Automatic sign language translation has gained particular interest in the computer vision and computational linguistics communities in recent years. Given each sign language country particularities, machine translation requires local data to develop new techniques and adapt existing ones. This work presents iLSU T, an open dataset of interpreted Uruguayan Sign Language RGB videos with audio and text transcriptions. This type of multimodal and curated data is paramount for developing novel approaches to understand or generate tools for sign language processing. iLSU T comprises more than 185 hours of interpreted sign language videos from public TV broadcasting. It covers diverse topics and includes the participation of 18 professional interpreters of sign language. A series of experiments using three state of the art translation algorithms is presented. The aim is to establish a baseline for this dataset and evaluate its usefulness and the proposed pipeline for data processing. The experiments highlight the need for more localized datasets for sign language translation and understanding, which are critical for developing novel tools to improve accessibility and inclusion of all individuals. Our data and code can be accessed.

As AI systems shape individual and societal decisions, fostering critical AI literacy is essential. Traditional approaches--such as blog articles, static lessons, and social media discussions--often fail to support deep conceptual understanding and critical engagement. This study examines whether interactive simulations can help learners "think like a scientist" by engaging them in hypothesis testing, experimentation, and direct observation of AI behavior. In a controlled study with 605 participants, we assess how interactive AI tutorials impact learning of key concepts such as fairness, dataset representativeness, and bias in language models. Results show that interactive simulations effectively enhance AI literacy across topics, supporting greater knowledge transfer and self-reported confidence, though engagement alone does not predict learning. This work contributes to the growing field of AI literacy education, highlighting how interactive, inquiry-driven methodologies can better equip individuals to critically engage with AI in their daily lives.

In human society, trust is an essential component of social attitude that helps build and maintain long-term, healthy relationships which creates a strong foundation for cooperation, enabling individuals to work together effectively and achieve shared goals. As many human interactions occur through electronic means such as using mobile apps, the potential arises for AI systems to assist users in understanding the social state of their relationships. In this paper we investigate the ability of Large Language Models (LLMs) to reason about trust between two individuals in an environment which requires fostering trust relationships. We also assess whether LLMs are capable of inducing trust by role-playing one party in a trust based interaction and planning actions which can instil trust.

As generative AI (Gen-AI) tools become more prevalent in education, there is a growing need to understand how educators, not just students, can actively shape their design and use. This study investigates how two instructors integrated four custom GPT tools into a Masters-level Qualitative Research Methods course for Urban Planning Policy students. Addressing two key gaps: the dominant framing of students as passive AI users, and the limited use of AI in qualitative methods education. The study explores how Gen-AI can support disciplinary learning when aligned with pedagogical intent. Drawing on the Technological Pedagogical Content Knowledge (TPACK) framework and action research methodology, the instructors designed GPTs to scaffold tasks such as research question formulation, interview practice, fieldnote analysis, and design thinking. Thematic analysis of student reflections, AI chat logs, and final assignments revealed that the tools enhanced student reflexivity, improved interview techniques, and supported structured analytic thinking. However, students also expressed concerns about cognitive overload, reduced immersion in data, and the formulaic nature of AI responses. The study offers three key insights: AI can be a powerful scaffold for active learning when paired with human facilitation; custom GPTs can serve as cognitive partners in iterative research practice; and educator-led design is critical to pedagogically meaningful AI integration. This research contributes to emerging scholarship on AI in higher education by demonstrating how empowering educators to design custom tools can promote more reflective, responsible, and collaborative learning with AI.

Text generated by artificial intelligence (AI) chatbots is increasingly used in English as a foreign language (EFL) writing contexts, yet its impact on students' expository writing process and compositions remains understudied. This research examines how EFL secondary students edit AI-generated text. Exploring editing behaviors in their expository writing process and in expository compositions, and their effect on human-rated scores for content, organization, language, and overall quality. Participants were 39 Hong Kong secondary students who wrote an expository composition with AI chatbots in a workshop. A convergent design was employed to analyze their screen recordings and compositions to examine students' editing behaviors and writing qualities. Analytical methods included qualitative coding, descriptive statistics, temporal sequence analysis, human-rated scoring, and multiple linear regression analysis. We analyzed over 260 edits per dataset, and identified two editing patterns: one where students refined introductory units repeatedly before progressing, and another where they quickly shifted to extensive edits in body units (e.g., topic and supporting sentences). MLR analyses revealed that the number of AI-generated words positively predicted all score dimensions, while most editing variables showed minimal impact. These results suggest a disconnect between students' significant editing effort and improved composition quality, indicating AI supports but does not replace writing skills. The findings highlight the importance of genre-specific instruction and process-focused writing before AI integration. Educators should also develop assessments valuing both process and product to encourage critical engagement with AI text.

Using g eneralizability t heory, the research evaluates and compares score consistency between human and AI raters across two types of AP Chinese free - response writing tasks: story narration and email response. These essays were independently scored by two trained human raters and seven AI raters. Each essay received four scores: one holistic score and three analytic scores corres ponding to the domains of task completion, delivery, and language use. Results indicate that although human raters produced more reliable scores overall, LLMs demonstrated reasonable consistency under certain conditions, particularly for story narration tasks. Composite scoring that incorporates both human and AI raters improved reliability, which supports that hybrid scoring models may offer benefits for large - scale writing assessments. Keywords: large language model; a utomated essay s coring; generalizability theory; w riting a ssessment; AI - h uman c omparison 2 Exploring LLM Autoscoring Reliability in Large - Scale Writing Assessments Using Generalizability Theory The integration of large language models (LLMs) into a utomated e ssay s coring (AES) represents a significant shift in how essay scoring is approached. While traditional AES systems have long depended on manually engineered features and statistical models (Attali & Burstein, 2006; Dikli, 2006), LLMs offer the potential to assess student writing with greater flexibility and contextual sensitivi ty by drawing on deep learning architectures trained on diverse textual corpora ( Ifenthaler, 2022; Ouyang et al., 2022). However, despite their promising capabilities, recent studies indic ate that LLMs have not yet consistently matched the scoring reliability of established AES tools or trained human raters, especially in high - stakes language assessment contexts (Mizumoto & Eguchi, 2023; Xiao et al., 2025; Y ancey et al., 2023). These concerns highlight the need for rigorous evaluation of LLM - based scoring systems, particularly with respect to their reliability and alignment with human scoring standards. This study addresses these challenges by applying generalizability theory to systematical ly examine the consistency of LLM - generated scores on standardized writing tasks in the AP Chinese Language and Culture Exam (AP Chinese Exam) . Literature Review This section reviews the literature on AES and the application of LLMs to AES. It also provides brief overviews of generalizability theory and the AP Chinese Language and Culture Exam, followed by the research questions addressed in this study.

Mispronunciation detection and diagnosis (MDD) is a significant part in modern computer aided language learning (CALL) systems. Within MDD, phoneme-level pronunciation assessment is key to helping L2 learners improve their pronunciation. However, most systems are based on a form of goodness of pronunciation (GOP) which requires pre-segmentation of speech into phonetic units. This limits the accuracy of these methods and the possibility to use modern CTC-based acoustic models for their evaluation. In this study, we first propose self-alignment GOP (GOP-SA) that enables the use of CTC-trained ASR models for MDD. Next, we define a more general alignment-free method that takes all possible alignments of the target phoneme into account (GOP-AF). We give a theoretical account of our definition of GOP-AF, an implementation that solves potential numerical issues as well as a proper normalization which makes the method applicable with acoustic models with different peakiness over time. We provide extensive experimental results on the CMU Kids and Speechocean762 datasets comparing the different definitions of our methods, estimating the dependency of GOP-AF on the peakiness of the acoustic models and on the amount of context around the target phoneme. Finally, we compare our methods with recent studies over the Speechocean762 data showing that the feature vectors derived from the proposed method achieve state-of-the-art results on phoneme-level pronunciation assessment.

Retrieval practice is a well-established pedagogical technique known to significantly enhance student learning and knowledge retention. However, generating high-quality retrieval practice questions is often time-consuming and labor intensive for instructors, especially in rapidly evolving technical subjects. Large Language Models (LLMs) offer the potential to automate this process by generating questions in response to prompts, yet the effectiveness of LLM-generated retrieval practice on student learning remains to be established. In this study, we conducted an empirical study involving two college-level data science courses, with approximately 60 students. We compared learning outcomes during one week in which students received LLM-generated multiple-choice retrieval practice questions to those from a week in which no such questions were provided. Results indicate that students exposed to LLM-generated retrieval practice achieved significantly higher knowledge retention, with an average accuracy of 89%, compared to 73% in the week without such practice. These findings suggest that LLM-generated retrieval questions can effectively support student learning and may provide a scalable solution for integrating retrieval practice into real-time teaching. However, despite these encouraging outcomes and the potential time-saving benefits, cautions must be taken, as the quality of LLM-generated questions can vary. Instructors must still manually verify and revise the generated questions before releasing them to students. K eywords Retrieval practice large language models generative AI student learning multiple-choice questions STEM education data science higher education 1 Introduction Retrieval practice [30], frequently termed the "testing effect" [3], is a teaching technique that has been extensively studied. Empirical evidence has clearly indicated its ability to enhance long-term memory retention and overall learning [29, 3, 24, 27, 18, 2]. This technique involves the active recall of information from memory, a process that inherently strengthens neural connections and improves the future retrieval of that information.

A hallmark of human innovation is recombination -- the creation of novel ideas by integrating elements from existing concepts and mechanisms. In this work, we introduce CHIMERA, a large-scale Knowledge Base (KB) of over 28K recombination examples automatically mined from the scientific literature. CHIMERA enables large-scale empirical analysis of how scientists recombine concepts and draw inspiration from different areas, and enables training models that propose novel, cross-disciplinary research directions. To construct this KB, we define a new information extraction task: identifying recombination instances in scientific abstracts. We curate a high-quality, expert-annotated dataset and use it to fine-tune a large language model, which we apply to a broad corpus of AI papers. We showcase the utility of CHIMERA through two applications. First, we analyze patterns of recombination across AI subfields. Second, we train a scientific hypothesis generation model using the KB, showing that it can propose novel research directions that researchers rate as inspiring. We release our data and code at https://github.com/noy-sternlicht/CHIMERA-KB.

We introduce LlamaPIE, the first real-time proactive assistant designed to enhance human conversations through discreet, concise guidance delivered via hearable devices. Unlike traditional language models that require explicit user invocation, this assistant operates in the background, anticipating user needs without interrupting conversations. We address several challenges, including determining when to respond, crafting concise responses that enhance conversations, leveraging knowledge of the user for context-aware assistance, and real-time, on-device processing. To achieve this, we construct a semi-synthetic dialogue dataset and propose a two-model pipeline: a small model that decides when to respond and a larger model that generates the response. We evaluate our approach on real-world datasets, demonstrating its effectiveness in providing helpful, unobtrusive assistance. User studies with our assistant, implemented on Apple Silicon M2 hardware, show a strong preference for the proactive assistant over both a baseline with no assistance and a reactive model, highlighting the potential of LlamaPie to enhance live conversations.