Involve teachers as co-designers of AI-based intelligent systems, with agency to train, correct, interpret, and override system recommendations¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The reflections panel recommends human-centered AI in learning: 'Empower and involve teachers (in addition to subject matter experts, learning scientists, and technologists) from the beginning as co-designers of AI-based intelligent systems'. Teachers should be enabled to train and correct systems that respond incorrectly, and 'Teachers and students should also be given agency to interpret and potentially override a system's recommendations'.

Design Implications¶

Context¶

Requirements¶

• Meaningful, inclusive partnerships with practitioners and institutions serving underrepresented minorities should be identified during the proposal-writing phase

Constraints¶

Target Learners¶

• Teachers and students using AI-based learning systems

Target Learning Goals¶

• Learning with AI-based intelligent systems across subject matters

Related Strategies¶

• Center co-design with teachers and students throughout AI tool R&D, using human-in-the-loop workflows as a primary risk mitigation
• Seven research priorities for AI in learning, spanning expanded scenarios, teacher assistance, assessment, responsible AI, equity policy, stakeholder engagement, and ecosystem strengthening
• Layered educator support: coaching, curated resources, low-lift entry points, and community engagement
• Systemic, human-centred approach treating digital data use as a school-and-teacher system, with AI integrated into professional development
• AI system developers should ensure human-centric design, institutional leaders should stay updated on global AI regulations, and LA experts should engage policymakers through dialogue
• Deploy AI-assisted classroom observation as a complementary first-pass screening and reflection tool, with raters retaining interpretive judgment
• Implement educational chatbots with human-centred design, explicit AI policies, training, curated knowledge bases and continuous monitoring
• Involve domain experts throughout the AI safety evaluation pipeline, especially for defining unsafe content

Examples¶

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Key Sources¶

• Center for Innovative Research in Cyberlearning (2020). Ambitious mashups: Reflections on a decade of cyberlearning research. [Report]. Digital Promise. https://circlcenter.org/resources/reflections-report/