Integrating CIPP, Technology Acceptance, and Media System Dependency Theories: A Conceptual Framework for Evaluating AI-Driven Teaching Resource Repositories in Vocational Journalism and Communication Education

Teaching resource repositories have become central infrastructure for vocational education, yet the artificial intelligence (AI) functions now embedded in these platforms, including intelligent recommendation, natural language processing (NLP) classification, learning analytics, algorithmic governance and, increasingly, generative content production, have outpaced the frameworks used to evaluate them. This conceptual paper develops an evaluation framework for AI-driven teaching resource repositories in vocational journalism and communication education, a discipline whose students are trained to work within, and critically upon, algorithmically mediated media systems. Three theories are assigned distinct and non-overlapping roles rather than being combined eclectically. The Context-Input-Process-Product (CIPP) model supplies programme-evaluation logic and generates the evaluation structure; the Technology Acceptance Model (TAM) specifies a mediating acceptance mechanism on one designated path rather than a general conditioning factor; and Media System Dependency (MSD) theory specifies goal-oriented dependency as a moderator whose valence is dual, amplifying adverse as well as beneficial repository effects. The framework was developed through an integrative, theory-driven review conducted under a stated search and selection protocol, and its derivation is made inspectable through a theory-to-dimension matrix. It specifies five evaluation dimensions, namely resource quality, technological adaptability, instructional integration, learning and vocational competence outcomes, and ethical and professional compliance, with the context component of CIPP retained as an evaluative frame rather than as a scored dimension, and with overall repository performance specified as a second-order construct comprising the dimensions rather than as a separate outcome predicted by them. Generative AI functions are brought explicitly within scope through criteria for generation accuracy, provenance, human verification and copyright, and the compliance dimension is extended to the discipline-specific concerns of misinformation, source attribution, synthetic media, algorithmic amplification and editorial accountability. Seven propositions are advanced, each stated without presupposing untested weight hierarchies and each accompanied by a proposed test. A staged validation pathway proceeds from Delphi indicator development and Analytic Hierarchy Process weighting to construct-validity testing, structural equation modelling of the mediated path, and multigroup comparison of dependency effects across resource-rich and resource-constrained institutions. The framework is developed primarily for the discipline-specific national repository systems characteristic of the Chinese vocational college sector, with the Malaysian technical and vocational education system as a secondary reference, and it aligns with United Nations Sustainable Development Goal 4 (Quality Education).

AI-driven teaching resource repository; conceptual framework; vocational journalism and communication education; CIPP-TAM-MSD integration; second-order construct; Quality Education

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