The paper "Effects of Explanations in Human-AI Interaction: A Systematic Review and Framework for Future Research on Explainable AI" by Philipp Reinhard, Mahei Li, Christoph Peters and Jan Marco Leimeister has been published in the journal "Information Systems Frontiers".

The journal "Information Systems Frontiers" focuses on scientific articles that deal with the interface between information systems and information technology from an analytical, behavioral science and technological perspective.

The study is dedicated to the question of how explanations work in the interaction between humans and artificial intelligence and what consequences they have for the perception, trust and use of AI systems. Transparency and traceability are becoming increasingly important, particularly due to current developments in the field of generative AI and agentic AI. At the same time, there has so far been a lack of consistent understanding of how explanations actually affect users and what real consequences arise from them.

To address this research gap, the researchers conducted a systematic literature review of 107 experimental user studies in the field of Explainable AI (XAI). The aim was to structure existing findings on human-AI interaction and systematically analyze the interdependencies of explanations.

Based on the Stimulus-Organism-Response-Consequences (S-O-R-C) model, the authors developed a conceptual framework that describes how different forms of explanations influence user perceptions and which behaviors and downstream effects result from them. The study shows that research on XAI has so far often been fragmented and central mechanisms of action have not been considered in an integrated manner.

In addition, the study identifies five central research directions for future investigations. In particular, these address the challenges of new AI systems such as large language models and AI agents as well as the increasing delegation of decisions and tasks to AI systems.

With their framework, the researchers are making an important contribution to the further development of Explainable AI research and creating a basis for designing human-centered and transparent AI systems in a more targeted way in the future.

The full article is available at the following link: https://link.springer.com/article/10.1007/s10796-026-10716-4

Reinhard, P., Li, M.M., Peters, C. & Leimeister, J. M. (2026). Effects of Explanations in Human-AI Interaction: A Systematic Review and Framework for Future Research on Explainable AI. Inf Syst Front.doi.org/10.1007/s10796-026-10716-4