All solutions developed in the labs are based on empirical research. The AI Research and Data Science group aims to:
- establish strong theoretical foundations, data models and empirical evidence that support the development of lab product concepts
- enable ETS to make evidence-based decisions on concept viability and move forward strategically with product concepts that have an existing body of marketable evidence
- ensure that the lab produces scientifically grounded concepts with robust data models and empirically supportable claims about impact, efficacy and equity.
Scientists in the AI Research and Data Science group begin with understanding user needs and determining how to leverage empirical foundational research to meet those needs. Working closely with the AI Technology and Learning Science and Design groups, members:
- establish intended outcomes of the solutions being developed
- create theories of action
- develop intentional user data footprints to be built into the solution so impact can be measured
Results from foundational, applied and impact research are used to:
- advance knowledge in the educational technology field
- continually improve the solutions being developed in the labs
- provide users insight into what solutions work and under what circumstances
Focus areas
The AI Research and Data Science group has the following major focus areas:
- personalized learning and assessment
- language learning, teaching and assessment
- technology-assisted teaching and learning
- embedded formative assessment and measuring growth over time
- validity and efficacy of educational technology
- educational technology implementation and evaluation of fidelity of use
- measuring the impact of use of technology in the classroom