🏷️ bias-mitigation
18 pages tagged with bias-mitigation(12 articles, 6 concepts)
🏷️ Assessment Validity in AI Education
> **Assessment validity** — whether assessments measure what they claim to measure. AI in education raises fundamental validity questions: do AI-graded assessments assess student learning or AI prompt…
🏷️ Automated Assessment
> **Automated assessment** — the use of AI to evaluate student work, from formative quizzes to high-stakes exams. Automated assessment spans multiple modalities — multiple-choice, short answer, essay,…
🏷️ Automated Grading
> **Automated grading** — AI systems that evaluate student work, from multiple-choice scoring to essay assessment and code review. Automated grading is one of the most mature and widely-deployed AI in…
🏷️ Equity in AI Education
> **Equity** — the principle that AI in education should serve all learners fairly, without exacerbating existing disparities. Equity research in the wiki examines access gaps, bias in AI systems, cul…
🏷️ Ethics in AI Education
> **Ethics** — the moral principles governing the design, deployment, and use of AI in educational contexts. AI education ethics spans data privacy, algorithmic fairness, transparency, accountability,…
📄 When Agents Learn to Be You: Benchmarking Privacy Leakage, Impersonation Risk, and Defenses in Persona Skills
> **When Agents Learn to Be You: Benchmarking Privacy Leakage, Impersonation Risk, and Defenses in Persona Skills** — Introduces AntiSkillBench with 7,500 persona-grounded dialogue traces from 50 beha…
📄 AI-based scoring systematically underestimates conceptual understanding of linguistically weak students' explanations in physics
> **Authors:** Markus S. Feser, Paul L. Tschisgale (Leibniz Institute for Science and Mathematics Education, Kiel, Germany)…
2026-07-31 · assessment-validity, automated-grading, equity, multilingual-learning, physics-education
📄 Data Annotations as Pedagogical Hints: From Subjective Labels to Critical Thinking
Machine learning courses typically hand students pre-labeled datasets, hiding the subjectivity baked into human annotation and cultivating an overly trusting view of AI data pipelines. This two-univer…
📄 The Paternalistic Filter: Epistemic Injustice and Differential Refusal in LLM-Mediated History Education for Marginalized Romanian Students
A systematic API audit of four LLMs acting as history tutors evaluates 1,800 responses about the 1989 Romanian Revolution, exposing a 'paternalistic filter': models differentially refuse or soften ans…
🏷️ Bias Mitigation
> **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 [[…
📄 Contaminated Collaboration: Measuring Gender Bias Transfer in LLM-Assisted Student Writing
> **Ariyan Hossain, Kazi Kamruzzaman Rabbi, Farig Sadeque, S M Taiabul Haque** (2026). arXiv cs.CL…
📄 Fair and explainable educational recommendations with a hybrid Graph-GRU framework
> **Synthesis:** 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
This paper argues that [[generative-ai]] systems in [[higher-ed]] are not epistemically neutral — they actively marginalize non-hegemonic ways of knowing. Drawing on educational sciences, critical tec…
📄 Artificial Intelligence in Lifelong Learning: Opportunities and Challenges in Adult Education Policy
Theodora and Tselios (2026) provide a policy-oriented synthesis of AI's dual role in adult and [[lifelong-learning]] contexts — as both an enabler of personalized, scalable education and a source of s…
📄 Explainable Artificial Intelligence in Education (XAI-ED)
📄 DOI: 10.1016/j.caeai.2022.100074 This paper introduces **XAI-ED**, a framework for explainable AI that is purpose-built for education. It argues that while XAI in education shares common ground wit…
📄 The Hidden Cost of Contextual Sycophancy: an AI Literacy Intervention in Human-AI Collaboration
LLM sycophancy creates a feedback loop where user errors propagate into AI advice, degrading outcomes; AI literacy training reduces but doesn't eliminate this contextual sycophantic dependence. This A…
📄 AI Tutor Safety and Pedagogical Harms
> Conventional LLM safety benchmarks focus on toxic outputs, jailbreaks, and bias. In education, the primary risks are quieter: > "Solving problems correctly and avoiding toxic language does not make …
📄 Educational LLM Alignment
> Hardy & Kim (2026) identify a **cascading proxy** problem in AI-for-education evaluation: > The gap between what LLMs are *capable* of and what actually *benefits learners* — benchmark performance, …