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Communities of Practice in Algorithmic Mobile Learning: Farmers' Knowledge Co-creation and Critical Engagement
Abstract
[POSTER] Digital platforms can shape learners' actions and subjectivities (Williamson, 2015) through algorithmic content curation (Smythe et al., 2022). As farmers increasingly rely on mobile phones for informal, self-directed learning, AI-driven algorithms may push unverified or conflicting information, raising concerns about misinformation. This qualitative study applies Actor-Network Theory to examine how mobile phones and algorithmic content influence farmers' learning within communities of practice, focusing on how they make sense of and co-create knowledge from 'pushed' content. Through interviews and focus groups with 18 farmers and 8 extension staff in St. Andrew's, Jamaica, we explore farmers' agentic engagement in mobile learning through communities of practice. While extension staff highlight challenges posed by overwhelming-and sometimes inaccurate-online information, farmers also engage in collective sense-making, validating algorithmic content through traditional knowledge, experimentation, and peer consultations. These interactions reflect the role of communities of learning and practice in strengthening digital literacy and knowledge co-creation. Findings emphasize the importance of community-driven approaches to critical engagement with AI-driven content. Supporting farmers and extension staff in navigating algorithmic learning through collaborative learning strategies is essential for lifelong learning and resilient agricultural knowledge systems.
PCF11 Sub-Theme: Sustaining Communities of Learning and Practice in Innovative Open Education
Paper ID: 1475
Country
Jamaica
Region
Caribbean and Americas
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Date
2025-09
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Commonwealth of Learning (COL)