Date
Publisher
arXiv
Recent studies have integrated large language models (LLMs) into diverse
educational contexts, including providing adaptive programming hints, a type of
feedback focuses on helping students move forward during problem-solving.
However, most existing LLM-based hint systems are limited to one single hint
type. To investigate whether and how different levels of hints can support
students' problem-solving and learning, we conducted a think-aloud study with
12 novices using the LLM Hint Factory, a system providing four levels of hints
from general natural language guidance to concrete code assistance, varying in
format and granularity. We discovered that high-level natural language hints
alone can be helpless or even misleading, especially when addressing next-step
or syntax-related help requests. Adding lower-level hints, like code examples
with in-line comments, can better support students. The findings open up future
work on customizing help responses from content, format, and granularity levels
to accurately identify and meet students' learning needs.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
