Designing Answer-Aware LLM Hints to Scaffold Deeper Learning in K-12 Programming Education

Abstract

Many K–12 students struggle with programming concepts. While LLMs offer scalable, timely support, overly direct answers can reduce reasoning and engagement [8], prompting the question: How can LLMs support learning without encouraging overreliance? In our study with 105 students, 31.4% showed misconceptions about variable assignment and data types, and in another survey, only 20% correctly solved conditional problems. This highlights the need for scaffolding to address conceptual gaps in K–12 programming. To address these gaps, we designed an answer-aware hint generation system using LLMs to support learning without reducing cognitive demand.We developed the system for CodeKids—an opensource, curriculum-aligned platform built with Virginia Tech and local public schools. It helps students practice grade-level programming through interactive activities, using LLM-generated hints to guide thinking without revealing answers [1, 11]. Based on Vygotsky’s Zone of Proximal Development [12], our approach balances support and autonomy through structured prompting that preserves productive struggle.