Stakeholder-Driven Contextual Evaluation of AI Bias: Novel Conceptions, Methods, Tools

Research Overview

This research explores translanguaging in AI-assisted classrooms.

Objectives

  • Understand how students switch languages while learning.
  • Build AI tools that adapt to language diversity.
  • Enable equitable education through tech.

Image Example

TRAIL Lab Logo

Dataset Sample

{
  "student_id": "abc123",
  "language": "Spanglish",
  "utterance": "Yo pienso que the Earth orbits el sol."
}

Inline code like const x = 5 is also supported.

Results Table

Metric Value
Accuracy 93.5%
Precision 91.2%
Recall 92.7%

External Resources

Future Work

  1. Expand model support for additional languages.
  2. Partner with more bilingual schools.
  3. Publish findings in academic venues.

Generated on: June 19, 2025

Publications

2019

Using Participatory Design to Facilitate In-service Teacher Learning in Computational Thinking

Shamya Karumbaiah, Sugat Dabholkar, Jooeun Shim, Susan Yoon, Betty Chandy, Andy Ye

Proceedings of the 13th International Conference on Computer Supported Collaborative Learning (CSCL)

View Paper

2018

Is Student Frustration in Learning Games More Associated with Game Mechanics or Conceptual Understanding?

Shamya Karumbaiah, Seyedahmad Rahimi, Ryan Baker, Valerie Shute, Sidney Dmello

Proceedings of the 13th International Conference of the Learning Sciences (ICLS)

View Paper

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