Text2Tradition: From Epistemological Tensions to AI-Mediated Cross-Cultural Co-Creation

TL;DR

This paper presents Text2Tradition, an AI system that translates user prompts into traditional Thai dance sequences, aiming to bridge epistemological gaps between modern language models and cultural dance knowledge.

Contribution

It introduces a novel system that mediates between data-driven AI and traditional cultural knowledge, addressing epistemological tensions in cross-cultural art creation.

Findings

Demonstrates potential for AI to facilitate traditional dance creation

Highlights epistemological challenges in cross-cultural AI applications

Proposes a framework for integrating traditional knowledge with language models

Abstract

This paper introduces Text2Tradition, a system designed to bridge the epistemological gap between modern language processing and traditional dance knowledge by translating user-generated prompts into Thai classical dance sequences. Our approach focuses on six traditional choreographic elements from No. 60 in Mae Bot Yai, a revered Thai dance repertoire, which embodies culturally specific knowledge passed down through generations. In contrast, large language models (LLMs) represent a different form of knowledge--data-driven, statistically derived, and often Western-centric. This research explores the potential of AI-mediated systems to connect traditional and contemporary art forms, highlighting the epistemological tensions and opportunities in cross-cultural translation.