Assess AI competencies continuously with formative quizzes, peer review, reflective journals, real-world summative projects, and portfolios¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The chapter recommends continuous evaluation of AI competencies to track student progress across the framework's dimensions. Formative strategies include regular quizzes on AI concepts, peer reviews of AI project proposals, and reflective journals. Summative assessments should "reflect real-world AI challenges," such as capstone projects with ethical and societal impact analysis, comprehensive exams, and industry-partnered projects. "Encouraging students to document their AI journey through portfolios can provide a holistic view of their developing competencies," supplemented by showcases and symposiums.

Design Implications¶

Context¶

Requirements¶

• Alignment of assessment tasks with the framework's knowledge dimensions and proficiency levels

Constraints¶

• Presented as recommended practice; the chapter reports no empirical evaluation of these assessment approaches

Target Learners¶

• higher education students developing AI and data literacy

Target Learning Goals¶

• demonstrating technical, ethical, and collaborative AI competencies

Related Strategies¶

• Summative Assessment to Measure Learning Over Time
• Balanced Assessment System
• Portfolio Assessment
• Portfolio Development
• Redesign assessment reflectively for possible AI use, using supervised tasks for lower-order skills and intentional AI use with reflection for higher-order work

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