SkillSwap: Smart Peer-to-Peer Skill Exchange

The proposed SwapSkills framework presents a conceptual approach for supporting reciprocal peer-to-peer skill exchange. The research addresses the limitation of conventional learning approaches in which knowledge exchange is frequently organized around predefined educational resources or instructor–learner relationships. Instead, the proposed framework considers individuals as both learners and contributors, enabling the possibility of mutually beneficial knowledge exchange.   The framework introduces a structured representation of user skills and learning requirements and proposes reciprocal matching based on complementary skills. Additional factors such as proficiency, learning objectives, preferences, availability, and previous exchange experience can further contribute to determining compatibility. The proposed exchange workflow provides a structured progression from partner discovery to exchange completion and mutual feedback.   The concept of reciprocity is particularly relevant to people-to-people recommendation because successful recommendations must consider the interests and compatibility of both participants rather than only the person requesting a recommendation. Similarly, research on online learning and peer learning highlights the importance of participation and interaction in learning environments.   The current research establishes a conceptual foundation rather than claiming an implemented or experimentally validated system. Future work can therefore focus on prototype development, intelligent matching algorithms, skill verification, reputation modelling, and user-based evaluation. Experimental studies can determine how effectively the proposed framework connects participants with complementary skills and whether reciprocal matching can contribute to meaningful peer-to-peer learning.

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