Stimulating and sustaining interest in a language course: An experimental comparison of Chatbot and Human task partners

By Luke K. Fryer, Mary D. Ainley, Andrew Thompson, Aaron Gibson, Zelinda Sherlock. Computers in Human Behavior, 2017

374 citations recorded by the source corpus.

Abstract Novel technology can be a powerful tool for enhancing students' interest in many learning domains. However, the sustainability and overall impact of such interest is unclear. This study tests the longer-term effects of technology on students' task and course interest. The experimental study was conducted with students in foreign language classes (n = 122): a 12-week experimental trial that included pre- and post-course interest, and a sequence of task interest measures. Employing a counterbalanced design, at three week intervals students engaged in separate speaking tasks with each of a Human and “Chatbot” partner. Students' interest in successive tasks and in the course (pre-post), were used to assess differential partner effects and course interest development trajectories. Comparisons of task interest under different partner conditions over time indicated a significant drop in students' task interest with the Chatbot but not Human partner. After accounting for initial course interest, Structural Equation Modelling indicated that only task interest with the Human partner contributed to developing course interest. While Human partner task interest predicted future course interest, task interest under Chatbot partner conditions did not. Under Chatbot partner conditions there was a drop in task interest after the first task: a novelty effect. Implications for theory and practice are discussed.

Subfields: Artificial Intelligence, Computer Science Applications, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/93e4c3f45266a51c8a2b90e2ee3d690260d027b7

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Citations in the corpus

Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education (2023, neutral): “VTAs can provide personalized support, instant feedback, and adaptive learning experiences, thereby enhancing student engagement, satisfaction, and learning outcomes [11].”
Interacting with educational chatbots: A systematic review (2022, background): “For example, the authors in (Fryer et al., 2017) used Cleverbot, a chatbot designed to learn from its past conversations with humans.”
Antecedents of customers’ acceptance of artificially intelligent robotic device use in hospitality services (2019, background): “As a result, while the hotel customers may intend to use AI robotic services due to their novelty (Fryer et al., 2017) and utilitarian bene ﬁ ts (Zalama et al., 2014), they may oppose to use these devices due to the need for social interaction (Ackerman, 2016) and for a sense of belonging (Murphy…”
A review of opportunities and challenges of chatbots in education (2021, positive): “Only a few studies have begun to pay attention to the behavior of students in the learning process; for example, Fryer et al. (2017) designed an experiment combined with a chatbot system, performing a 12week experiment for students in foreign language classes, and observed and monitored the learning”
Learning design to support student-AI collaboration: perspectives of leading teachers for AI in education (2022, background): “On the other hand, Fryer et al. (2017) demonstrate that the AI chat-bot and students learn foreign languages together through peer relationships.”
Rediscovering the use of chatbots in education: A systematic literature review (2020, background): “Some comparative studies have tried to obtain information about the knowledge transmitted by a chatbot versus by a human tutor [19], high-lighting the problems of the novelty effect.”