Toward Inclusive Educational AI: Auditing Frontier LLMs through a Multiplexity Lens
Project Overview
The document explores the role of generative AI, particularly Large Language Models (LLMs), in education, emphasizing the need to address cultural biases and ethical concerns associated with these technologies. It introduces a framework centered on multiplexity to foster cultural inclusivity in educational applications of AI. Two primary strategies are proposed: Contextually-Implemented Multiplex LLMs, which adapt to specific educational contexts, and Multi-Agent System (MAS)-Implemented Multiplex LLMs, which utilize multiple agents to represent diverse cultural perspectives. The findings indicate that employing these strategies leads to notable enhancements in cultural inclusivity and positive sentiment towards various cultures among learners. Overall, the document highlights the potential of generative AI in education to create more equitable and representative learning environments while acknowledging the importance of ethical considerations in its implementation.
Key Applications
Multiplex LLMs for Diverse Cultural Representation
Context: Educational settings targeting diverse learners globally, utilizing AI to incorporate multiple cultural perspectives into responses.
Implementation: Employing contextually-implemented and Multi-Agent System (MAS) approaches with multiplex principles where agents representing different cultural viewpoints collaboratively generate and synthesize responses, enhancing inclusivity and diversity.
Outcomes: Achieved a PDS Entropy of 98%, indicating a balanced representation of cultural perspectives, with positive sentiment shifts and a notable increase in cultural diversity reflected in outputs.
Challenges: Challenges in fully integrating diverse cultural perspectives without extensive training or adaptation, along with complexity in coordination among multiple agents and ensuring consistent quality of responses.
Implementation Barriers
Cultural and Ethical Considerations
LLMs often reflect Western-centric biases and may overlook ethical and cultural diversity, marginalizing global perspectives.
Proposed Solutions: Implementing frameworks like multiplexity to embed diverse cultural viewpoints into the model's outputs and creating culturally aligned educational AI frameworks to assess and incorporate diverse ethical values.
Project Team
Abdullah Mushtaq
Researcher
Muhammad Rafay Naeem
Researcher
Muhammad Imran Taj
Researcher
Ibrahim Ghaznavi
Researcher
Junaid Qadir
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Abdullah Mushtaq, Muhammad Rafay Naeem, Muhammad Imran Taj, Ibrahim Ghaznavi, Junaid Qadir
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18