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Evaluation of a question generation approach using semantic web for supporting argumentation.

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This study shows computer-generated questions, using WordNet, can effectively stimulate student discussion and argument development in science education. These AI-generated questions are comparable to human experts in relevance and usefulness.

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Area of Science:

  • Educational Technology
  • Natural Language Processing
  • Science Education

Background:

  • Discourse and argumentation are vital for learning in social and science domains.
  • Teachers face challenges in facilitating student discussions and argument development.
  • Existing methods may not sufficiently support students in articulating their ideas.

Purpose of the Study:

  • To investigate the use of WordNet for generating questions to stimulate student brainstorming.
  • To evaluate the understandability, relevance, and usefulness of system-generated questions in supporting argument development.
  • To compare AI-generated questions with those created by human experts.

Main Methods:

  • Utilized WordNet as a semantic resource to automatically generate discussion-stimulating questions.
  • Conducted an evaluation study comparing 141 human-generated questions with 44 system-generated questions.
  • Assessed system-generated questions on understandability, relevance, and predicted usefulness for argument development.

Main Results:

  • System-generated questions were indistinguishable from human-generated questions in two out of three discussion contexts.
  • Relevant system-generated questions showed a moderate correlation with usefulness for developing new arguments in two contexts.
  • Understandable system-generated questions were rated as useful in one specific discussion context.

Conclusions:

  • AI-powered question generation using WordNet shows promise in supporting science education discussions.
  • System-generated questions can be a valuable tool for enhancing student engagement and argument formulation.
  • Further research can refine AI models to consistently produce highly effective discussion prompts.