ECIS 2020 Research Papers


Conversational agents (CAs), defined as software with which users interact through natural language, have gained increasing interest in education due to their potential to support individual learning on a large scale. With improved capabilities driven by advances in machine learning and natural language processing, these agents pave the way for a new generation of tutoring systems that offer an intuitive learning experience and can automatically tailor to individual learning styles and needs. A particular characteristic of CAs is their potential for anthropomorphic design, which can facilitate the feeling of a human contact in technology-enabled individual learning and contribute to a learner’s motivation. While recent studies provide valuable prescriptive knowledge on how to design pedagogical CAs, we still lack an understanding of the impact of a human-like design on individual motivational regulations and perceived inclusiveness. In this study, we contribute to closing this research gap by investigating individual learner’s perception of a CA by means of a 3x1 experiment with 149 participants. Drawing on Social Response Theory and Self-Determination Theory, we find empirical evidence that anthropomorphic design and associated perceptions of humanness contribute to individual intrinsic motivation of learners and discover a positive effect on inclusiveness.



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