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ArgueBot: A Conversational Agent for Adaptive Argumentation Feedback

In: Innovation Through Information Systems

Author

Listed:
  • Thiemo Wambsganss

    (University of St.Gallen (HSG))

  • Sebastian Guggisberg

    (University of St.Gallen (HSG))

  • Matthias Söllner

    (University of Kassel)

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.

Suggested Citation

  • Thiemo Wambsganss & Sebastian Guggisberg & Matthias Söllner, 2021. "ArgueBot: A Conversational Agent for Adaptive Argumentation Feedback," Lecture Notes in Information Systems and Organization, in: Frederik Ahlemann & Reinhard Schütte & Stefan Stieglitz (ed.), Innovation Through Information Systems, pages 267-282, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-86797-3_18
    DOI: 10.1007/978-3-030-86797-3_18
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