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<p>This systematic literature review synthesizes IS research on human-AI collaboration to develop a comprehensive framework guiding future inquiry. Analyzing 137 articles from the AIS library and related outlets, we identify critical gaps and research directions. Findings reveal three key themes: redefined human–machine relationships, effective interaction design, and organizational-societal implications. Significant gaps include a paucity of longitudinal studies, absent unified evaluation frameworks, and insufficient theoretical grounding. We propose an integrated approach incorporating human-centered design, sociotechnical perspectives, and ethical considerations. We further examine the transformative role of large language models (LLMs), AI copilots, and agentic AI systems in reshaping collaboration paradigms, and provide use cases demonstrating our framework applicability. This review contributes to theory and practice by providing a foundation for understanding human-AI collaboration. Future research should focus on developing native theories, examining long-term impacts, and addressing ethical challenges in collaborative AI systems, with particular attention to the rapidly evolving generative AI landscape and its implications for IS education and practice.</p>

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Human-AI collaboration in information systems research: a systematic literature review and future research directions

  • 期刊论文

  • 发表日期：

    2026年5月18日

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  • Adiata Borresa Seini,
  • Ibrahim Osman Adam,
  • Mansah Preko

摘要

This systematic literature review synthesizes IS research on human-AI collaboration to develop a comprehensive framework guiding future inquiry. Analyzing 137 articles from the AIS library and related outlets, we identify critical gaps and research directions. Findings reveal three key themes: redefined human–machine relationships, effective interaction design, and organizational-societal implications. Significant gaps include a paucity of longitudinal studies, absent unified evaluation frameworks, and insufficient theoretical grounding. We propose an integrated approach incorporating human-centered design, sociotechnical perspectives, and ethical considerations. We further examine the transformative role of large language models (LLMs), AI copilots, and agentic AI systems in reshaping collaboration paradigms, and provide use cases demonstrating our framework applicability. This review contributes to theory and practice by providing a foundation for understanding human-AI collaboration. Future research should focus on developing native theories, examining long-term impacts, and addressing ethical challenges in collaborative AI systems, with particular attention to the rapidly evolving generative AI landscape and its implications for IS education and practice.