Dear Colleagues,

Objectives

Although intelligent tutoring systems (ITSs) are highly prevalent, they have limitations (Murray, 1999) in providing rich interactivity. Interactivity (Sundar, 2012), known as “the extent to which learners can participate in modifying the form and content of a mediated environment in real time (Steuer, 1992, p. 84)”, is a major driver of engagement in e-learning system design. Engagement indicates “the emotional, cognitive, and behavioral experience of a learner with a technological resource that exists, at any point in time and over time” (Lalmas et al., 2014, p. 3) and relates positively with learner involvement and learning outcomes. An ITS with greater interactivity has been shown to increase involvement, focused attention, attitude towards it, and conscious processing (Sundar, 2012). Thus, interactivity has increasingly become a key priority for e-learning system developers to promote learners’ engagement (Doherty & Doherty, 2018). In interactive e-learning systems/environments (ILEs), learners are now active instead of passive recipients (Sundar et al., 2015). An ILE designed for information sharing, negotiation, critical thinking, and knowledge construction is in line with cognitive and social constructivists that emphasize the active role of learners as knowledge constructors both individually and collaboratively (Cobb, 1994). In other words, ILEs preferably involve “both individual interaction with content and social interaction with people (Wang et al., 2009, p. 97)”, which is fundamental in cognitive development (Vygotsky, 1978) to achieve optimal learning.

With the rapid expansion of educational technology, diverse computer-based ILEs (e.g., virtual reality, digital games, and augmented reality) that allow learners to interact with learning materials using various interaction features (Patwardhan, 2016) are increasingly used to promote real-time and positive social interactions, development, and support. Among diverse ILEs, a computer program called chatbots with the intention to mimic human–human interactions facilitates interactions by answering learners’ questions and providing correct answers via audio or texts (Haristiani and Rifa’i, 2020).

The advent of big data and the increase in computing power has paved the way for new technological advancements. Artificial intelligence (AI), especially natural language processing advances, as an important AI application that focuses on making machines or computers understand human language (Nagarhalli et al., 2020), has played a crucial role in chatbot advancements. Unlike ruled-based chatbots, AI-based chatbots not only allow information automatism (Massaro et al., 2018) but also learn to be smarter over time and are more scalable and inherently adept in handling unpredictable semantics. Thus, AI-based chatbots have become an increasingly popular choice for researchers and educators (Palasundram et al., 2019) and are proving to be important to fill the gap between technologies and education. The benefits of educational chatbots have been reported in the literature, for example, longer memory retention, enhanced critical thinking skills, improved language use and engagement, and reduced anxiety (Zahour et al., 2020; Lin & Chang, 2020).

Despite chatbots’ potential in facilitating teaching and learning, the acceptance, adoption, and impact of educational chatbots is growing much slower than expected due to drawbacks such as 1) poor performance in emulating human dialogues (Zahour et al., 2020), 2) difficulties in storytelling (Nagarhalli et al., 2020), 3) zero information about learners to generate adaptive feedback (Vijayakumar et al., 2018), 4) the inability to perform goal-directed discussion (Hill et al., 2015), 5) low-quality chatbot responses (Feine et al., 2020), 6) ineffective natural language understanding (Lin and Chang, 2020), and 7) the lack of cohesion during chatbot interactions (Bailey and Almusharraf, 2021).

Therefore, it is important to resolve the above challenges by developing chatbots that best respond to learners’ personalized needs in a real human way (Ralston et al., 2019). An increasing number of researchers are using deep learning algorithms, such as deep neural networks and deep reinforcement learning, to train chatbots (Zahour et al., 2020; Nithuna and Laseena, 2020) to talk intelligently and make their conversations with learners as close to real-world conversations (Dhyani and Kumar, 2021). For example, recurrent neural networks based on sequence-to-sequence models have shown great progress in AI chatbots (Patwardhan, 2016). Convolution neural networks (CNNs) and deep CNNs can promote personalized learning experiences with chatbots (Patwardhan, 2016). However, this has not been implemented extensively in educational chatbots (Palasundram et al., 2019). Additionally, questions about 1) chatbots’ potentials to realize functions beyond mere communication, 2) factors affecting learners’ engagement with chatbots and their ability to gain benefits, 3) chatbots’ potential to facilitate high-order thinking (Bailey and Almusharraf, 2021), 4) long-term effect of educational chatbots (Ruan et al., 2021), 5) linguistic characteristics of chatbot communication (Hill et al., 2015), and 6) effects of learners’ personality traits on chatbot performance (Hill et al., 2015), remain poorly explored. In addition to educational chatbots, improvements of and advances in the design and applications of all types of ILEs, including environments supporting individual learners and environments supporting group collaboration, are also topics within the scope of this Special Issue.

Submission topics

This Special Issue aims to advance the theoretical and practical knowledge related to teaching and learning based on AI-driven chatbots and AI-empowered interactive learning environments, addressing questions that have remained unanswered and those that have emerged alongside developments and advances in relevant fields, such as computer science, education, AI, and especially deep learning. It is hoped that this could expand our knowledge of the theoretical underpinnings and pedagogical practicalities of chatbot and interactive learning environments in teaching and learning. For the scope of this Special Issue, we invite authors to submit their research work (including case studies, empirical studies, design-based research, theoretical papers, and reviews) across different educational levels (e.g., primary and secondary) and types (e.g., formal and informal) based on, but not limited to, the following topics:

• Theories and models of educational chatbots and interactive learning environments;
• Performance measure of educational chatbots and interactive learning environments;
• Educational chatbots for storytelling;
• Domain-specific educational chatbots educational chatbots for general purposes;
• Management of conversational workflow in chatbot-assisted learning;
• Linguistic characteristics of educational chatbot communication;
• Impact of learners’ attribution of human qualities or personality traits on the performance of educational chatbots and interactive learning environments;
• Educational chatbots and interactive learning environments for promoting higher- order thinking;
• Goal-directed discussions assisted by educational chatbots;
• Adaptive feedback generation in educational chatbots;
• Educational chatbots and interactive learning environments for learners with special needs;
• Learning and instruction approaches about educational chatbots and interactive learning environments for individual and collaborative learning;
• Educational chatbots and interactive learning environments for specific purposes;
• Educational chatbots and interactive learning environments for diverse learners;
• Learning emotion and classroom dialogue analysis in educational chatbots and interactive learning environments;
• Personalized learning using educational chatbots and interactive learning environments;
• Deep learning algorithms for educational chatbots and interactive learning environments;
• Learning process analysis and visualization in educational chatbots and interactive learning environments;
• Teacher training for instructions assisted by educational chatbots and interactive learning environments;
• Innovative learning outcome evaluation in educational chatbots and interactive learning environments;
• Learner behaviors in learning processes assisted by educational chatbots and interactive learning environments;
• Innovative strategies and systems for educational chatbots and interactive learning environments;
• Review studies on the recent advancements in educational chatbots and interactive learning

References

Bailey, D., & Almusharraf, N. (2021). Investigating the Effect of Chatbot-to-User Questions and Directives on Student Participation. 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA), 85–90.

Cobb, P. (1994). Where is the mind? Constructivist and sociocultural perspectives on mathematical development. Educational Researcher, 23(7), 13–20.

Dhyani, M., & Kumar, R. (2021). An intelligent Chatbot using deep learning with Bidirectional RNN and attention model. Materials Today: Proceedings, 34, 817–824. Doherty, K., & Doherty, G. (2018). Engagement in HCI: conception, theory and measurement. ACM Computing Surveys (CSUR), 51(5), 1–39.

Feine, J., Morana, S., & Maedche, A. (2020). Designing Interactive Chatbot Development Systems. Proceedings of the 41st International Conference on Information Systems (ICIS). India: AISel.

Haristiani, N., & Rifa’i, M. M. (2020). Combining chatbot and social media: Enhancing personal learning environment (PLE) in Language Learning. Indonesian Journal of Science and Technology, 5(3), 487–506.

Hill, J., Ford, W. R., & Farreras, I. G. (2015). Real conversations with artificial intelligence: A comparison between human–human online conversations and human– chatbot conversations. Computers in Human Behavior, 49, 245–250.

Lalmas, M., O’Brien, H., & Yom-Tov, E. (2014). Measuring user engagement.

Synthesis Lectures on Information Concepts, Retrieval, and Services, 6(4), 1–132. Lin, M. P.-C., & Chang, D. (2020). Enhancing Post-secondary Writers’ Writing Skills with a Chatbot. Journal of Educational Technology & Society, 23(1), 78–92.

Massaro, A., Maritati, V., & Galiano, A. (2018). Automated Self-learning Chatbot Initially Build as a FAQs Database Information Retrieval System: Multi-level and

Intelligent Universal Virtual Front-office Implementing Neural Network. Informatica, 42(4), 515–525.

Murray, T. (1999). Authoring intelligent tutoring systems: An analysis of the state of the art. International Journal of Artificial Intelligence in Education (IJAIED), 10, 98– 129.

Nagarhalli, T. P., Vaze, V., & Rana, N. K. (2020). A Review of Current Trends in the Development of Chatbot Systems. 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 706–710.

Nithuna, S., & Laseena, C. A. (2020). Review on Implementation Techniques of Chatbot. 2020 International Conference on Communication and Signal Processing (ICCSP), 157–161.

Palasundram, K., Sharef, N. M., Nasharuddin, N., Kasmiran, K., & Azman, A. (2019). Sequence to sequence model performance for education chatbot. International Journal of Emerging Technologies in Learning (IJET), 14(24), 56–68.

Patwardhan, M. (2016). Determining Interactivity Enriching Features for Effective Interactive Learning Environments. Indian Institute of Technology Bombay. http://www.et.iitb.ac.in/~sahanamurthy/students/mrinal-thesis.pdf

Ralston, K., Chen, Y., Isah, H., & Zulkernine, F. (2019). A voice interactive multilingual student support system using IBM Watson. 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 1924–1929.

Ruan, S., Jiang, L., Xu, Q., Liu, Z., Davis, G. M., Brunskill, E., & Landay, J. A. (2021). EnglishBot: An AI-Powered Conversational System for Second Language Learning. 26th International Conference on Intelligent User Interfaces, 434–444.

Steuer, J. (1992). Defining virtual reality: Dimensions determining telepresence. Journal of Communication, 42(4), 73–93.

Sundar, S. S. (2012). Social psychology of interactivity in human-website interaction. In Oxford handbook of internet psychology. Oxford University Press.

Sundar, S. S., Jia, H., Waddell, T. F., & Huang, Y. (2015). Toward a theory of interactive media effects (TIME): Four models for explaining how interface features affect user psychology. The Handbook of the Psychology of Communication Technology, 47–86.

Vijayakumar, B., Höhn, S., & Schommer, C. (2018). Quizbot: Exploring formative feedback with conversational interfaces. International Conference on Technology Enhanced Assessment, 102–120.

Vygotsky, L. (1978). Interaction between learning and development. In M. Cole (Ed.), Mind in society: the development of higher psychological processes. Harvard University Press.

Wang, Q., Woo, H. L., & Zhao, J. (2009). Investigating critical thinking and knowledge construction in an interactive learning environment. Interactive Learning Environments, 17(1), 95–104.

Zahour, O., Eddaoui, A., Ouchra, H., & Hourrane, O. (2020). A system for educational and vocational guidance in Morocco: Chatbot E-Orientation. Procedia Computer Science, 175, 554–559.

Prof. Dr. Haoran Xie
Prof. Dr. Gwo-Jen Hwang
Prof. Dr. Fu-lee Wang
Dr. Man Kong Chow
Guest Editors

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• AI in education
• interactive learning environment
• chabots
• teaching and learning