%0 Conference Paper %1 wambsganss2021arguebot %A Wambsganss, Thiemo %A Guggisberg, Sebastian %A Söllner, Matthias %B 16th International Conference on Wirtschaftsinformatik (WI) %D 2021 %K itegpub pub_msö pub_wise-kassel %T ArgueBot: A Conversational Agent for Adaptive Argumentation Feedback %X By combining recent advances in Natural Language Processing and Conversational Agent (CAs), we suggest a new form of human-computer interaction for individuals to receive formative feedback on their argumentation to help them to foster their logical reasoning skills. Hence, we introduce ArgueBot, a conversational agent, that provides adaptive feedback on students' logical argumentation. We, therefore, 1) leveraged a corpus of argumentative student-written peer-reviews in German, 2) trained, tuned, and benchmarked a model that identifies claims, premises and non-argumentative sections of a given text, and 3) built a conversational feedback tool. We evaluated ArgueBot in a proof-of-concept evaluation with students. The evaluation results regarding technology acceptance, the performance of our trained model, and the qualitative feedback indicate the potential of leveraging recent advances in Natural Language Processing for new human-computer interaction use cases for scalable educational feedback.

@inproceedings{wambsganss2021arguebot, abstract = {By combining recent advances in Natural Language Processing and Conversational Agent (CAs), we suggest a new form of human-computer interaction for individuals to receive formative feedback on their argumentation to help them to foster their logical reasoning skills. Hence, we introduce ArgueBot, a conversational agent, that provides adaptive feedback on students' logical argumentation. We, therefore, 1) leveraged a corpus of argumentative student-written peer-reviews in German, 2) trained, tuned, and benchmarked a model that identifies claims, premises and non-argumentative sections of a given text, and 3) built a conversational feedback tool. We evaluated ArgueBot in a proof-of-concept evaluation with students. The evaluation results regarding technology acceptance, the performance of our trained model, and the qualitative feedback indicate the potential of leveraging recent advances in Natural Language Processing for new human-computer interaction use cases for scalable educational feedback.}, added-at = {2021-07-13T12:28:36.000+0200}, author = {Wambsganss, Thiemo and Guggisberg, Sebastian and Söllner, Matthias}, biburl = {https://www.bibsonomy.org/bibtex/21fda127b94e0eb0e5683683f57321abd/wise-kassel}, booktitle = {16th International Conference on Wirtschaftsinformatik (WI)}, interhash = {e7134a212f8bf58a5caa8d4e679fd23a}, intrahash = {1fda127b94e0eb0e5683683f57321abd}, keywords = {itegpub pub_msö pub_wise-kassel}, timestamp = {2021-07-13T12:28:36.000+0200}, title = {ArgueBot: A Conversational Agent for Adaptive Argumentation Feedback}, year = 2021 }