A review of opportunities and challenges of chatbots in education
By Gwo-Jen Hwang, Ching-Yi Chang. Interactive Learning Environments, 2021
449 citations recorded by the source corpus.
ABSTRACT This study explores the trends of chatbots in education studies by conducting a literature review to analyze relevant papers published in the Social Science Citation Index (SSCI) journals by searching the Web of Science (WoS) database. From the analysis results, it was found that the United States, Taiwan and Hong Kong are the top three contributing countries or regions. In addition, most studies adopted quantitative methods in their research design, such as ANOVA (Analysis of variance), descriptive statistics, t test, and correlation analysis. ANCOVA (Analysis of covariance) was the most frequently adopted approach for comparing the performances or perceptions of different groups of students. From the analysis results, the greatest proportion of studies adopted guided learning, followed by no learning activities. It was determined that the studies related to chatbots in education are still in an early stage since there are few empirical studies investigating the use of effective learning designs or learning strategies with chatbots. This implies much room for conducting relevant research to drive innovative teaching in terms of improving the learning process and learning outcomes. Finally, we highlight the research gaps and suggest several directions for future research based on the findings in the present study.
Subfields: Artificial Intelligence, Computer Science Applications
View Paper: https://www.semanticscholar.org/paper/fd605038195e1dc07dab312bc81112f692b9a0c0
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In Artificial Intelligence
In Computer Science Applications
Citations in the corpus
- Perceptions and usage of AI chatbots among students in higher education across genders, academic levels and fields of study (2024, background): “…empirical research to support or reject claims made about the benefits and challenges of AI in education, and in particular large language models is, however, scarce, and research on AI chatbots in education is still very much in a state of evolution (Hwang & Chang, 2021; Rudolph et al., 2023).”
- Interacting with educational chatbots: A systematic review (2022, methodology): “…2020) Objectives and learning experiences (Winkler & Söllner, 2018; Cunningham-Nelson et al., 2019; Pérez et al., 2020; Wollny et al., 2021; Hwang & Chang, 2021) How it is applied (Winkler & Söllner, 2018; Cunningham-Nelson et al., 2019; Wollny et al., 2021) Design approaches (Winkler &…”
- New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution (2023, background): “The advent of chatbots has also affected the field of education [3].”
- Determinants of Intention to Use ChatGPT for Educational Purposes: Findings from PLS-SEM and fsQCA (2023, unknown): “Prior studies revealed that using AI-based chatbots has a wide range of benefits for education (e.g., Al-Sharafi et al., 2022; Hwang & Chang, 2021).”
- Will ChatGPT get you caught? Rethinking of Plagiarism Detection (2023, background): “Education has been one of the prominent sectors that has greatly benefited from this advancing AI technology [8].”
- Artificial Intelligence (AI) Student Assistants in the Classroom: Designing Chatbots to Support Student Success (2022, background): “…agents (Luger & Sellen, 2016) such as chatbots (Schlesinger et al., 2018; Stieglitz et al., 2021), have been leveraged to assist student life in education (Goel & Polepeddi, 2016; Hwang & Chang, 2021; Kerly et al., 2007; Kerry et al., 2009; Okonkwo & Ade-Ibijola, 2020, 2021; Zhao et al., 2020).”