Abstract
Chatbots belong to the many technological novelties that could be employed in educational settings. Already being shown successfully in contexts such as being able to answer frequently asked questions, more advanced educational chatbots have been developed to support learners in their studies. However, despite their potential, chatbots have faced challenges related to low interaction rates, often attributed to factors such as insufficient user engagement, limited conversational capabilities, and the inability to sustain learners’ interest over time. To address this issue, we explore the concept of gamification, a strategy frequently employed to boost extrinsic motivation in existing activities or applications. By incorporating game-like elements into an application, students may be more inclined to engage with it. In this work, we present the design, implementation, and evaluation of a gamified Quizbot called Quiz-GBot tailored for educational purposes. The chatbot records student interactions in a learning record store and subsequently gamifies these learning activities to stimulate student engagement. Our evaluation with 54 participants shows the support for tailored gamification in educational chatbots, particularly among users with gaming experience. Additionally, gamification elements impacted participants differently depending on player type, revealing the necessity for personalization to further improve user enjoyment. Importantly, features such as leaderboards can enhance motivation but should be personalizable.
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Acknowledgements
The research leading to these results has received funding from the German Federal Ministry of Education and Research (BMBF) through the project “Personalisierte Kompetenzentwicklung und hybrides KI-Mentoring” (tech4compKI) (grant no. 16DHB2213).
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Neumann, A.T., Conrardy, A.D., Decker, S., Jarke, M. (2023). Motivating Learners with Gamified Chatbot-Assisted Learning Activities. In: Xie, H., Lai, CL., Chen, W., Xu, G., Popescu, E. (eds) Advances in Web-Based Learning – ICWL 2023. ICWL 2023. Lecture Notes in Computer Science, vol 14409. Springer, Singapore. https://doi.org/10.1007/978-981-99-8385-8_15
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