Conversational agents for academically productive talk: a comparison of directed and undirected agent interventions

Conversational agents that draw on the framework of academically productive talk (APT) have been lately shown to be effective in helping learners sustain productive forms of peer dialogue in diverse learning settings. Yet, literature suggests that more research is required on how learners respond to and benefit from such flexible agents in order to fine-tune the design of automated APT intervention modes and, thus, enhance agent pedagogical efficacy. Building on this line of research, this work explores the impact of a configurable APT agent that prompts peers to build on prior knowledge and logically connect their contributions to important domain concepts discussed in class. A total of 96 computer science students engaged in a dialogue-based activity in the context of a Human-Computer Interaction (HCI) university course. During the activity, students worked online in dyads to accomplish a learning task. The study compares three conditions: students who collaborated without any agent interference (control), students who received undirected agent interventions that addressed both peers in the dyad (U treatment), and students who received directed agent interventions addressing a particular learner instead of the dyad (D treatment). The results suggest that although both agent intervention methods can improve students’ learning outcomes and dyad in-task performance, the directed one is more effective than the undirected one in enhancing individual domain knowledge acquisition and explicit reasoning. Furthermore, findings show that the positive effect of the agent on dyad performance is mediated by the frequency of students’ contributions displaying explicit reasoning, while most students perceive agent involvement favorably.