Student Misconception
Student misconceptions, encompassing inaccurate understandings of concepts across diverse fields like AI, mathematics, and security, are a significant focus of current research. Studies explore how these misconceptions manifest in various learning contexts and investigate the effectiveness of different pedagogical approaches, including adaptive teaching models and the use of large language models (LLMs) as both learners and tutors. Understanding and addressing these misconceptions is crucial for improving educational outcomes and ensuring the responsible development and application of technologies like AI, where flawed understanding can lead to inaccurate or biased results. This research highlights the need for robust evaluation methods and innovative strategies to effectively identify and correct student misunderstandings.
Papers
Novice Learner and Expert Tutor: Evaluating Math Reasoning Abilities of Large Language Models with Misconceptions
Naiming Liu, Shashank Sonkar, Zichao Wang, Simon Woodhead, Richard G. Baraniuk
Can Large Language Models Provide Security & Privacy Advice? Measuring the Ability of LLMs to Refute Misconceptions
Yufan Chen, Arjun Arunasalam, Z. Berkay Celik