Use generative AI to make assessment systems more instructionally relevant, not to reify problematic practices¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The report recommends harnessing generative AI for reimagining assessment systems rather than reinforcing harmful practices. It states: "GenAI should be used to make assessment systems more instructionally relevant, not to reinforce practices that have the potential to cause harm to students and their learning." Instead of using large-language models to generate superficial multiple-choice items, developers could use the technology to customize deeper learning tasks to local contexts and operationalize asset-based scoring and reporting of performance assessments at scale.

Design Implications¶

Context¶

Requirements¶

• Instructional relevance and equity considerations as first-order principles when deciding whether and how to incorporate genAI

Constraints¶

• The report frames this as a near-future possibility contingent on current efforts, not an established result

Target Learners¶

• K-12 students in state assessment systems

Target Learning Goals¶

• Authentic and sophisticated assessment at scale
• Asset-based scoring and reporting of performance assessments

Affordances¶

• Six Principles Instructionally Relevant Assessment

Related Strategies¶

• Districts should build community-shared visions, evaluation infrastructure, and policies for procuring and using AI edtech
• Seven research priorities for AI in learning, spanning expanded scenarios, teacher assistance, assessment, responsible AI, equity policy, stakeholder engagement, and ecosystem strengthening
• Integrate co-design and continuous feedback loops into AI evaluation processes
• Use GenAI-driven disruption as an occasion to redesign assessment practices rather than preserve them

Examples¶

-

Key Sources¶

• Badrinarayan, A. (2024). Design principles for instructionally relevant assessment systems. Learning Policy Institute. https://doi.org/10.54300/111.528