Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education?
Project Overview
The document examines the transformative potential of Artificial Intelligence in Education (AIEd) to mitigate inequities within educational systems, while also cautioning against the risks of exacerbating existing disparities. It underscores the critical importance of fairness, accountability, and transparency in the implementation of AIEd technologies. To explore the dual role of AIEd in either promoting or hindering equity, the authors introduce four analytical lenses: the design of socio-technical systems, the use of data, the functioning of algorithms, and the dynamics between automated and human decision-making. Through these lenses, the document outlines pathways for creating more equitable AIEd solutions and stresses the necessity of engaging a diverse range of stakeholders in the design process. The findings suggest that with careful consideration and inclusive practices, generative AI can be harnessed effectively to support educational equity and improve outcomes for all learners.
Key Applications
AIEd systems for promoting educational equity
Context: K-12 education, addressing achievement gaps among different learner groups
Implementation: Developing AIEd systems that scale benefits of one-on-one tutoring and fill educational service gaps.
Outcomes: Potential reduction of achievement gaps, broader access to educational resources.
Challenges: Risk of perpetuating existing biases and inequities if not designed thoughtfully.
Implementation Barriers
Design-related
Inequities arising from the socio-technical system design, including disparities in access to technology and the interactions between AIEd systems and human decision-makers that can perpetuate biases.
Proposed Solutions: Invest in equity-focused design processes and toolkits for AIEd practitioners, and design systems that support critical reflection and awareness of biases among educators.
Data-related
Historical biases in datasets used to train AIEd systems perpetuating existing inequities.
Proposed Solutions: Develop datasets that reflect diverse educational contexts and prioritize equity.
Algorithmic
Algorithms may amplify biases present in training data, leading to inequitable outcomes.
Proposed Solutions: Design algorithms to counteract biases and ensure equitable decision-making.
Project Team
Kenneth Holstein
Researcher
Shayan Doroudi
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Kenneth Holstein, Shayan Doroudi
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai