The Evolution of Gen Alpha Slang: Linguistic Patterns and AI Translation Challenges

Ishita, Radhika Mamidi

Keywords: Gen Alpha slang, AI translation challenges, lexical compression, semantic shifts, cultural hybridization, gaming lexicon, meme culture, mental health vocabulary, dynamic lexicon updating, context-aware frameworks, multimodal analysis, linguistic evolution, digital-native communication

TL;DR: This paper analyzes Gen Alpha slang's evolution, its challenges for AI translation and proposes solutions for culturally adaptive language models.

Abstract: Generation Alpha (born 2010-2024) is the first generation fully raised within the digital ecosystem. They exhibit unique linguistic behaviours influenced by rampant online communication and platform-specific cultures. This study examines the rapid evolution of Gen Alpha slang through a comparative analysis of Millennial and Gen Z vernacular. We identify three core linguistic patterns: extreme lexical compression, digital culture-driven semantic shifts and part-of-speech conversion. We construct a comprehensive slang corpus sourced from online platforms and evaluate the performance of four AI translation systems (viz. Google Translate, ChatGPT 4, Gemini 1.0, DeepSeek v3) on over 100 slang terms. Our results reveal significant translation challenges rooted in culturally-bound terms from gaming, meme culture, and mental health discourse. Most errors are the result of inadequate cultural contextualization, with literal translations dominating the error patterns. Our findings highlight the critical limitations in current language models and emphasize the need for adaptive, culturally sensitive and context-aware frameworks that can handle the dynamic lexicon of evolving youth vernacular.

Archival Status: Archival

Paper Length: Long Paper (up to 8 pages of content)

Submission Number: 152