Apply RAISE flexibly across methodologies and settings where AI is central or peripheral¶

Strategy · All strategies
Evidence · no claims cited

Description¶

RAISE is intended for all researchers conducting empirical studies involving AI as part of the educational experience, directly or indirectly — from personalised tutoring and automated grading to curriculum generation, learning analytics, conversational tools, co-writing, and simulated peer collaboration. The article states: "It applies across methodologies; qualitative, quantitative, mixed-methods, design-based, and across settings, from formal K–12 to higher education and informal learning." The framework is designed to be flexible, scalable, and responsive to varying centrality of AI use.

Design Implications¶

Context¶

Requirements¶

• Applicable where AI is a key contributing factor to the pedagogical intervention or approach

Constraints¶

• AI use may be central in some studies and peripheral in others; the framework flexes accordingly

Target Learners¶

• Researchers, reviewers, and editors across K-12, higher education, and informal learning

Target Learning Goals¶

• Consistent transparency expectations regardless of methodology or setting

Related Strategies¶

• Describe AI instructional role and learner–AI interaction design in detail (prompts, scaffolds, modality, dialogue structures)

Examples¶

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Key Sources¶

• Allison, J. (2026). RAISE the Standard: A Framework for Transparent Reporting of Artificial Intelligence Studies in Education. Journal of Educational Computing Research, 64(1), 3-15. https://doi.org/10.1177/07356331251377430