Date
Publisher
arXiv
Online learning has experienced rapid growth due to its flexibility and
accessibility. Personalization, adapted to the needs of individual learners, is
crucial for enhancing the learning experience, particularly in online settings.
A key aspect of personalization is providing learners with answers customized
to their specific questions. This paper therefore explores the potential of
Large Language Models (LLMs) to generate personalized answers to learners'
questions, thereby enhancing engagement and reducing the workload on educators.
To evaluate the effectiveness of LLMs in this context, we conducted a
comprehensive study using the StackExchange platform in two distinct areas:
language learning and programming. We developed a framework and a dataset for
validating automatically generated personalized answers. Subsequently, we
generated personalized answers using different strategies, including 0-shot,
1-shot, and few-shot scenarios. The generated answers were evaluated using
three methods: 1. BERTScore, 2. LLM evaluation, and 3. human evaluation. Our
findings indicated that providing LLMs with examples of desired answers (from
the learner or similar learners) can significantly enhance the LLMs' ability to
tailor responses to individual learners' needs.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
