@article{,
    author = {Schäffer, Burkhard; Lieder, Fabio},
    title = {Distributed interpretation  : Teaching reconstructive methods in the social sciences supported by artificial intelligence},
    editor = {},
    booktitle = {},
    series = {},
    journal = {Journal of Research on Technology in Education},
    address = {},
    publisher = {},
    edition = {},
    year = {2023},
    isbn = {},
    volume = {55},
    number =  {1},
    pages = {111-124},
    url = {https://www.tandfonline.com/doi/full/10.1080/15391523.2022.2148786},
    doi = {10.1080/15391523.2022.2148786},
    keywords = {GPT ; documentary method ; promp engineering ; groupdiscussion ; method skills ; method knowledge},
    abstract = {This article highlights teaching and learning in reconstructive research supported by artificial intelligence (AI) and machine interpretation in particular. The focus is whether the traditional teaching of methodological competence through research workshops can be supplemented with artificial intelligence (natural language processing, NLP) implemented in computer-assisted qualitative data analysis software (CAQDAS). A case study shows that AI models can be trained to interpret texts. Thus, distributed interpretation by humans and AI becomes possible, opening up new possibilities for teaching qualitative methods. How people deal with these new possibilities is presented based on an explorative evaluation of a group discussion with young researchers. Finally, this contribution discusses the possibilities and limits of this new form of interpretation together with a machine.},
    note = {},
    institution = {Universität der Bundeswehr München, Fakultät für Humanwissenschaften, HUM 2 - Institut für Bildungswissenschaft, Professur: Schäffer, Burkhard},
    
    
}