Understanding Gen Alpha Digital Language: Evaluation of LLM Safety Systems for Content Moderation
Manisha Mehta, Fausto Giunchiglia

TL;DR
This study evaluates how well current AI moderation tools understand Gen Alpha's unique digital language, revealing significant gaps that threaten online safety and highlighting the need for tailored safety systems for youth.
Contribution
It introduces a novel dataset of Gen Alpha expressions and a framework for enhancing AI safety tools to better protect young users online.
Findings
AI models struggle to detect masked harassment in Gen Alpha language
Current safety systems have critical comprehension failures
Gen Alpha's linguistic divergence increases youth vulnerability
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsLLaMA
