This guide is designed to assist educational technology leaders and developers in creating more inclusive and equitable artificial intelligence (AI) for higher education. 

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Designed to inform technologists and non-technologists, this guide defines key concepts like equity, trust, accountability, and responsibility in the context of AI in EdTech. It examines how bias impacts equity and how addressing it can improve student outcomes, institutional metrics, and product value. To infuse equity throughout the entire machine learning pipeline, this guide offers a comprehensive roadmap including:

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• Rationale for the consideration and descriptions
• Resources including worksheets, worked examples, process maps, guiding questions, and more
• Sample code
• Principles for authentic community engagement
• Tools that project teams and developers can easily implement

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The guide empowers educational technology leaders and developers with the equity-aligned knowledge and tools needed to make AI a force for good, bridging gaps rather than widening them. Its ultimate goal is to help developers understand bias complexities, utilize diverse data sets effectively, and develop AI solutions that are fair and beneficial to all users. Whether you are a seasoned AI professional or a newcomer to the field, this guide will provide valuable insights into the ethical considerations essential for responsible AI development.

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Developers wishing to dive deeper into the technical aspects of ensuring equity in AI can access our GitHub site.

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