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How can generative AI (GenAI) be integrated in educational practices? This paper describes an approach combining problem-based learning with the creation of Interactive Digital Narratives (IDNs) using GenAI, applied during a training school for which the concrete task was to represent Malta’s complex Neolithic history. After introducing the topic, the concept of IDN, and GenAI tools, the educational journey continued by interweaving contextual information such as site visits with iterative design steps facilitating trainees to create their own IDN projects. This approach was applied during a five-day course, resulting in four IDNs incorporating the specific perspectives of the respective teams. The paper describes the pedagogical approach as well as the different ways the trainee teams found to address the limitations and challenges of current GenAI tools.

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Neolithic Experiences: Approaching a Complex Topic Through IDNs Using GenAI

  • 会议论文

  • 发表日期：

    2026年

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  • Hartmut Koenitz,
  • Jonathan Barbara,
  • Mirjam Palosaari Eladhari,
  • Lissa Holloway Attaway,
  • Pakezea Anwar,
  • Zlatan Filipovic,
  • Pouya Jahanshahi,
  • Maria Meli,
  • Sebastian R. Richter,
  • Juan David Rodas,
  • Digdem Sezen,
  • Anjuman Shaheen

摘要

How can generative AI (GenAI) be integrated in educational practices? This paper describes an approach combining problem-based learning with the creation of Interactive Digital Narratives (IDNs) using GenAI, applied during a training school for which the concrete task was to represent Malta’s complex Neolithic history. After introducing the topic, the concept of IDN, and GenAI tools, the educational journey continued by interweaving contextual information such as site visits with iterative design steps facilitating trainees to create their own IDN projects. This approach was applied during a five-day course, resulting in four IDNs incorporating the specific perspectives of the respective teams. The paper describes the pedagogical approach as well as the different ways the trainee teams found to address the limitations and challenges of current GenAI tools.