Generative AI tools such as ChatGPT now provide novice programmers with unprecedented access to instant, personalized support. While this holds clear promise, their influence on students' metacognitive processes remains underexplored. Existing work has largely focused on correctness and usability, with limited attention to whether and how students' use of AI assistants supports or bypasses key metacognitive processes. This study addresses that gap by analyzing student-AI interactions through a metacognitive lens in university-level programming courses. We examined more than 10,000 dialogue logs collected over three years, complemented by surveys of students and educators. Our analysis focused on how prompts and responses aligned with metacognitive phases and strategies. Synthesizing these findings across data sources, we distill design considerations for AI-powered coding assistants that aim to support rather than supplant metacognitive engagement. Our findings provide guidance for developing educational AI tools that strengthen students' learning processes in programming education.

Computer-use agents can operate computers and automate laborious tasks, but despite recent rapid progress, they still lag behind human users, especially when tasks require domain-specific procedural knowledge about particular applications, platforms, and multi-step workflows. Humans can bridge this gap by watching video tutorials: we search, skim, and selectively imitate short segments that match our current subgoal. In this paper, we study how to enable computer-use agents to learn from online videos at inference time effectively. We propose a framework that retrieves and filters tutorial videos, converts them into structured demonstration trajectories, and dynamically selects trajectories as in-context guidance during execution. Particularly, using a VLM, we infer UI actions, segment videos into short subsequences of actions, and assign each subsequence a textual objective. At inference time, a two-stage selection mechanism dynamically chooses a single trajectory to add in context at each step, focusing the agent on the most helpful local guidance for its next decision. Experiments on two widely used benchmarks show that our framework consistently outperforms strong base agents and variants that use only textual tutorials or transcripts. Analyses highlight the importance of trajectory segmentation and selection, action filtering, and visual information, suggesting that abundant online videos can be systematically distilled into actionable guidance that improves computer-use agents at inference time. Our code is available at https://github.com/UCSB-NLP-Chang/video_demo.

Objective and scalable measurement of teaching quality is a persistent challenge in education. While Large Language Models (LLMs) offer potential, general-purpose models have struggled to reliably apply complex, authentic classroom observation instruments. This paper uses custom LLMs built on sentence-level embeddings, an architecture better suited for the long-form, interpretive nature of classroom transcripts than conventional subword tokenization. We systematically evaluate five different sentence embeddings under a data-efficient training regime designed to prevent overfitting. Our results demonstrate that these specialized models can achieve human-level and even super-human performance with expert human ratings above 0.65 and surpassing the average human-human rater correlation. Further, through analysis of annotation context windows, we find that more advanced models-those better aligned with human judgments-attribute a larger share of score variation to lesson-level features rather than isolated utterances, challenging the sufficiency of single-turn annotation paradigms. Finally, to assess external validity, we find that aggregate model scores align with teacher value-added measures, indicating they are capturing features relevant to student learning. However, this trend does not hold at the individual item level, suggesting that while the models learn useful signals, they have not yet achieved full generalization. This work establishes a viable and powerful new methodology for AI-driven instructional measurement, offering a path toward providing scalable, reliable, and valid feedback for educator development.

This thesis introduces the Haptic-Audio Code Interface (HACI), an educational tool designed to enhance programming education for visually impaired (VI) students by integrating haptic and audio feedback to compensate for the absence of visual cues. HACI consists of a non-resource-intensive web application supporting JavaScript program development, execution, and debugging, connected via a cable to an Arduino-powered glove with six integrated haptic motors to provide physical feedback to VI programmers. Motivated by the need to provide equitable educational opportunities in computer science, HACI aims to improve non-visual code navigation, comprehension, summarizing, editing, and debugging for students with visual impairments while minimizing cognitive load. This work details HACI's design principles, technical implementation, and a preliminary evaluation through a pilot study conducted with undergraduate Computer Science students. Findings indicate that HACI aids in the non-visual navigation and understanding of programming constructs, although challenges remain in refining feedback mechanisms to ensure consistency and reliability, as well as supplementing the current functionality with a more feature-reach and customizable accessible learning experience which will allow visually impaired students to fully utilize interleaved haptic and audio feedback. The study underscores the transformative potential of haptic and audio feedback in educational practices for the visually impaired, setting a foundation for future research and development in accessible programming education. This thesis contributes to the field of accessible technology by demonstrating how tactile and auditory feedback can be effectively integrated into educational tools, thereby broadening accessibility in STEM education.

The AI Song Contest was founded with the aim of showcasing the potential of human-AI co-creativity in the songwriting process. Now in its sixth year, the competition will conclude on 16 November with a live show in Amsterdam. From all the entrants, the jury have selected their top ten songs. The live event will feature performances from the ten finalists, and you will be able to watch on YouTube here . Listen to the songs and vote for your favourite.

Designing reinforcement learning curricula for agile robots traditionally requires extensive manual tuning of reward functions, environment randomizations, and training configurations. We introduce AURA (Autonomous Upskilling with Retrieval-Augmented Agents), a schema-validated curriculum reinforcement learning (RL) framework that leverages Large Language Models (LLMs) as autonomous designers of multi-stage curricula. AURA transforms user prompts into YAML workflows that encode full reward functions, domain randomization strategies, and training configurations. All files are statically validated before any GPU time is used, ensuring efficient and reliable execution. A retrieval-augmented feedback loop allows specialized LLM agents to design, execute, and refine curriculum stages based on prior training results stored in a vector database, enabling continual improvement over time. Quantitative experiments show that AURA consistently outperforms LLM-guided baselines in generation success rate, humanoid locomotion, and manipulation tasks. Ablation studies highlight the importance of schema validation and retrieval for curriculum quality. AURA successfully trains end-to-end policies directly from user prompts and deploys them zero-shot on a custom humanoid robot in multiple environments - capabilities that did not exist previously with manually designed controllers. By abstracting the complexity of curriculum design, AURA enables scalable and adaptive policy learning pipelines that would be complex to construct by hand. Project page: https://aura-research.org/

Climate models are limited by heavy computational costs, often producing outputs at coarse spatial resolutions, while many climate change impact studies require finer scales. Statistical downscaling bridges this gap, and we adapt the probabilistic U-Net for this task, combining a deterministic U-Net backbone with a variational latent space to capture aleatoric uncertainty. We evaluate four training objectives, afCRPS and WMSE-MS-SSIM with three settings for downscaling precipitation and temperature from $16\times$ coarser resolution. Our main finding is that WMSE-MS-SSIM performs well for extremes under certain settings, whereas afCRPS better captures spatial variability across scales.

Aspect-based Sentiment Analysis (ABSA) is a fine-grained opinion mining approach that identifies and classifies opinions associated with specific entities (aspects) or their categories within a sentence. Despite its rapid growth and broad potential, ABSA research and resources remain concentrated in commercial domains, leaving analytical needs unmet in high-demand yet low-resource areas such as education and healthcare. Domain adaptation challenges and most existing methods' reliance on resource-intensive in-training knowledge injection further hinder progress in these areas. Moreover, traditional evaluation methods based on exact matches are overly rigid for ABSA tasks, penalising any boundary variations which may misrepresent the performance of generative models. This work addresses these gaps through three contributions: 1) We propose a novel evaluation method, Flexible Text Similarity Matching and Optimal Bipartite Pairing (FTS-OBP), which accommodates realistic extraction boundary variations while maintaining strong correlation with traditional metrics and offering fine-grained diagnostics. 2) We present the first ABSA study of small decoder-only generative language models (SLMs; <7B parameters), examining resource lower bounds via a case study in education review ABSA. We systematically explore data-free (in-context learning and weight merging) and data-light fine-tuning methods, and propose a multitask fine-tuning strategy that significantly enhances SLM performance, enabling 1.5-3.8 B models to surpass proprietary large models and approach benchmark results with only 200-1,000 examples on a single GPU. 3) We release the first public set of education review ABSA resources to support future research in low-resource domains.

In the context of increasing urban risks, particularly from climate change-induced flooding, this paper presents an extended Reality (XR)-based framework to improve user risk training within urban built environments. The framework is designed to improve risk awareness and preparedness among various stakeholders, including citizens, local authorities, and emergency responders. Using immersive XR technologies, the training experience simulates real-world emergency scenarios, contributing to active participation and a deeper understanding of potential hazards and especially for floods. The framework highlights the importance of stakeholder participation in its development, ensuring that training modules are customized to address the specific needs of different user groups. The iterative approach of the framework supports ongoing refinement through user feedback and performance data, thus improving the overall effectiveness of risk training initiatives. This work outlines the methodological phases involved in the framework's implementation, including i) user flow mapping, ii) scenario selection, and iii) performance evaluation, with a focus on the pilot application in Senigallia, Italy. The findings underscore the potential of XR technologies to transform urban risk training, promoting a culture of preparedness and resilience against urban hazards.

Idempotent Generative Networks (IGNs) are deep generative models that also function as local data manifold projectors, mapping arbitrary inputs back onto the manifold. They are trained to act as identity operators on the data and as idempotent operators off the data manifold. However, IGNs suffer from mode collapse, mode dropping, and training instability due to their objectives, which contain adversarial components and can cause the model to cover the data manifold only partially -- an issue shared with generative adversarial networks. We introduce Non-Adversarial Idempotent Generative Networks (NAIGNs) to address these issues. Our loss function combines reconstruction with the non-adversarial generative objective of Implicit Maximum Likelihood Estimation (IMLE). This improves on IGN's ability to restore corrupted data and generate new samples that closely match the data distribution. We moreover demonstrate that NAIGNs implicitly learn the distance field to the data manifold, as well as an energy-based model.