Potential Abstract:
Recent advancements in artificial intelligence (AI) and machine learning have raised concerns about the potential reinforcement of stereotypes in educational settings. This study explores the concept of flexible stereotypes within AI systems and the role of collective intelligence in mitigating bias. By examining the intersection of AI, machine learning, and educational practices, we aim to uncover strategies for promoting diversity and inclusivity in technology-mediated learning environments. Drawing on theories of stereotype threat and social cognition, we investigate how AI algorithms can adapt and evolve to challenge traditional stereotypes through collective intelligence mechanisms. Through a mixed-methods approach, including content analysis of educational datasets and interviews with key stakeholders, we seek to identify patterns of bias in AI systems and develop interventions to foster more equitable outcomes for learners from diverse backgrounds. Our findings highlight the importance of integrating human feedback and diverse perspectives into AI design processes to enhance the adaptability and fairness of educational technologies. This research contributes to the ongoing dialogue on the ethical implications of AI in education and offers practical recommendations for educators, policymakers, and technology developers to create more inclusive learning environments.
Potential References:
- Is technology gender neutral? A systematic literature review on gender stereotypes attached to artificial intelligence
- A gendered perspective on artificial intelligence
- Performance and Flexibility of Stereotype-based User Models
- The algorithmic divide and equality in the age of artificial intelligence
- Robots enact malignant stereotypes
