Artificial Intelligence (AI) Student Assistants in the Classroom: Designing Chatbots to Support Student Success
Information Systems Frontiers
In higher education, low teacher-student ratios can make it difficult for students to receive immediate and interactive help. Chatbots, increasingly used in various scenarios such as customer service, work productivity, and healthcare, might be one way of helping instructors better meet student needs. However, few empirical studies in the field of Information Systems (IS) have investigated pedagogical chatbot efficacy in higher education and fewer still discuss their potential challenges and drawbacks. In this research we address this gap in the IS literature by exploring the opportunities, challenges, efficacy, and ethical concerns of using chatbots as pedagogical tools in business education. In this two study project, we conducted a chatbot-guided interview with 215 undergraduate students to understand student attitudes regarding the potential benefits and challenges of using chatbots as intelligent student assistants. Our findings revealed the potential for chatbots to help students learn basic content in a responsive, interactive, and confidential way. Findings also provided insights into student learning needs which we then used to design and develop a new, experimental chatbot assistant to teach basic AI concepts to 195 students. Results of this second study suggest chatbots can be engaging and responsive conversational learning tools for teaching basic concepts and for providing educational resources. Herein, we provide the results of both studies and discuss possible promising opportunities and ethical implications of using chatbots to support inclusive learning.
Chatbots, Conversational agents, Higher education, Inclusive learning
Information Systems and Technology
Yu Chen, Scott Jensen, Leslie J. Albert, Sambhav Gupta, and Terri Lee. "Artificial Intelligence (AI) Student Assistants in the Classroom: Designing Chatbots to Support Student Success" Information Systems Frontiers (2023): 161-182. https://doi.org/10.1007/s10796-022-10291-4