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Understanding Gen Alpha Digital Language: Evaluation of LLM Safety Systems for Content Moderation

Manisha Mehta, Fausto Giunchiglia

TL;DR

This study addresses Gen Alpha's unique digital language and online safety by constructing a dataset of 100 contemporary expressions and evaluating four leading LLMs (GPT-4, Claude, Gemini, Llama 3) alongside human moderators and Gen Alpha users. Using a three-dimensional evaluation framework (basic understanding, context recognition, safety recognition), the research reveals that Gen Alpha users possess near-native comprehension of their own expressions, while parents, professional moderators, and AI systems exhibit significant gaps in detecting context-dependent harm and evolution-based risks. The analysis identifies critical failure patterns in safety detection, highlights platform-specific and evolution-driven challenges, and introduces a vulnerability framework that links risk categories to moderation gaps and help-seeking barriers. The work advocates for youth-centered design, regular bias audits, and human-AI collaboration to enhance content moderation without overly constraining authentic youth expression, with implications for policy, platform accountability, and future research in youth online safety.

Abstract

This research offers a unique evaluation of how AI systems interpret the digital language of Generation Alpha (Gen Alpha, born 2010-2024). As the first cohort raised alongside AI, Gen Alpha faces new forms of online risk due to immersive digital engagement and a growing mismatch between their evolving communication and existing safety tools. Their distinct language, shaped by gaming, memes, and AI-driven trends, often conceals harmful interactions from both human moderators and automated systems. We assess four leading AI models (GPT-4, Claude, Gemini, and Llama 3) on their ability to detect masked harassment and manipulation within Gen Alpha discourse. Using a dataset of 100 recent expressions from gaming platforms, social media, and video content, the study reveals critical comprehension failures with direct implications for online safety. This work contributes: (1) a first-of-its-kind dataset capturing Gen Alpha expressions; (2) a framework to improve AI moderation systems for youth protection; (3) a multi-perspective evaluation including AI systems, human moderators, and parents, with direct input from Gen Alpha co-researchers; and (4) an analysis of how linguistic divergence increases youth vulnerability. Findings highlight the urgent need to redesign safety systems attuned to youth communication, especially given Gen Alpha reluctance to seek help when adults fail to understand their digital world. This study combines the insight of a Gen Alpha researcher with systematic academic analysis to address critical digital safety challenges.

Understanding Gen Alpha Digital Language: Evaluation of LLM Safety Systems for Content Moderation

TL;DR

This study addresses Gen Alpha's unique digital language and online safety by constructing a dataset of 100 contemporary expressions and evaluating four leading LLMs (GPT-4, Claude, Gemini, Llama 3) alongside human moderators and Gen Alpha users. Using a three-dimensional evaluation framework (basic understanding, context recognition, safety recognition), the research reveals that Gen Alpha users possess near-native comprehension of their own expressions, while parents, professional moderators, and AI systems exhibit significant gaps in detecting context-dependent harm and evolution-based risks. The analysis identifies critical failure patterns in safety detection, highlights platform-specific and evolution-driven challenges, and introduces a vulnerability framework that links risk categories to moderation gaps and help-seeking barriers. The work advocates for youth-centered design, regular bias audits, and human-AI collaboration to enhance content moderation without overly constraining authentic youth expression, with implications for policy, platform accountability, and future research in youth online safety.

Abstract

This research offers a unique evaluation of how AI systems interpret the digital language of Generation Alpha (Gen Alpha, born 2010-2024). As the first cohort raised alongside AI, Gen Alpha faces new forms of online risk due to immersive digital engagement and a growing mismatch between their evolving communication and existing safety tools. Their distinct language, shaped by gaming, memes, and AI-driven trends, often conceals harmful interactions from both human moderators and automated systems. We assess four leading AI models (GPT-4, Claude, Gemini, and Llama 3) on their ability to detect masked harassment and manipulation within Gen Alpha discourse. Using a dataset of 100 recent expressions from gaming platforms, social media, and video content, the study reveals critical comprehension failures with direct implications for online safety. This work contributes: (1) a first-of-its-kind dataset capturing Gen Alpha expressions; (2) a framework to improve AI moderation systems for youth protection; (3) a multi-perspective evaluation including AI systems, human moderators, and parents, with direct input from Gen Alpha co-researchers; and (4) an analysis of how linguistic divergence increases youth vulnerability. Findings highlight the urgent need to redesign safety systems attuned to youth communication, especially given Gen Alpha reluctance to seek help when adults fail to understand their digital world. This study combines the insight of a Gen Alpha researcher with systematic academic analysis to address critical digital safety challenges.
Paper Structure (28 sections, 1 figure, 14 tables)

This paper contains 28 sections, 1 figure, 14 tables.

Table of Contents

  1. Introduction
  2. Relevant Work
  3. Methodology
  4. Research Design
  5. Data Collection
  6. Expression Analysis
  7. LLM Evaluation
  8. Human Evaluation
  9. Research Focus
  10. Dataset Composition
  11. Evaluation Methodology
  12. Evaluation Framework
  13. Testing Protocol and Implementation
  14. Evaluation Results
  15. Comparative Performance Evaluation
  16. ...and 13 more sections

Figures (1)

  • Figure 1: Framework for analyzing Gen Alpha digital communication vulnerabilities. The diagram shows the relationship between different types of risks, protection systems, and resulting moderation gaps.