Algorithmic Bias in Education

Algorithmic Bias in Education

This Wiki summarizes the current evidence surrounding Algorithmic Bias in Education: which groups are impacted, and in which contexts.

For a relatively recent review on this topic, see Baker, R.S., Hawn, M.A. (in press) Algorithmic Bias in Education. To appear in International Journal of Artificial Intelligence and Education (pdf)

Note: Within this Wiki, we recommend that article editors use the group labels originally used within the publications being cited, to best represent the articles included here.

By Group Impacted

• Race and Ethnicity
  • Black/African-American Learners in North America
  • Latino/Latina/Latinx/Hispanic Learners in North America
  • Asian/Asian-American Learners in North America
  • White Learners in North America
  • Indigenous Learners in North America
  • Research on Race and Ethnicity Conducted Outside of North America
• Gender: Male/Female
• Gender: Non-Binary and Transgender Learners
• Sexual Orientation
• Linguistic Origin
• National Origin or National Location
• International Students
• Native Language and Dialect
• Learners with Disabilities
• Age
• Urbanicity
• Parental Educational Background
• Socioeconomic Status
• Military-Connected Status
• Children of Migrant Workers
• Religion and Religious Background
• Public or Private K-12 School
• Intersectional Research

By Algorithm Application

• At-risk/Dropout/Stopout/Graduation Prediction
• Course Grade and GPA Prediction
• National and International Examination
• Short-term Performance and Learning Gains Prediction
• Automated Essay Scoring
• Speech Recognition for Education
• Other NLP Applications of Algorithms in Education
• Student Knowledge Modeling
• Engagement and Affect Detection
• Self-regulated Learning