The conversational capabilities of large language models hold significant promise for enabling scalable and interactive tutoring. Prior work has primarily evaluated general instructional competence while overlooking a critical dimension: the ability to adaptively guide learners based on their evolving cognitive states. This study takes instructional guidance as a core capability and asks whether LLMs can emulate expert tutors who dynamically adjust strategies in response to learners’ needs. We introduce GuideEval, a benchmark grounded in authentic educational dialogues that evaluates pedagogical guidance through a three-phase behavioral framework: (i) Assessment, inferring learner states; (ii) Orchestration, adapting instructional strategies; and (iii) Elicitation, stimulating proper reflections. Empirical results indicate that existing LLMs often fail to provide effective adaptive scaffolding when learners experience confusion or require redirection. Complementing quantitative results, we present a detailed failure case analysis, offering intuitive insights into these limitations. We further propose a behavior-guided finetuning strategy based on behavior-prompted instructional dialogues, substantially enhancing guidance performance. By shifting the focus from isolated content correctness to learner-centered state-aware interaction, this work advocates a more dialogic paradigm for evaluating Socratic LLMs.