This paper argues that evaluation of educational LLMs should move beyond decontextualized task-based benchmarks toward situated benchmarks centered on pedagogical action. Prior work in human-computer interaction and the learning sciences has long emphasized situated use, user context, and interaction processes; our focus is narrower and complementary: how these concerns should reshape benchmark design for educational LLMs. Existing evaluations already cover question generation, knowledge answering, error diagnosis, feedback generation, and teaching analysis, but their dominant unit remains the single input-output episode, making it difficult to compare whether a model genuinely supports learning progress, instructional judgment, or subsequent revision in context. Educational activity is processual, context-dependent, and development-oriented, so explanations, feedback, and hints can only be evaluated properly in relation to learner state, interaction history, and pedagogical goals. We review a landscape still dominated by task-based paradigms and argue, from educational conceptions, educational values, and research methodology, that next-generation educational benchmarks should make pedagogical function in context a core target. We further propose layered, scalable, and benchmarkable situated protocols built through simulation-first development and follow-up empirical calibration, extending evaluation beyond task quality to contextual fit and downstream consequences for learning and teaching decisions.

Simulating Professions (SP) enables Large Language Models (LLMs) to emulate professional roles. However, comprehensive psychological and ethical evaluation in these contexts remains lacking. This paper introduces EMNLP, an Educator-role Moral and Normative LLMs Profiling framework for personality profiling, moral development stage measurement, and ethical risk under soft prompt injection. EMNLP extends existing scales and constructs 88 teacher-specific moral dilemmas, enabling profession-oriented comparison with human teachers. A targeted soft prompt injection set evaluates compliance and vulnerability in teacher SP. Experiments on 14 LLMs show teacher-role LLMs exhibit more idealized and polarized personalities than human teachers, excel in abstract moral reasoning, but struggle with emotionally complex situations. Models with stronger reasoning are more vulnerable to harmful prompt injection, revealing a paradox between capability and safety. The model temperature and other hyperparameters have limited influence except in some risk behaviors. This paper presents the first benchmark to assess ethical and psychological alignment of teacher-role LLMs for educational AI. Resources are available at https://e-m-n-l-p.github.io/.