Promoting students’ motivation in language education with gamified pedagogical conversational agents
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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in: Computers and Education, Jahrgang 238, 105374, 12.2025.
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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TY - JOUR
T1 - Promoting students’ motivation in language education with gamified pedagogical conversational agents
AU - Khosrawi-Rad, Bijan
AU - Keller, Paul Felix
AU - Benner, Dennis
AU - Grogorick, Linda
AU - Borchers, Arne
AU - Janson, Andreas
AU - Leimeister, Jan Marco
AU - Robra-Bissantz, Susanne
N1 - Publisher Copyright: © 2025 The Authors
PY - 2025/12
Y1 - 2025/12
N2 - Pedagogical conversational agents (PCAs) like chatbots are a novel approach to technology-mediated language learning with artificial intelligence. They convey learning content interactively and accompany students in their education. However, many users find conversations with PCAs unmotivating. Gamification is a suitable solution to these motivational hurdles due to its playful nature. Given the difficulty of selecting the appropriate game elements and the scarcity of design recommendations for gamified PCAs, we propose the GNPL framework including a cohesive set of four design principles: goal-setting and reflection, novice-expert relationship, performance-related motivation, and learning story narration. In two design cycles, the article shows the application of the design principles in English learning – a domain commonly associated with motivational challenges – by implementing and evaluating a gamified PCA. The results show that the design principles significantly foster learners' motivation and that learners perceive a solid language learning experience, expressed by higher perceived value and social factors. They highlight the relevance of aligning the PCA's social role, the motivational impact of gamification, and the educational goals of the learning context. The design principles guide educators and developers in gamified PCA design. The paper contributes to the theory stream of PCAs by investigating learners' motivation enhancement when using PCAs. In addition, the paper provides new knowledge on meaningful gamification in an unexplored context and practical insights to solve the design challenges of selecting game elements in this context. Furthermore, it shows how language education can be supported by educational technology.
AB - Pedagogical conversational agents (PCAs) like chatbots are a novel approach to technology-mediated language learning with artificial intelligence. They convey learning content interactively and accompany students in their education. However, many users find conversations with PCAs unmotivating. Gamification is a suitable solution to these motivational hurdles due to its playful nature. Given the difficulty of selecting the appropriate game elements and the scarcity of design recommendations for gamified PCAs, we propose the GNPL framework including a cohesive set of four design principles: goal-setting and reflection, novice-expert relationship, performance-related motivation, and learning story narration. In two design cycles, the article shows the application of the design principles in English learning – a domain commonly associated with motivational challenges – by implementing and evaluating a gamified PCA. The results show that the design principles significantly foster learners' motivation and that learners perceive a solid language learning experience, expressed by higher perceived value and social factors. They highlight the relevance of aligning the PCA's social role, the motivational impact of gamification, and the educational goals of the learning context. The design principles guide educators and developers in gamified PCA design. The paper contributes to the theory stream of PCAs by investigating learners' motivation enhancement when using PCAs. In addition, the paper provides new knowledge on meaningful gamification in an unexplored context and practical insights to solve the design challenges of selecting game elements in this context. Furthermore, it shows how language education can be supported by educational technology.
KW - Artificial intelligence
KW - Design science research
KW - Gamification
KW - Language learning
KW - Pedagogical conversational agent
UR - http://www.scopus.com/inward/record.url?scp=105011992391&partnerID=8YFLogxK
U2 - 10.1016/j.compedu.2025.105374
DO - 10.1016/j.compedu.2025.105374
M3 - Journal articles
AN - SCOPUS:105011992391
VL - 238
JO - Computers and Education
JF - Computers and Education
SN - 0360-1315
M1 - 105374
ER -