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Quality assessments, groundbreaking research and measurement, and user-driven educational solutions

Learn more about ETS Research & Development.


AI Technologies

Foundational research, modern technologies and robust, flexible data strategies are at the core of successful AI research and development. The AI technology lab team is comprised of Natural Language Processing (NLP) researchers and engineers, AI engineers, data engineers, and software developers.

The AI technology team is focused on supporting equity through the research, development and application of responsible AI. The team has documentation of best practices, guidelines and security measures that are followed throughout the ETS® AI Labs™.

The AI technology team aims to:

  • drive innovation in the NLP and AI space through foundational research and development of generalizable capabilities
  • enable advancements in learning and assessment through the application of cutting-edge technology to learning solutions
  • create a strategic technological pipeline to scale capabilities and prototypes

Technologists are 100% dedicated to capability and prototype teams through the labs for the entirety of the research and development life cycle. Sprinting with teams from discovery through ideation, prototyping and optimization, technologists can apply technological solutions that meet user needs. Similarly, the foundational research that technologists conduct are based on needs uncovered through user engagement.

Focus areas

The AI technologies team has the following major focus areas:

  • development of modern architectures that enable the effective prototyping and releasing of solutions throughout the labs
  • use of multimodal AI to assess and provide feedback on recorded presentations
  • automated assessment and feedback of written and spoken language
  • automated generation of instructional and assessment content
  • personalized learning paths based on cognitive learning model
  • automated recommendation engines
  • creating data pipelines and architectures for modeling and analytics
  • development of software to test capabilities and build prototypes that meet user needs.