Towards Inclusive Educational AI: Auditing Frontier LLMs for Cultural Biases through a Multiplexity Lens

As large language models (LLMs) like GPT-4 and Llama 3 become integral to educational contexts, concerns are mounting over the cultural biases, power imbalances, and ethical limitations embedded within these technologies. Though generative AI tools aim to enhance learning experiences, they often reflect values rooted in Western, Educated, Industrialized, Rich, and Democratic (WEIRD) cultural paradigms, potentially sidelining diverse global perspectives. This paper proposes a framework to assess and mitigate cultural bias within LLMs through applied multiplexity. Multiplexity, inspired by Senturk et al. and rooted in Islamic and other wisdom traditions, emphasizes the coexistence of diverse cultural viewpoints, supporting a multilayered epistemology that integrates empirical sciences and normative values. Our analysis reveals that LLMs frequently exhibit cultural polarization in both overt responses and subtle contextual cues. To address these biases, we propose two strategies: Contextually-Implemented Multiplex LLMs, which embed multiplex principles directly into the system prompt, and Multi-Agent System (MAS)-Implemented Multiplex LLMs, where multiple LLM agents representing distinct cultural viewpoints collaboratively generate balanced responses. Our findings demonstrate that as mitigation strategies evolve from contextual prompting to MAS-implementation, cultural inclusivity markedly improves, evidenced by a significant rise in the Perspectives Distribution Score (PDS) and a PDS Entropy increase from 3.25% at baseline to 98% with the MAS-Implemented Multiplex LLMs. Sentiment analysis shows a shift towards positive sentiment across cultures, with the MAS-Implemented Multiplex LLMs achieving 0% negative sentiment. This study establishes a baseline for assessing and fostering cultural inclusivity in educational AI, laying the groundwork for a globally pluralistic approach that respects diverse cultural perspectives.