Immersive multi-modal pedagogical conversational artificial intelligence for early childhood education: An exploratory case study in the wild
Sinem Aslan, Lenitra Durham, Neşe Alyüz, Eda Okur, Sangita Sharma, Celal Savur, Lama Nachman
Intel (United States)
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Educational technology research has found that parents of young children widely share concerns about extended screen time, lack of physical activity, and lack of social interaction. Kid Space was developed to address these concerns by enabling multi-modal and immersive collaborative play-based learning. Kid Space utilizes multiple sensing technologies with an immersive physical space through a human-scale wall projection and incorporates a conversational AI agent to interact with children, understand individual progress, and personalize learning experiences in a blended physical and digital environment. To evaluate Kid Space in the wild, we conducted a multi-method user study involving a quasi-experimental design and exploratory case study with 14 students and three educators in an elementary school. Mixed methods for data collection and analysis were used to understand the students' and educators' perceptions of Kid Space and its impact on the students’ educational outcomes (learning engagement, experience, and performance). The findings showed (1) positive perceptions toward Kid Space, (2) high levels of engagement - with decreased screen time (41% of the time), increased physical activity (99.3% of the time), and increased social interactions with conversational AI agent and the other collaborating student (52% of the time), and (3) significant learning gains after experiencing Kid Space (24% gain, paired t-test: p < 0.01). These positive results are accompanied by critical user insights for improving future iterations of Kid Space to validate long-term educational outcomes.
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计算机 / AIIntelligent Tutoring Systems and Adaptive Learning
Augmented Reality Applications · Artificial Intelligence in Education
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