Reorient AI assessment toward formative, holistic portraits of learner competencies rather than automated grading¶

Strategy · All strategies
Evidence · no claims cited

Description¶

In the assessment scenario, experts rejected AI as merely faster grading: "they did not see AI as merely making conventional assessment more accurate or efficient, but as supporting broader goals for assessment that are unmet by conventional tests." The envisioned shifts include end-of-course outcomes toward rapid formative assessment, question-answering toward realistic performances and portfolios, and narrower academic achievement toward broader competencies. AI could help a teacher build a portrait of a student's competencies across contexts and trajectory.

Design Implications¶

Context¶

Requirements¶

• Sound models or theories of learning as a complex domain are a hinge point for AI assessment; fairness and explainability of scores must be addressed

Constraints¶

• Experts asked whether AI-based assessments can be at least as fair as today's and whether scores will be explainable and trusted

Target Learners¶

• Students whose writing, collaboration, and broader competencies are assessed across settings

Target Learning Goals¶

• Broader competencies valued in education, society, and workplaces; real-time formative support for learning

Related Strategies¶

• Seven research priorities for AI in learning, spanning expanded scenarios, teacher assistance, assessment, responsible AI, equity policy, stakeholder engagement, and ecosystem strengthening
• Embed automated question categorisation in learning management systems and develop ethical guidelines for AI in formative assessment

Examples¶

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Key Sources¶

• Roschelle, J., Lester, J. & Fusco, J. (Eds.) (2020). AI and the future of learning: Expert panel report [Report]. Digital Promise. https://circls.org/reports/ai-report. doi:10.51388/20.500.12265/106