Journal of Scientific Research and Reports

Artificial intelligence (AI) is increasingly being explored as a means of supporting rural youth in accessing agricultural knowledge, public services, education, and livelihood opportunities. However, access to a smartphone or internet connection does not necessarily provide the ability to locate reliable information, evaluate digital advice, communicate safely, or use online services independently. This paper develops a design-oriented conceptual framework for integrating AI-supported learning into agricultural extension to strengthen these practical digital competencies among rural youth. The framework combines baseline task-based assessment, adaptive learning recommendations, local-language microlearning, agricultural task-based exercises, peer support, and human facilitation by extension workers. The manuscript is conceptual and protocol-oriented; no primary dataset was supplied, no field intervention was conducted, and no empirical outcomes are claimed. The framework is informed by literature on digital competence, digital agricultural extension, and AI adoption in extension services. It also specifies a proposed mixed-method evaluation protocol involving baseline and post-intervention tasks, follow-up assessment, learner interviews, and subgroup analysis where feasible. The paper outlines implementation components, measurable indicators, ethical safeguards, and priorities for future field validation. It argues that AI may help personalise and extend agricultural learning, but equitable and trustworthy implementation requires local-language design, accessible infrastructure, human verification, privacy protection, and continued face-to-face support.