Gender Bias in AI-Powered Educational Context: A Literature Review (Poster 1)

• In Event: Artificial Intelligence, Gender, Race, Culture, and Environmental Justice in Education: An Intersectional Analysis of Bias, Equity, and Sustainability Globally

Abstract

The widespread integration of artificial intelligence (AI) in educational context has largely reshaped pedagogical practices, student learning experiences, and institutional decision-making processes. While existing literature has extensively debated whether AI technologies amplify or alleviate entrenched gender inequalities, limited attention has been paid to the interplay between AI and gender bias within the educational field. Thus, this literature review firstly investigates how gender biases embedded in AI-driven educational technologies may perpetuate discriminatory outcomes by shaping students' academic trajectories, self-perceptions, and career aspirations. Second, this literature review evaluates AI's role in educational content curation and interrogates whether content generation algorithms reproduce historical gender stereotypes in curricular materials, career guidance interfaces, and competency assessments. Third, this literature review tried to address the ethical imperative for bias-mitigation strategies, proposing a multi-stakeholder framework encompassing algorithmic transparency protocols, intersectional dataset audits, and policy interventions.
The review result advocates for human-centered AI design paradigms that actively counteract structural gender inequities rather than passively mirror societal prejudices. The findings also underscore the urgency of reimagining educational AI through an equity lens, emphasizing educator-AI collaboration, student agency preservation, and proactive governance mechanisms to foster inclusive learning environments.

Authors

• Tianxiang Zhu, University of Rochester

• Jialin Yan, University of Rochester