Large language models (LLMs) are increasingly embedded in AI-based tutoring systems. Can they faithfully model novice reasoning and metacognitive judgments? Existing evaluations emphasize problem-solving accuracy, overlooking the fragmented and imperfect reasoning that characterizes human learning. We evaluate LLMs as novices using 630 think-aloud utterances from multi-step chemistry tutoring problems with problem-solving logs of student hint use, attempts, and problem context. We compare LLM-generated reasoning to human learner utterances under minimal and extended contextual prompting, and assess the models' ability to predict step-level learner success. Although GPT-4.1 generates fluent and contextually appropriate continuations, its reasoning is systematically over-coherent, verbose, and less variable than human think-alouds. These effects intensify with a richer problem-solving context during prompting. Learner performance was consistently overestimated. These findings highlight epistemic limitations of simulating learning with LLMs. We attribute these limitations to LLM training data, including expert-like solutions devoid of expressions of affect and working memory constraints during problem solving. Our evaluation framework can guide future design of adaptive systems that more faithfully support novice learning and self-regulation using generative artificial intelligence.

翻译：大型语言模型（LLMs）正日益嵌入基于人工智能的辅导系统。它们能否准确模拟新手推理与元认知判断？现有评估侧重于问题解决准确性，却忽视了人类学习过程中碎片化且不完美的推理特征。本研究通过多步骤化学辅导问题中的630条出声思考话语（包含学生提示使用、尝试记录及问题情境的解题日志）来评估LLMs作为新手的表现。我们在最小化与扩展情境提示条件下，比较LLM生成推理与人类学习者话语，并评估模型预测学习者分步成功的能力。尽管GPT-4.1能生成流畅且情境恰当的延续内容，但其推理存在系统性过度连贯、冗长且变异性低于人类出声思考的特征。这些效应在提示阶段提供更丰富解题情境时会进一步增强。模型对学习者表现的预估持续偏高。这些发现凸显了使用LLMs模拟学习过程的认识论局限。我们将此归因于LLM训练数据的特点——包含类专家解决方案，却缺乏问题解决过程中的情感表达与工作记忆约束。本评估框架可为未来设计自适应系统提供指引，助力生成式人工智能更真实地支持新手学习与自我调节。