Technology-Enhanced Learning for Agricultural Extension: A Multi-Channel Framework, Evaluation Model, and Design Guidelines for India

Technology-enhanced learning can extend the reach, timeliness, and personalization of agricultural extension, but its benefits depend on design choices more than on technology itself. This paper has presented a taxonomy of TEL channels, six pedagogical design principles, a layered multi-channel architecture that keeps human intermediaries in the loop, and a four-level evaluation framework. The central message is that technology should complement, not replace, trusted local relationships, and that programs should be judged by adoption and outcomes rather than downloads. Future work should empirically validate the proposed architecture and evaluation framework through field pilots, with attention to women and marginal farmers, and should study how grounded generative-AI assistants affect trust and decision quality.

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