Bias mitigation in educational AI requires auditing models across the pipeline: gender-bias-transfer-llm-writing, ai-scoring-language-bias-physics, llm-cultural-relevance-k12, and inclusive-ai (merged into equity) document bias sources and mitigation strategies from data curation to prompt design.
Bias mitigation in AIED concerns identifying and reducing unfair, identity-patterned behavior in AI tutors and educational systems (e.g., differential refusals or softening of answers for marginalized students). Surfaced by recent auditing work on LLM history tutors.
Related Pages
- paternalistic-filter-llm-history-education — Epistemic injustice via identity-patterned LLM refusals
- equity — Structural fairness in AI education
- ai-tutor-safety-harms — Catalogue of tutor harms
- data-annotations-pedagogical-hints — Data Annotations as Pedagogical Hints: From Subjective Labels to Critical Thinki
Citation
APA: (stub). Bias Mitigation. AI Ed Wiki.📎 10 other pages tagged bias-mitigation
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- Artificial Intelligence in Lifelong Learning: Opportunities and Challenges in Adult Education Policy
- Contaminated Collaboration: Measuring Gender Bias Transfer in LLM-Assisted Student Writing
- Educational LLM Alignment
- Explainable Artificial Intelligence in Education (XAI-ED)
- Fair and explainable educational recommendations with a hybrid Graph-GRU framework
- Generative AI and the marginalization of minoritized knowledges in higher education: the case of disability
- The Hidden Cost of Contextual Sycophancy: an AI Literacy Intervention in Human-AI Collaboration
- The Paternalistic Filter: Epistemic Injustice and Differential Refusal in LLM-Mediated History Education for Marginalized Romanian Students