Abstract
Abstract
Although artificial intelligence (AI) tools are now deeply embedded in Turkish university students’ academic lives, students’ cognitive representations of AI remain instrumentally oriented and structurally fragmented. This study presents a needs assessment for designing AI literacy micro-credential programmes grounded in learners’ actual knowledge structures. Using the Word Association Test (WAT) as a psycholinguistic cognitive diagnostic instrument, data were collected from
N
= 436 undergraduate students enrolled at four public universities in Turkey (Kırklareli, İnönü, Niğde Ömer Halisdemir, and Hatay Mustafa Kemal), representing social sciences and humanities (80.3%), natural and applied sciences (12.2%), and health sciences (7.6%). WAT responses (1376 raw tokens) were analysed through a seven-stage protocol comprising canonical code taxonomy (65 codes; inter-rater reliability κ = 0.87), Kendall’s τ relatedness estimation with Benjamini-Hochberg correction, multidimensional scaling (stress = 0.396), hierarchical threshold network construction, and Z-score band analysis. Findings reveal a consistent pattern of cognitive fragmentation: utilitarian concepts-most prominently convenience (f = 145), speed, and technology-dominate students’ associative networks, while algorithmic transparency, ethical governance, and technical mechanisms remain conceptually absent. Network analysis reveals structural polarisation between positive-utility and negative-risk clusters (τ = − 0.819), confirming a pervasive ‘black-box’ orientation toward AI. Utilising these diagnostic findings as a curriculum roadmap, the study proposes an Ethical AI and Workforce Readiness micro-credential framework that bridges the gap between functional use and conceptual mastery. This study contributes the first large-scale WAT-based cognitive mapping of AI knowledge structures in Turkish higher education and demonstrates a replicable, data-driven methodology for needs-assessment-driven credential design.