Learning to Give a Complete Argument with a Conversational Agent: An Experimental Study in Two Domains of Argumentation

This paper reports a between-subjects experiment (treatment group N = 42, control group N = 53) evaluating the effect of a conversational agent that teaches users to give a complete argument. The agent analyses a given argument for whether it contains a claim, a warrant and evidence, which are understood to be essential elements in a good argument. The agent detects which of these elements is missing, and accordingly scaffolds the argument completion. The experiment includes a treatment task (Task 1) in which participants of the treatment group converse with the agent, and two assessment tasks (Tasks 2 and 3) in which both the treatment and the control group answer an argumentative question. We find that in Task 1, 36 out of 42 conversations with the agent are coherent. This indicates good interaction quality. We further find that in Tasks 2 and 3, the treatment group writes a significantly higher percentage of argumentative sentences (task 2: t(94) = 1.73, p = 0.042, task 3: t(94) = 1.7, p = 0.045). This shows that participants of the treatment group used the scaffold, taught by the agent in Task 1, outside the tutoring conversation (namely in the assessment Tasks 2 and 3) and across argumentation domains (Task 3 is in a different domain of argumentation than Tasks 1 and 2). The work complements existing research on adaptive and conversational support for teaching argumentation in essays.