A Generative Framework for Agricultural Education Curricula Alignment

Thesis Abstract

This study addresses the persistent misalignment between agricultural education curricula and the evolving competencies required by modern agribusiness, extension services, and rural development initiatives, which undermines graduate employability and transformative learning in agricultural sectors. The aim is to develop a generative framework for curricula alignment that integrates national standards, industry skill demands, and student-centered pedagogies to produce graduates capable of adaptive problem solving in complex agricultural systems. Specific objectives are (1) to map current agricultural education curricula against national competency frameworks, industry job analyses, and stakeholder expectations; (2) to identify gaps and misalignments across cognitive, psychomotor, and affective domains; (3) to formulate a generative alignment framework anchored in systems thinking and constructivist learning principles; (4) to validate the framework with expert stakeholders and pilot test its applicability in curriculum design processes; and (5) to propose implementation guidelines and evaluative metrics for continuous alignment. The study adopts a mixed-methods sequential explanatory design, beginning with a diagnostic phase followed by iterative validation. The population comprises agricultural education stakeholders, including curriculum developers, university program coordinators, extension officers, employers in agribusiness, and senior students across five accredited institutions. A stratified random sample of 40 curriculum developers, 28 program coordinators, 35 extension professionals, and 124 final-year students is drawn, with a total sample size of 227 participants. Data collection employs (a) document analysis of official curricula, syllabi, and competency standards; (b) semi-structured interviews with 40 stakeholders to elicit perceptions of alignment gaps; (c) an industry needs survey administered to 150 employers using a Likert-scale instrument on required competencies; and (d) pilot design workshops with 60 students and 12 faculty to test preliminary framework constructs. Validity and reliability are ensured through triangulation, member checking, and Cronbach’s alpha testing of the survey instrument (target ? = .88). Quantitative data are analyzed using descriptive statistics, multiple regression to examine predictors of perceived alignment, and ANOVA to assess differences by institution type. Qualitative data are analyzed thematically via NVivo, with coding guided by the theoretical underpinnings of systems theory (ecological systems perspective) and constructivist pedagogy. The analytical framework is further informed by two established theories the Skill Demands Theory, which links labor market requirements to curriculum design, and the Constructivist Curriculum Alignment Model, which emphasizes active learning and iterative refinement. The expected findings indicate substantial misalignment between current curricula and targeted occupational competencies, with gaps concentrated in advanced agronomy technologies, data analytics, sustainability metrics, and soft skills such as collaboration and communication. The generative framework is anticipated to reveal core mechanisms for alignment a modular curriculum architecture enabling flexible recombination of modules, a competency-anchored credit system, stakeholder co-design loops, and an iterative evaluation cycle employing performance-based assessment. The study contributes to knowledge by offering a rigorously developed, theory-informed, practically implementable framework that can guide policymakers, educators, and industry partners in continuously aligning agricultural education with evolving sector demands. It advances methodological practice by integrating systems theory with constructivist pedagogy in curriculum design research and by demonstrating a robust mixed-methods approach to validation. The main conclusion will articulate a scalable pathway for institutionalization of the generative framework across varying contexts, supported by implementation matrices and monitoring tools. Recommendations include establishing national advisory panels to oversee alignment processes, developing a repository of modular learning units linked to industry certifications, and instituting annual alignment audits using the proposed evaluative metrics. Further research directions will address longitudinal impact assessment on graduate employability, adaptability across different agricultural subsectors, and refinement of the framework in response to rapid technological change in farming systems.