ArgueBot: A Conversational Agent for Adaptive Argumentation Feedback

Thiemo Wambsganss (), Sebastian Guggisberg () and Matthias Söllner ()
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Thiemo Wambsganss: University of St.Gallen (HSG)
Sebastian Guggisberg: University of St.Gallen (HSG)
Matthias Söllner: University of Kassel

A chapter in Innovation Through Information Systems, 2021, pp 267-282 from Springer

Abstract: 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.

Keywords: Argumentation learning; Argumentation mining; Pedagogical conversational agents (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-86797-3_18

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DOI: 10.1007/978-3-030-86797-3_18

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