Reconstructing Student Learning Engagement through AI-Supported Service-Learning: Evidence from a Community-Based Educational Intervention in Eastern Indonesia
Keywords:
Artificial Intelligence, Community-Based Learning, Developing Regions, Service-Learning, Student EngagementAbstract
This study aims to reconstruct student learning engagement through the development and implementation of an Artificial Intelligence (AI)–supported service-learning model within a community-based educational intervention in Eastern Indonesia. The research addresses a critical gap between the rapid adoption of AI technologies and persistently low student engagement in developing regions, as well as the limited integration of service-learning approaches with adaptive technologies. Employing a mixed-methods approach with an embedded case study design, quantitative data were collected through pre- and post-test surveys to assess changes in cognitive, affective, and behavioral engagement, while qualitative data were obtained through in-depth interviews, participatory observation, and focus group discussions. The intervention involved needs assessment, program design, implementation in classroom and community contexts, and participatory reflective evaluation. The findings indicate that integrating AI within a service-learning framework significantly enhances multidimensional student engagement, strengthens the social relevance of learning, and transforms teachers’ roles from knowledge transmitters to learning facilitators. Furthermore, AI functions effectively as a co-constructive agent that mediates adaptive and collaborative learning processes. The novelty of this study lies in proposing an integrative model that positions AI as an integral component of a community-based learning ecosystem, bridging technological, pedagogical, and social dimensions. The study concludes that reconstructing student engagement requires the alignment of technological innovation with contextual pedagogical approaches.
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