@misc{11330,
  abstract     = {{With the increasing complexity in manual assembly and a demographic decline in skilled workforce, the importance of well-documented processes through assembly instructions has grown. Creating these instructions is a time-consuming and knowledge-intensive task that typically relies on experienced employees. Although various automation solutions have been proposed to assist in generating assembly instructions, they often fall short in providing detailed textual guidance. With the rise of generative artificial intelligence (AI), new potentials arise in this domain. Therefore, this paper explores these potentials by employing various large language models (LLMs), prompting techniques and input data in an experimental setup for generating detailed assembly instructions, including the planning of assembly sequences as well as textual guidance on tools, assembly activities, and quality assurance measures. The findings reveal promising opportunities in leveraging LLMs but also substantial challenges, particularly in assembly sequence planning. To improve the reliability of generating assembly instructions, we propose a multi-agent concept that decomposes the complex task into simpler subtasks, each managed by specialized agents.}},
  author       = {{Meyer, Frederic and Freitag, Lennart and Hinrichsen, Sven and Niggemann, Oliver}},
  booktitle    = {{2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  isbn         = {{979-8-3503-6123-0}},
  keywords     = {{assembly instruction, GPT, large language model, LLM, prompt}},
  location     = {{Padova, Italy}},
  publisher    = {{IEEE}},
  title        = {{{Potentials of Large Language Models for Generating Assembly Instructions}}},
  doi          = {{https://doi.org/10.1109/ETFA61755.2024.10710806}},
  volume       = {{78}},
  year         = {{2024}},
}

@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}},
}