The architecture of AI credentials: how design quality and educational context build self-efficacy and shape readiness for AI-intensive work
Muhammad Waqas, Usman Ahmad Qadri, Alsadig Mohamed Ahmed MoustafaPurpose
This study examines how the design of AI-focused higher education credentials shapes students' readiness for AI-intensive work. Drawing on Social Cognitive Career Theory and constructive alignment, it investigates whether perceived AI-credential design quality enhances perceived workforce readiness via AI self-efficacy and how institutional ethical AI climate and faculty AI pedagogical competence condition this process.
Design/methodology/approach
Two quantitative studies were conducted with students at Chinese public universities that offer AI-focused programs and micro-credentials. Study 1 used a scenario-based experiment (N = 205) to compare high- and low-quality AI micro-credential designs. Study 2 used a two-wave field survey (N = 290) of students enrolled in actual AI-focused credentials.
Findings
Across both studies, perceived AI-credential design quality was positively associated with perceived readiness for AI-intensive roles. AI self-efficacy partially mediated this relationship. Institutional ethical AI climate and faculty AI pedagogical competence strengthened the AI self-efficacy–readiness link and amplified the indirect effect of design quality on readiness through AI self-efficacy.
Originality/value
The study positions AI credentials as ecosystem artifacts rather than standalone course labels. It extends SCCT to AI-intensive labor markets, offers a student-centered view of constructive alignment in AI curricula and demonstrates how ethical AI climate and faculty AI pedagogical competence jointly shape the readiness effects of AI-credential design.