Multimodal Large Language Models (MLLMs) hold significant promise for revolutionizing traditional education and reducing teachers’ workload. However, accurately interpreting unconstrained STEM student handwritten solutions with intertwined mathematical formulas, diagrams, and textual reasoning poses a significant challenge due to the lack of authentic and domain-specific benchmarks. Additionally, current evaluation paradigms predominantly rely on the outcomes of downstream tasks (e.g., auto-grading), which often probe only a subset of the recognized content, thereby failing to capture the MLLMs’ understanding of complex handwritten logic as a whole. To bridge this gap, we release EDU-CIRCUIT-HW, a dataset consisting of 1,300+ authentic student handwritten solutions from a university-level STEM course. Utilizing the expert-verified verbatim transcriptions and grading reports of student solutions, we simultaneously evaluate various MLLMs’ upstream recognition fidelity and downstream auto-grading performance. Our evaluation uncovers an astonishing scale of latent failures within MLLM-recognized student handwritten content, highlighting the models’ insufficient reliability for auto-grading and other understanding-oriented applications in high-stakes educational settings. In response, we present a case study demonstrating that leveraging identified error patterns to preemptively detect and rectify recognition errors, with only minimal human intervention (e.g., with 3.3% assignments routed to human graders while the rest to GPT-5.1 grader), can effectively enhance the robustness of the deployed AI-enabled grading system on unseen student solutions.

As mobile devices become central to modern life, elderly users often struggle with their complexity, leading to digital divide. This paper explores how the integration of Human-Computer Interaction (HCI) principles and Natural Language Processing (NLP) techniques can enhance the way elderly users learn to use smartphones. To demonstrate this approach, we present AgeMate, a prototype mobile agent designed to support seniors in acquiring smartphone skills more intuitively and effectively. Specifically, we investigate how personalizedfeedback generated by large language models (LLMs), appropriate granularity in instructional content, and mechanisms for preventing and correcting user errors can contribute to more adaptive and user-friendly learning experiences for elderly users. Rather than focusing solely on system performance, our study emphasizes the instructional value of NLP-enhanced interaction: enabling step-by-step, conversational teaching tailored to users’ real-time context. By analyzing usage patterns and interaction challenges, we propose design strategies that bridge the gap between accessibility and intelligent guidance to better support elderly users in digital environments.

Massive false rumors emerging along with breaking news or trending topics severely hinder the truth. Existing rumor detection approaches achieve promising performance on the yesterday’s news, since there is enough corpus collected from the same domain for model training. However, they are poor at detecting rumors about unforeseen events especially those propagated in minority languages due to the lack of training data and prior knowledge (i.e., low-resource regimes). In this paper, we propose an adversarial contrastive learning framework to detect rumors by adapting the features learned from well-resourced rumor data to that of the low-resourced. Our model explicitly overcomes the restriction of domain and/or language usage via language alignment and a novel supervised contrastive training paradigm. Moreover, we develop an adversarial augmentation mechanism to further enhance the robustness of low-resource rumor representation. Extensive experiments conducted on two low-resource datasets collected from real-world microblog platforms demonstrate that our framework achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.

Rumors are rampant in the era of social media. Conversation structures provide valuable clues to differentiate between real and fake claims. However, existing rumor detection methods are either limited to the strict relation of user responses or oversimplify the conversation structure. In this study, to substantially reinforces the interaction of user opinions while alleviating the negative impact imposed by irrelevant posts, we first represent the conversation thread as an undirected interaction graph. We then present a Claim-guided Hierarchical Graph Attention Network for rumor classification, which enhances the representation learning for responsive posts considering the entire social contexts and attends over the posts that can semantically infer the target claim. Extensive experiments on three Twitter datasets demonstrate that our rumor detection method achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.