Enhancing Social Service Learning through Chat Bots: A Centered Approach based on Directed Acyclic Graphs

Potential Abstract:
This research article explores the potential of chat bots to enhance social service learning experiences through a centered approach based on directed acyclic graphs. Service learning, as an educational pedagogy, has been widely recognized for its ability to foster civic engagement, critical thinking, and social responsibility among students. However, the implementation of service learning activities often faces challenges related to limited resources, logistical constraints, and the need for continuous support and guidance. To address these challenges, this study proposes the integration of chat bots, powered by artificial intelligence, as virtual mentors to guide and support students during their service learning experiences.

By leveraging directed acyclic graphs, a graphical model that represents the flow of conversation and decision-making within a chat bot, the proposed approach ensures a centered learning experience. The directed acyclic graphs provide a structured and adaptive framework, enabling chat bots to offer personalized guidance and resources to students based on their individual needs and progress. Additionally, chat bots can facilitate social interactions among students, allowing them to collaborate and reflect on their service learning experiences.

Through a mixed-methods research design, this study investigates the effects of the chat bot-centered approach on student engagement, learning outcomes, and overall satisfaction with their service learning experiences. Quantitative data will be collected through pre- and post-assessments, surveys, and system usage logs, while qualitative data will be gathered through focus group interviews and open-ended questions. The findings from this research will provide insights into the effectiveness of chat bots as virtual mentors in service learning contexts and their potential to enhance social and emotional learning outcomes.

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