“Interacting with a conversational agent system for educational purposes in online courses.”
Abstract
Since distance education creates new opportunities for learners, the enrollment in online courses has been sharply increasing in higher education. However, the higher attrition rate is one of the more significant concerns in this field. Educational researchers have found that meaningful interactions play a significant role in learner persistence in online courses. Still, it is challenging for an individual instructor to promote learners' positive interaction experiences. The expectation of improved learners' interaction with conversational agent systems has received attention in the distance education field. Few conversational agent systems have been developed for educational purposes, and few systems are used in real online learning settings. This study aims at designing and developing a conversational agent system to promote the learner's meaningful interaction in online courses, and also exploring the feasibility of human interaction with the conversational agent system, or chatbot, in online courses in higher education. The primary findings of this study show that instant, content-related, and quality interactions between the learner and the conversational agent system is applicable to graduate-level online courses. Implications and future research are discussed.
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Some teaching agents started the conversation by asking students to reflect on past learning.
“the teaching agent started the conversation by asking students to reflect on past learning (Song et al., 2017).”
From Interacting with educational chatbots: A systematic review (2023) · cites this paper as Song et al., 2017
Use of Natural Language Processing (NLP) supports a more natural conversation instead of one that relies on a limited dataset and a rule-based mechanism.
“Further, Song et al. (2017) argue that the use of Natural Language Processing (NLP) supports a more natural conversation instead of one that relies on a limited dataset and a rule-based mechanism.”
From Interacting with educational chatbots: A systematic review (2023) · cites this paper as Song et al., 2017
The study by Song et al. (2017) reported moderate motivation.
“| (Song et al., 2017) | Teaching agent, chatbot driven 11 | Moderate motivation | |”
From Interacting with educational chatbots: A systematic review (2023) · cites this paper as Song et al., 2017
Song et al. (2017) found that immediate interactions related to course content between learners and chatbot systems are best suited to graduate-level online courses.
“Their findings demonstrated that immediate interactions related to course content between learners and chatbot systems are best suited to graduate-level online courses.”
From Implementing the bashayer chatbot in Saudi higher education: measuring the influence on students' motivation and learning strategies (2023) · cites this paper as Song et al., 2017
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- Authors
- Song, D., Oh, E. Y., & Rice, M.
- Affiliations
- Sam Houston State University (US)
- Seoul National University (KR)
- Venue
- 2017 10th International Conference on Human System Interactions (HSI)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 78-82
- OpenAlex
- W2742631515
- Cited by
- 101
- References
- 26
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 14 · 2023: 15 · 2022: 12 · 2021: 12 · 2020: 20 · 2019: 10 · 2018: 3
- FWCI
- 4.12
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Conference on Human System Interactions (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Online and Blended Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- chatbots, online courses, distance education, learning persistence, student attrition, human-agent interaction, meaningful interaction, higher education, graduate courses, interaction quality
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