Potential Abstract:
Large language models (LLMs) have gained widespread attention in the field of education for their potential to enhance learning experiences and improve educational outcomes. However, the deployment of these sophisticated AI systems also raises concerns about perpetuating and exacerbating existing inequities in the educational landscape. This study critically examines the mediated inequities that arise from the uncritical adoption of LLMs in educational settings, particularly focusing on the naive regime surrounding their implementation. By “naive regime,” we refer to the inherent assumptions, biases, and oversights that accompany the utilization of LLMs without a thorough understanding of their implications and potential consequences.
Through a comprehensive review of existing literature and case studies, this research sheds light on how the uncritical use of LLMs in education can reinforce systemic inequities, widen the digital divide, and perpetuate biases across various demographic groups. By unpacking the complex interplay between LLMs, educational practices, and equity considerations, this study aims to provide a nuanced understanding of the challenges and opportunities associated with the integration of AI technologies in education.
Potential References:
- Health inequities, bias, and artificial intelligence
- On the dangers of stochastic parrots: Can language models be too big?🦜
- AI and the Increase of Productivity and Labor Inequality in Latin America: Potential Impact of Large Language Models on Latin American Workforce
- Causal inference, mediation analysis and racial inequities
- Gender, race, and intersectional bias in resume screening via language model retrieval
