Experts' View on Challenges and Needs for Fairness in Artificial Intelligence for Education

TL;DR

This paper presents a systematic investigation into experts' perspectives on the challenges and needs for ensuring fairness in AI-driven educational systems, highlighting key issues and future directions.

Contribution

It provides the first expert-driven analysis of fairness challenges in AI for education through surveys and interviews, identifying common concerns and research gaps.

Findings

Experts recognize fairness as a critical challenge in AI for education.

Diverging views exist on how to effectively address fairness issues.

The study highlights key research directions to improve fairness in educational AI systems.

Abstract

In recent years, there has been a stimulating discussion on how artificial intelligence (AI) can support the science and engineering of intelligent educational applications. Many studies in the field are proposing actionable data mining pipelines and machine-learning models driven by learning-related data. The potential of these pipelines and models to amplify unfairness for certain categories of students is however receiving increasing attention. If AI applications are to have a positive impact on education, it is crucial that their design considers fairness at every step. Through anonymous surveys and interviews with experts (researchers and practitioners) who have published their research at top-tier educational conferences in the last year, we conducted the first expert-driven systematic investigation on the challenges and needs for addressing fairness throughout the development of educational systems based on AI. We identified common and diverging views about the challenges and the needs faced by educational technologies experts in practice, that lead the community to have a clear understanding on the main questions raising doubts in this topic. Based on these findings, we highlighted directions that will facilitate the ongoing research towards fairer AI for education.