> ## Documentation Index
> Fetch the complete documentation index at: https://docs.clairelabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Claire is based on research

> Explore the peer-reviewed research that informs how Claire uses AI for grading support — including what the evidence says about AI feedback effectiveness.

Claire's approach to AI-assisted grading and feedback is grounded in peer-reviewed research. The studies below informed key design decisions — including how Claire positions AI suggestions as inputs to instructor judgment rather than replacements for it, and how it handles the complementary roles of AI and human feedback in student learning.

## Featured research

<CardGroup cols={2}>
  <Card title="Comparing GenAI and teacher feedback" icon="book-open" href="/research/paper/paper-1">
    Students rated teacher feedback as more helpful and trustworthy, but valued GenAI for ease of access, timeliness, and volume. AI and teacher feedback appear to serve different, complementary needs.
  </Card>

  <Card title="How does AI compare to human feedback?" icon="book-open" href="/research/paper/paper-2">
    A meta-analysis of 41 studies found no statistically significant difference in learning performance between students who received AI versus human feedback, advocating for a hybrid approach.
  </Card>

  <Card title="GenAI explanations and educator grading practices" icon="book-open" href="/research/paper/paper-3">
    Research examining how GenAI-generated explanations influence educator grading decisions and feedback practices in real assessment contexts.
  </Card>

  <Card title="AI feedback perceptions by non-native English speakers" icon="book-open" href="/research/paper/paper-4">
    A study exploring how non-native English speakers perceive and experience AI-generated feedback differently from native speakers — with implications for equity and accessibility.
  </Card>
</CardGroup>