Large language models (LLMs) have shown remarkable progress in various tasks, but how well do they understand how students learn... and how they make mistakes? A new research paper and dataset called "MalAlgoQA" explores this by testing LLMs' ability to identify not just correct reasoning, but also the flawed logic behind incorrect answers, something educators call 'malgorithms.' Think of it like multiple-choice questions where the LLM needs to figure out not only the right answer but also the specific misconception that led to each wrong choice. The results are revealing. While LLMs are pretty good at identifying correct reasoning paths (scoring up to 95% accuracy), they stumble significantly when it comes to understanding why students might choose the wrong answer, often scoring below 70%. This is a big deal for AI in education. If AI tutors can't reliably understand *how* a student arrives at an incorrect answer, they can’t provide truly effective feedback or address the underlying misconception. The research also found a surprising twist: getting LLMs to 'show their work' through chain-of-thought prompting didn't actually help and sometimes even made them worse at identifying malgorithms! This suggests that current LLMs are heavily biased toward correct reasoning and struggle to deviate from those learned patterns. The implications are far-reaching. While AI has huge potential to personalize learning and enhance education, this research highlights a key limitation: reliably modeling student misconceptions. MalAlgoQA offers a critical step toward creating AI tutors that not only teach but also diagnose and correct individual learning gaps. The challenge now is to develop more sophisticated models that can not only recognize correct answers but also understand how and why students make mistakes, paving the way for truly personalized and effective AI-powered educational tools.