Let's teach Kibot

Discovering discussion patterns between student groups and two conversational agent designs

Bibliographic Data

ID21297154
AuthorsHa Nguyen (0000-0001-8327-5488, School of Education University of California, Irvine Irvine California USA, corresponding author)
Year2022
Volume53
Issue6
Pages1864-1884
Publication date2022-11-01
Open AccessYes
TypeARTICLE
VenueBritish Journal of Educational Technology (JOURNAL)
Journal identifiersISSN: 0007-1013 • E-ISSN: 1467-8535
PublisherWiley (PUBLISHER • GB)
DOI10.1111/bjet.13219
OpenAlexW4226057045
LanguageEN
Citations received12
References cited63

Conversational agents can deepen reasoning and encourage students to build on others' knowledge in collaborative learning. Embedding agents in group work, however, presents challenges where groups may ignore the agents, and this calls for designs where students perceive agents as learning partners. This study examines group interactions with two text‐based agents (ie, chatbots) that posed as an expert and a less knowledgeable peer in a high school marine biology lesson. Student messages ( N = 1764) from 18 groups (52 students ages 14–15) received codes for reasoning, building on prior ideas, and responsiveness to the agents. Results indicate no differences between agents in how often each discussion move occurred. Interestingly, sequential pattern mining suggests that the less‐knowledgeable‐peer agent prompted groups to show questioning and building on others' ideas, similar to how students may act as peer tutors to the agent. Meanwhile, sequences with the expert agent resembled student‐teacher exchange, where groups responded to the agent's nudges and then provided reasoning. Findings illustrate the affordances of embedding humanized features in technology designs to promote discussion. Practitioner notes What is already known about this topic Conversational agents can facilitate group discussions, but can get abused or ignored by student groups. To engage students, agent designs can simulate the characteristics of familiar classroom figures, such as peers or teachers. There are limited explorations of how student groups adapt their interactions to different agent designs in collaborative settings. What the paper adds Illustrations of the utilities of diversifying agent designs in collaborative learning. Insights into the unique interaction patterns that student groups displayed to different designs, such as questioning and building on others' ideas with a less‐knowledgeable‐peer agent. Implications for practice and/or policy Embedding signs of humanness in classroom facilitation and technology design can foster responsiveness among users. Pedagogy can consider adaptive designs to promote interaction sequences that contribute to learning (eg, questioning, expanding on prior ideas) at opportune moments.

Affordance · Collaborative learning · Cooperative learning · Group (periodic table) · Human–computer interaction · Mathematics education · Session (web analytics) · Teaching method · World Wide Web · AI in Service Interactions · Computer Science · Innovative Teaching and Learning Methods · Psychology · Speech and dialogue systems

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