A study of the impact of AI-generated feedback and AI-provided guidance on concept development in science learning and on student attitude towards the use of AI in education

  • Chu, Hye Eun (Primary Chief Investigator)
  • Ponton, Fleur (Primary Chief Investigator)
  • Jho, Hunkoog (Chief Investigator)
  • Martin, Sonya (Chief Investigator)
  • Ha, Minsu (Chief Investigator)
  • Faisal (Chief Investigator)

Project: Research

Project Details

Description

Funded by MQ's Data Horizons Research Centre with a grant of $5,000, this project aims to investigate the effectiveness of AI-generated feedback in enhancing students' conceptual understanding in science learning. Utilizing a large sample of over 1,000 students from Australia, Korea, and Indonesia, the study will validate AI feedback and determine the most suitable large language model (LLM) for improving science education outcomes.

Key findings

The key findings explore the effectiveness of AI-generated feedback in science education. They assess how well AI improves concept development, aligns with human feedback, identifies the best large language model for tracking student progress, and gauges student attitudes towards AI feedback. These insights will help refine AI's role in teaching and assessment.
StatusFinished
Effective start/end date1/07/24 → 30/11/24