Essential Features and Critical Issues With Educational Chatbots

Riel, Jeremy

doi:10.4018/978-1-7998-3476-2.ch015

Cited by 5 publications

(6 citation statements)

References 37 publications

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“…Lastly, the chatbot provides learning choices with optional links to content-specific videos and voluntary self-evaluation to support choice motivation (Patall et al., 2008). In sum, the chatbot-based learning design in this study ensures its motivational and educational objectives are met with a verifiable conversational functionality, educational goals, and pedagogical roles that strengthen students’ ability to learn autonomously at a self-directed learning pace (Riel, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Conversation Technology With Micro-Learning: The Impact of Chatbot-Based Learning on Students’ Learning Motivation and Performance

Jia-qi

Goh

Bing

et al. 2020

Journal of Educational Computing Research

156

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This study investigated the impact of a chatbot-based micro-learning system on students’ learning motivation and performance. A quasi-experiment was conducted with 99 first-year students taking part in a basic computer course on number system conversion. The students were assigned to a traditional learning group or a chatbot-based micro-learning group. After the experiment, both groups achieved a comparable performance, suggesting that students are sufficiently competent to learn independently in the chatbot-based learning environment without the need for continuous face-to-face delivery. Moreover, students in the chatbot learning group attained significantly higher intrinsic motivation than the traditional learning group with perceived choice and perceived value as core predictors of intrinsic motivation. Further analysis with the Johnson-Neyman procedure revealed differences on interaction between the perceived choice and the learning environments. For students with a high initial perceived choice (>=5.1), chatbot-based learning further enhances their post choice motivation whereas for students with a low initial perceived choice (<=3.0), the traditional classroom is more suitable to enhance their post choice motivation. The implications of the findings can help instructors to incorporate chatbot-based learning in the classroom.

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Section: Literature Reviewmentioning

confidence: 99%

Conversation Technology With Micro-Learning: The Impact of Chatbot-Based Learning on Students’ Learning Motivation and Performance

Jia-qi

Goh

Bing

et al. 2020

Journal of Educational Computing Research

156

View full text Add to dashboard Cite

show abstract

“…PSN of different technologies related to teaching and learning is explored in the literature, such as virtual teaching assistants (Benedetto et al, 2019), a multilingual personalized chatbot (Lin et al, 2020) and education chatbot with a learning avatar (Vladova et al, 2019). PSN includes a T-bot's ability to provide personalized study material, presentations, videos and answer individual queries (Kadariya et al, 2019;Riel, 2019;Vladova et al, 2019). Moreover, T-bots can maintain a one-on-one conversation with students by understanding their personalized information and feedback.…”

Section: H2b Peu Positively Influences the Pfl Of T-botsmentioning

confidence: 99%

Students' adoption of AI-based teacher-bots (T-bots) for learning in higher education

Pillai¹,

Metri

Kaushik

2023

ITP

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PurposeThe purpose of this paper is to investigate students' adoption intention (ADI) and actual usage (ATU) of artificial intelligence (AI)-based teacher bots (T-bots) for learning using technology adoption model (TAM) and context-specific variables.Design/methodology/approachA mixed-method design is used wherein the quantitative and qualitative approaches were used to explore the adoption of T-bots for learning. Overall, 45 principals/directors/deans/professors were interviewed and NVivo 8.0 was used for interview data analysis. Overall, 1,380 students of higher education institutes were surveyed, and the collected data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique.FindingsThe T-bot's ADI’s antecedents found were perceived ease of use, perceived usefulness, personalization, interactivity, perceived trust, anthropomorphism and perceived intelligence. The ADI influences the ATU of T-bots, and its relationship is negatively moderated by stickiness to learn from human teachers in the classroom. It comprehends the insights of senior authorities of the higher education institutions in India toward the adoption of T-bots.Practical implicationsThe research provides distinctive insights for principals, directors and professors in higher education institutes to understand the factors affecting the students' behavioral intention and use of T-bots. The developers and designers of T-bots need to ensure that T-bots are more interactive, provide personalized information to students and ensure the anthropomorphic characteristics of T-bots. The education policymakers can also comprehend the factors of T-bot adoption for developing the policies related to T-bots and their implications in education.Originality/valueT-bot is a new disruptive technology in the education sector, and this is the first step in exploring the adoption factors. The TAM model is extended with context-specific factors related to T-bot technology to offer a comprehensive explanatory power to the proposed model. The research outcome provides the unique antecedents of the adoption of T-bots.

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“…However, those were very limited systems in terms of the capacity of the models used at the time (Hutchens, 1996). Only recently this type of software has been able to use more complex vocabularies and more robust computer models to provide better responses to users (Hoy, 2018;Riel, 2020).…”

Section: State Of the Artmentioning

confidence: 99%

The use of chatbot as an element of tutorial action in university teaching

Valdivieso¹

2021

ReiDoCrea: Revista Electrónica De Investigación Docencia Creativa

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It is of great importance to help and pay attention to students through different educational activities to ensure their participation in class and thus reduce the dropout rate. Traditionally, tutoring activities have been limited to face-to-face sessions in which students pose questions to the teacher. However, in a connected world with many available information systems, innovative tools are needed to facilitate and speed up both the study and the resolution of doubts in a comfortable way. Methods: This paper proposes using a chatbot based tutoring system as a novel educational experience focused on motivating universities students. Results: Besides, we provide a proof-of-concept implementation of a chatbot that answers questions as quickly and accurately possible at any time, in a comfortable way for the students, and at the same time it gathers feedback from the students regarding those topics that need to be explained in class in more detail. Conclusions: This experience is intended to increase the engagement and collaboration of both students and instructors and has helped to decrease the dropout rate in recent years.

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Essential Features and Critical Issues With Educational Chatbots

Cited by 5 publications

References 37 publications

Conversation Technology With Micro-Learning: The Impact of Chatbot-Based Learning on Students’ Learning Motivation and Performance

Conversation Technology With Micro-Learning: The Impact of Chatbot-Based Learning on Students’ Learning Motivation and Performance

Students' adoption of AI-based teacher-bots (T-bots) for learning in higher education

The use of chatbot as an element of tutorial action in university teaching

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