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<p>Conceptual maps are valuable tools for measuring the complexities and understanding the interconnectivity of concepts in various scientific fields. This systematic literature review examined the usage and limitations of conceptual maps in complexity science, using 42 peer-reviewed papers. The review identified two main applications: (1) in research to visualise and analyse complex systems, particularly in social sciences, natural sciences, and art education; and (2) in education and knowledge transfer to enhance the teaching and learning of complex concepts, especially in fields such as education, business, and health sciences. The results also show that the effective use of conceptual maps offers a pathway for (non) scientists, students, and other users to visualise, interpret, organise, and co-produce knowledge to enhance the understanding of complex systems. However, limitations were also identified, such as the time-consuming nature of map creation, potential bias, difficulties in keeping pace with rapid scientific developments, and challenges in application to certain methodologies. The review recommends integrating conceptual maps (CMs) with complementary tools, such as artificial intelligence (AI)-driven modelling, dynamic mapping techniques, and adaptive learning platforms, to automate updates, keep pace with scientific advancements, and improve adaptability for interdisciplinary applications. Furthermore, structured training programs can enhance objectivity and improve the reliability and effectiveness of CMs across different disciplines to maximise their potential for evaluating complexity.</p>

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Conceptual Map as a Tool for Evaluation in Complexity Science: Usage and Limitations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月6日

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  • Taiwo Temitope Bamgboye,
  • Tamara Avellán

摘要

Conceptual maps are valuable tools for measuring the complexities and understanding the interconnectivity of concepts in various scientific fields. This systematic literature review examined the usage and limitations of conceptual maps in complexity science, using 42 peer-reviewed papers. The review identified two main applications: (1) in research to visualise and analyse complex systems, particularly in social sciences, natural sciences, and art education; and (2) in education and knowledge transfer to enhance the teaching and learning of complex concepts, especially in fields such as education, business, and health sciences. The results also show that the effective use of conceptual maps offers a pathway for (non) scientists, students, and other users to visualise, interpret, organise, and co-produce knowledge to enhance the understanding of complex systems. However, limitations were also identified, such as the time-consuming nature of map creation, potential bias, difficulties in keeping pace with rapid scientific developments, and challenges in application to certain methodologies. The review recommends integrating conceptual maps (CMs) with complementary tools, such as artificial intelligence (AI)-driven modelling, dynamic mapping techniques, and adaptive learning platforms, to automate updates, keep pace with scientific advancements, and improve adaptability for interdisciplinary applications. Furthermore, structured training programs can enhance objectivity and improve the reliability and effectiveness of CMs across different disciplines to maximise their potential for evaluating complexity.