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<p>Advancements in artificial intelligence (AI), particularly in generative AI and agentic AI, have intensified challenges related to transparency and explainability. While explainable AI (XAI) research has evolved to facilitate human interaction with such complex, black-box systems, research and practice lack clarity on the causal chain linking explanations, user perceptions, and real-world outcomes, a relationship that remains conceptually fragmented. To address this gap, we conducted a systematic literature review of 107 experimental user studies on XAI. We developed a conceptual framework guided by the stimulus-organism-response-consequences (S-O-R-C) model to systematize current human-XAI research and examine how users respond to explanations. Our study contributes to the literature by clarifying how explanations shape user interactions and downstream effects in real-world settings. We propose five research directions to help navigate the challenges of emerging AI systems (e.g., LLMs, AI agents) and evolving human-AI delegation.</p>

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Effects of Explanations in Human-AI Interaction: A Systematic Review and Framework for Future Research on Explainable AI

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月5日

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  • Philipp Reinhard,
  • Mahei Manhai Li,
  • Christoph Peters,
  • Jan Marco Leimeister

摘要

Advancements in artificial intelligence (AI), particularly in generative AI and agentic AI, have intensified challenges related to transparency and explainability. While explainable AI (XAI) research has evolved to facilitate human interaction with such complex, black-box systems, research and practice lack clarity on the causal chain linking explanations, user perceptions, and real-world outcomes, a relationship that remains conceptually fragmented. To address this gap, we conducted a systematic literature review of 107 experimental user studies on XAI. We developed a conceptual framework guided by the stimulus-organism-response-consequences (S-O-R-C) model to systematize current human-XAI research and examine how users respond to explanations. Our study contributes to the literature by clarifying how explanations shape user interactions and downstream effects in real-world settings. We propose five research directions to help navigate the challenges of emerging AI systems (e.g., LLMs, AI agents) and evolving human-AI delegation.