This study examines the university–community alliance with regard to experiential learning activities that may be used to enhance the competencies of agricultural extension postgraduate students (AEPS). Through research and alliance, university education provides agricultural extension students with ideal learning spaces to explore cause-related social, economic, and sustainability aspects of agriculture. The objectives of the study were for the AEPS to work on community farms for between six to eight weeks, to identify production challenges, and to attempt to solve problems using a participatory action research (PAR) approach. Students collected data daily, using parameters that included types of agro-enterprise, agricultural practices, observation and control of pests and diseases, identification, and control of weed infestation types, control of predators, and management of various security challenges. Social media were also used to share posts (pictures and videos) of the various project activities with the public for discussion and knowledge sharing. Findings show that there was an improved relationship between the students and their community collaborators. All participants mutually benefited from the programme; students gained indigenous farming knowledge from the farmers, while farmers benefited from the scientific approaches to solving common farming problems employed by the students—mostly improvised technologies with local content. Both the students and the farmers learned from the knowledge shared by various followers on Facebook, who gave suggestions to address some of the challenges posted on social media. The programme advocates the need to shift from a mostly rigid, conventional curriculum to a more dynamic, interactive one, which embraces active experimentation with theoretical knowledge. It underscores the significance of experiential learning for developing students’ technical competencies. The success of the programme could influence curriculum development and re-design to accommodate more experience-based modules.