New publication at SoftwareX

@article{Schrottenbacher:et:al:2026:c,
  title     = {MMAnnotator: Enhancing the annotation of web documents in the
               field of educational sciences with a RAG chatbot},
  journal   = {SoftwareX},
  volume    = {36},
  pages     = {103188},
  year      = {2026},
  issn      = {2352-7110},
  doi       = {https://doi.org/10.1016/j.softx.2026.103188},
  url       = {https://www.sciencedirect.com/science/article/pii/S2352711026006783},
  author    = {Patrick Schrottenbacher and Daniel Baumartz and Giuseppe Abrami
               and Alexander Mehler and Sebastian Gombert},
  keywords  = {Multimodal annotation, Linguistic annotation, LLM-assisted annotation, Retrieval-augmented generation, Educational science, core_c08, FACES},
  abstract  = {Generative AI has led to changes in linguistic annotation. Tasks
               that previously required human annotation can now be automated.
               As LLMs become more capable, the problems they can address become
               more complex. LLM-based chatbots present new opportunities: Annotators
               can ask the bot questions and request explanations within the
               UI. This is highly relevant for multimodal annotation, given that
               only a few platforms support the annotation of different modalities.
               We introduce MMAnnotator, a MultiMedia annotation system aiming
               to fill this gap. It allows for linguistic and multimodal annotation
               with the help of a chatbot. We demonstrate MMAnnotator in the
               field of educational sciences and provide a user study for evaluation.}
}