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Our approach to AI is guided by customer questions:

K-12 Schools

  • How can AI help teachers optimize time spent in lesson planning so they can increase time on direct interaction with students?
  • Can AI-driven adaptive tools personalize learning at scale to accelerate student outcomes?
  • How can AI curriculum development tools, including text leveling, help scaffold instruction and promote equity for students with different needs?

Higher Education

  • How can AI support my research and teaching projects? Will it save me time by summarizing content, surfacing new sources, or translating content?
  • How much transparency is there behind the training data or legal status behind the first-to-market generative AI solutions?
  • What are the implications for copyright or privacy? What does “open” mean in the context of generative AI? What are the implications for libraries’ licensing agreements with publishers?

Our Generative AI Principles

We are committed to responsibly and ethically using AI in learning and research.

Intentional

We aim to apply AI effectively where it can most benefit researchers, educators, and students—from elementary to higher education—in meaningful ways.

Conscientious

We are dedicated to careful development and exploration with extensive testing in limited environments.

Progressive

We will integrate AI to expand research and learning possibilities while emphasizing trust, quality output, and respect for intellectual property.

Explore Existing AI Applications

Since the launch of Gale Digital Scholar Lab, Gale has supported teaching text and data mining, data literacy, text analysis, and—by extension—AI literacy. All tools within the Lab, such as document clustering, use natural language processing. This resource was developed in close collaboration with customers to ensure transparency, data privacy, research replicability, and extensibility. Learn more about the Lab >

What’s in the Works

Gale is actively exploring AI tools to enhance teaching and learning and address the evolving needs of researchers, educators, and libraries.

Our current areas of focus include:

  • Semantic Search: Improving search results and curriculum alignment recommendations for schools with natural language search
  • Search Translation: Supporting the needs of language learners and international researchers
  • Content Leveling: Customizing content for students at all reading levels

Curious to learn more about AI across Cengage businesses? Explore now >

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