Institutional benchmarking and career readiness tools built on the framework¶

Element · All elements
Evidence · no claims cited

Description¶

The chapter describes two applied uses of the framework as tools. As an institutional benchmarking tool, "By mapping existing courses and programs to the framework's dimensions and proficiency levels, institutions can identif y strengths and areas for improvement in their AI literacy initiatives," via course-framework matrices, regular content audits, institutional goals, and results-informed resource allocation. As a career readiness tool, curriculum committees evaluate job requirements across the seven knowledge dimensions, career services build AI competency profiles from job specifications, and advisors guide students against career requirements.

Design Implications¶

Context¶

Requirements¶

• Coordinated institutional efforts with defined responsibilities across academic leadership, curriculum committees, career services, advisors, and assessment specialists

Constraints¶

• Effective implementation requires governance structures and clearly defined roles, which the authors note many institutions may struggle to resource

Target Learners¶

• higher education students, with the tool operated by administrators, advisors, and assessment specialists

Target Learning Goals¶

• aligning educational outcomes with industry AI and data literacy needs and tracking programmatic progress

Claims¶

Related Elements¶

• Talent Development Framework Excel-based data tool
• STaR Chart: a self-assessment tool for school technology and readiness

Examples¶

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Key Sources¶

• Kathleen Kennedy & Anuj Gupta. (2025). AI & Data Competencies: Scaffolding holistic AI literacy in Higher Education. Thresholds Volume 48, Issue 2. https://journals.ku.edu/thresholds