@misc{13165,
  abstract     = {{It will be examined, on the basis of three guiding questions (LF1–LF3), whether and to what extent storytelling components (storytelling principles, elements, techniques) are related to the quality and success levels of selected true crime-podcast formats. The three selected formats are each represented by two individual episodes. Parallels and differences in the composition of narrative devices across the three true crime-podcast formats will be identified. The individual episodes are analyzed in tabular form, using the pre-structured analytical scheme that lists the storytelling components in three subcategories. The theoretical discussion is conducted by means of a literature review of the current state of research and its influence on answering the guiding questions. Before outlining the analytical framework, the study introduces podcasts as an audio format, the true-crime podcast genre, and storytelling and its significance for true-crime podcasts. The aim of this bachelor’s thesis is to determine the extent to which storytelling components in different true crime-podcast formats correlate with the format’s success, operationalized via quality criteria, so that the findings can be used to evaluate existing formats and/or be incorporated into further studies. It is expected that the success and quality of different true crime narrative formats in podcast form are closely tied to a coherent blend of storytelling principles, elements, and techniques, and that this relationship can be captured through the intended analysis. The results obtained may then serve as a starting point for further research questions and studies. 
Keywords: Audio podcast format, True Crime Genre, Storytelling components}},
  author       = {{Bussmann, Lale}},
  keywords     = {{Audio-Podcastformat, True Crime-Genre, Storytellingkomponenten, Storytelling}},
  pages        = {{176}},
  publisher    = {{Technische Hochschule Ostwestfalen Lippe}},
  title        = {{{Storytelling in True Crime-Podcasts}}},
  year         = {{2025}},
}

@misc{12048,
  abstract     = {{Interactive stories can be an effective approach for teaching purposes. One shortcoming is the effort necessary to author and create these stories, especially complex storylines with choices for the readers. Based on recent advances in Natural Language Processing (NLP), new opportunities arise for assistance systems in the context of interactive stories. In our work, we present an authoring approach and prototypical tool for the creation of visual comic-strip like interactive stories, a type of hypercomics, that integrate an Artificial Intelligence (AI) assistance. Such comics are already used in our Gekonnt hanDeln web platform. The AI assistance provides suggestions for the overall story outline as well as how to design and write individual story frames. We provide a detailed description about the approach and its prototypical implementation. Furthermore, we present a study evaluating the prototype with student groups and how the prototype evolved in an iterative style based on the students’ feedback.}},
  author       = {{Grimm, Valentin and Rubart, Jessica}},
  booktitle    = {{HT '24: Proceedings of the 35th ACM Conference on Hypertext and Social Media}},
  isbn         = {{979-8-4007-0595-3 }},
  keywords     = {{Storytelling, Authoring, GPT, Hypercomics, Large Language Models}},
  location     = {{Poznan, Poland}},
  pages        = {{88--97}},
  publisher    = {{Association for Computing Machinery (ACM)}},
  title        = {{{Authoring Educational Hypercomics assisted by Large Language Models}}},
  doi          = {{10.1145/3648188.3675124}},
  year         = {{2024}},
}

@misc{12383,
  abstract     = {{Data stories are about revealing and communicating insights from complex data. In this paper, we propose conversational data stories, which support end users in understanding the key findings of the data analysis at hand by natural language conversation. Creating these stories manually means to put a lot of effort into understanding the data and crafting visuals. With increasingly powerful generative large language models (LLMs), natural language processing as well as automating the creation of data stories is a promising field. We present a concept for a conversational data storytelling system that integrates LLMs as well as explainable AI. We present the collected requirements for our system concept and how the requirements are addressed. To show the potential of our approach, we provide a use case scenario and a discussion in this paper. This is supposed to serve as a basis for future research that will aim at investigating the technical reliability and the user experience of such a system.}},
  author       = {{Grimm, Valentin and Rubart, Jessica and Söhlke, Patrick}},
  booktitle    = {{Proceedings of the 7th Workshop on Human Factors in Hypertext (HUMAN'24)}},
  editor       = {{Atzenbeck, Claus and Rubart, Jessica}},
  isbn         = {{979-8-4007-1120-6}},
  keywords     = {{Data Storytelling, Conversational Assistant, Conversational Data Storytelling, Explainable AI}},
  location     = {{Poznan Poland}},
  pages        = {{6}},
  publisher    = {{ACM}},
  title        = {{{Conversational Data Stories}}},
  doi          = {{10.1145/3679058.3688631}},
  year         = {{2024}},
}

@misc{1719,
  author       = {{Lohfink, Lena-Carolin}},
  keywords     = {{Animation, Concept Art, Film, visuelle Konzeption visual storytelling}},
  pages        = {{46}},
  publisher    = {{Hochschule Ostwestfalen-Lippe}},
  title        = {{{Concept Art - Die Bedeutung visueller Konzeption in Realfilm- und Animationsfilmproduktionen}}},
  year         = {{2015}},
}