Simulated students play a central role in training and evaluating AI tutor agents, yet most approaches rely on static personas or unconstrained generation and implicitly assume stationary behavior. Real student responses also depend on transient learner states such as affect, attention, and memory that evolve during interaction. We introduce DYSAS, a dynamic state-aware student-simulation framework with three tutoring-facing modules, Affect (A), Attention (T), and Memory (M). Their low, medium, and high levels form the discrete ATM27 state space. We operationalize this interface through Dynamic Learner States (DLS), a prefix-only representation inferred at each turn without future leakage and used to condition student generation. We evaluate static and dynamic variants on the Eedi benchmark across prompting, supervised fine-tuning, and preference optimization. No configuration wins every objective as each of correctness-state match, misconception behavior, social tone, lexical fidelity, and tutor-response compatibility favors different settings. We therefore present DYSAS as an audit mechanism within a multi-objective simulator-selection framework, not as a universally superior add-on.