code generation, continuous integration, and quality monitoring as isolated concerns. This Special Issue is to collect papers on AI-driven Software Development methodologies.

Large Language Models (LLMs) are being rapidly adopted across organizations, businesses, and education. Included in their applications are capabilities to generate text, code, and models. This raises questions about their potential role in the conceptual modeling phase of information systems development.

This Special Issue focuses on two fundamental keywords, i.e., artificial intelligence and information systems. However, the recommended topics include, but are not limited to, the following:

• Conceptual models;
• AI- supported modeling for information system lifecycle management;
• Practice of system modeling;
• Model-driven engineering;
• Digital ecosystem modeling;
• Enterprise architecture frameworks and models;
• LLM-based test generation;
• AI in the process from requirements to code;
• Prompt engineering strategies;
• Languages, tools, and notations for information system modeling;
• AI-driven DevOps;
• AI-driven agile methods;
• Practical guidelines for prompt design and model selection;
• AI governance and compliance for system development.

Prof. Dr. Małgorzata B Pankowska
Prof. Dr. Dominik Spinczyk
Guest Editors

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• conceptual model
• artificial intelligence
• large language models
• practice of modeling
• prompt engineering
• software engineering
• enterprise architecture
• model driven engineering