Large Language Models for Simulating Professions (SP-LLMs), particularly as teachers, are pivotal for personalized education. However, ensuring their professional competence and ethical safety remains a major challenge, as existing benchmarks fail to measure role-playing fidelity or address the unique teaching harms inherent in educational scenarios. To address this gap, we propose EduGuardBench, a dual-component benchmark that evaluates professional fidelity through the Role-playing Fidelity Score (RFS) and diagnoses harms specific to the teaching profession. It also probes safety vulnerabilities using persona-based adversarial prompts targeting both general harms and academic misconduct, with metrics such as Attack Success Rate (ASR) and a three-tier Refusal Quality assessment. Extensive experiments on 14 leading models reveal a stark polarization in performance. While reasoning-oriented models generally demonstrate higher fidelity, incompetence remains the dominant failure mode across most models. Adversarial testing uncovered a counterintuitive scaling paradox, where mid-sized models appear more vulnerable, challenging monotonic safety assumptions. Notably, we identify an Educational Transformation Effect, where the safest models convert harmful requests into teachable moments through ideal educational refusals. This ability is strongly negatively correlated with ASR, revealing a new dimension of advanced AI safety. EduGuardBench thus provides a reproducible framework for holistic assessment of professional, ethical, and pedagogical alignment, uncovering dynamics critical to deploying trustworthy AI in education.

Can Multimodal Large Language Models (MLLMs), with capabilities in perception, recognition, understanding, and reasoning, function as independent assistants in art evaluation dialogues? Current MLLM evaluation methods, which rely on subjective human scoring or costly interviews, lack comprehensive coverage of various scenarios. This paper proposes a process-oriented Human-Computer Interaction (HCI) space design to facilitate more accurate MLLM assessment and development. This approach aids teachers in efficient art evaluation while also recording interactions for MLLM capability assessment. We introduce ArtMentor, a comprehensive space that integrates a dataset and three systems to optimize MLLM evaluation. The dataset consists of 380 sessions conducted by five art teachers across nine critical dimensions. The modular system includes agents for entity recognition, review generation, and suggestion generation, enabling iterative upgrades. Machine learning and natural language processing techniques ensure the reliability of evaluations. The results confirm GPT-4o's effectiveness in assisting teachers in art evaluation dialogues. Our contributions are available at https://artmentor.github.io/.

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/.

Graph Neural Networks (GNN) have emerged as a powerful tool in recommendation systems due to their ability to adeptly model complex relational data. Despite their potential, existing GNN-based approaches often fail to fully harness the synergistic benefits of integrating social networks and knowledge graphs into the recommendation process, over-looking the nuanced differences between these data sources. To address these gaps, we propose a novel Integrating Social and Knowledge Graphs (ISKG) framework tailored for GNN-based Recommender Systems. The ISKG model amalgamates user-item interactions, social connections, and knowledge graph insights into a unified representation, enhancing the recommendation quality through a multi-faceted approach. It starts with generating initial embeddings, progresses through a fusion layer for feature amalgamation, and refines these features in successive propagation layers. An innovative Adaptive Weighting Mechanism dynamically balances the influence of social and knowledge graph-enhanced features, leading to a Prediction Layer that finalizes the recommendations. Our comprehensive evaluation showcases ISKG’s superiority over conventional baselines, high-lighting its ability to achieve an effective balance between social and knowledge-based recommendations, thus paving the way for more accurate and nuanced recommendation systems. The project details are available at https://yuzengyi.github.io/ISKG/.