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Die Deutschen sind Digital-Profis – zumindest beim Katzenvideo posten. Doch wenn’s ums Erkennen von Fake News, Urheberrecht oder Passwörterraten geht, sieht’s düster aus. Initiative D21 nennt das „Digital Skills Gap“. Man könnte auch sagen: Bedienung top, Verständnis flop.
➡️ https://initiatived21.de/publikatione…

Digital Skills Gap
Digitale Spaltung neu vermessen: Kompetenzen im Lebenslagenvergleich

Validity Verification of the New TOEFL Writing Task Based on Classical Test Theory
Yinyu Zhang
https://arxiv.org/abs/2509.05347 https://arxiv.org/pdf/2509.…

Validity Verification of the New TOEFL Writing Task Based on Classical Test Theory
The TOEFL iBT has introduced the Academic Discussion Task (ADT) to assess test-takers' ability to engage in academic discourse, reflecting the growing emphasis on interactive communication skills in higher education. However, research on the ADT's validity and fairness particularly for culturally and linguistically diverse groups, such as Chinese students, remains limited. This study addresses this gap by employing Classical Test Theory (CTT) to evaluate the psychometric properties of the ADT a…

MagiClaw: A Dual-Use, Vision-Based Soft Gripper for Bridging the Human Demonstration to Robotic Deployment Gap
Tianyu Wu, Xudong Han, Haoran Sun, Zishang Zhang, Bangchao Huang, Chaoyang Song, Fang Wan
https://arxiv.org/abs/2509.19169

MagiClaw: A Dual-Use, Vision-Based Soft Gripper for Bridging the Human Demonstration to Robotic Deployment Gap
The transfer of manipulation skills from human demonstration to robotic execution is often hindered by a "domain gap" in sensing and morphology. This paper introduces MagiClaw, a versatile two-finger end-effector designed to bridge this gap. MagiClaw functions interchangeably as both a handheld tool for intuitive data collection and a robotic end-effector for policy deployment, ensuring hardware consistency and reliability. Each finger incorporates a Soft Polyhedral Network (SPN) with an embedd…

How Well Do LLMs Predict Prerequisite Skills? Zero-Shot Comparison to Expert-Defined Concepts
Ngoc Luyen Le, Marie-H\'el\`ene Abel
https://arxiv.org/abs/2507.18479 https://

How Well Do LLMs Predict Prerequisite Skills? Zero-Shot Comparison to Expert-Defined Concepts
Prerequisite skills - foundational competencies required before mastering more advanced concepts - are important for supporting effective learning, assessment, and skill-gap analysis. Traditionally curated by domain experts, these relationships are costly to maintain and difficult to scale. This paper investigates whether large language models (LLMs) can predict prerequisite skills in a zero-shot setting, using only natural language descriptions and without task-specific fine-tuning. We introdu…

The Dialogue That Heals: A Comprehensive Evaluation of Doctor Agents' Inquiry Capability
Linlu Gong, Ante Wang, Yunghwei Lai, Weizhi Ma, Yang Liu
https://arxiv.org/abs/2509.24958

The Dialogue That Heals: A Comprehensive Evaluation of Doctor Agents' Inquiry Capability
An effective physician should possess a combination of empathy, expertise, patience, and clear communication when treating a patient. Recent advances have successfully endowed AI doctors with expert diagnostic skills, particularly the ability to actively seek information through inquiry. However, other essential qualities of a good doctor remain overlooked. To bridge this gap, we present MAQuE(Medical Agent Questioning Evaluation), the largest-ever benchmark for the automatic and comprehensive …

LingoQ: Bridging the Gap between ESL Learning and Work through AI-Generated Work-Related Quizzes
Yeonsun Yang, Sang Won Lee, Jean Y. Song, Sangdoo Yun, Young-Ho Kim
https://arxiv.org/abs/2509.17477

LingoQ: Bridging the Gap between ESL Learning and Work through AI-Generated Work-Related Quizzes
Non-native English speakers performing English-related tasks at work struggle to sustain ESL learning, despite their motivation. Often, study materials are disconnected from their work context. Although workers rely on LLM assistants to address their immediate needs, these interactions may not directly contribute to their English skills. We present LingoQ, an AI-mediated system that allows workers to practice English using quizzes generated from their LLM queries during work. LingoQ leverages t…

MARG: MAstering Risky Gap Terrains for Legged Robots with Elevation Mapping
Yinzhao Dong, Ji Ma, Liu Zhao, Wanyue Li, Peng Lu
https://arxiv.org/abs/2509.20036 https://

MARG: MAstering Risky Gap Terrains for Legged Robots with Elevation Mapping
Deep Reinforcement Learning (DRL) controllers for quadrupedal locomotion have demonstrated impressive performance on challenging terrains, allowing robots to execute complex skills such as climbing, running, and jumping. However, existing blind locomotion controllers often struggle to ensure safety and efficient traversal through risky gap terrains, which are typically highly complex, requiring robots to perceive terrain information and select appropriate footholds during locomotion accurately.…

SCoGen: Scenario-Centric Graph-Based Synthesis of Real-World Code Problems
Xifeng Yao, Dongyu Lang, Wu Zhang, Xintong Guo, Huarui Xie, Yinhao Ni, Ping Liu, Guang Shen, Yi Bai, Dandan Tu, Changzheng Zhang
https://arxiv.org/abs/2509.14281

SCoGen: Scenario-Centric Graph-Based Synthesis of Real-World Code Problems
Significant advancements have been made in the capabilities of code large language models, leading to their rapid adoption and application across a wide range of domains. However, their further advancements are often constrained by the scarcity of real-world coding problems. To bridge this gap, we propose a novel framework for synthesizing code problems that emulate authentic real-world scenarios. This framework systematically integrates domain knowledge, domain skills, and coding skills, all o…

RadGame: An AI-Powered Platform for Radiology Education
Mohammed Baharoon, Siavash Raissi, John S. Jun, Thibault Heintz, Mahmoud Alabbad, Ali Alburkani, Sung Eun Kim, Kent Kleinschmidt, Abdulrahman O. Alhumaydhi, Mohannad Mohammed G. Alghamdi, Jeremy Francis Palacio, Mohammed Bukhaytan, Noah Michael Prudlo, Rithvik Akula, Brady Chrisler, Benjamin Galligos, Mohammed O. Almutairi, Mazeen Mohammed Alanazi, Nasser M. Alrashdi, Joel Jihwan Hwang, Sri Sai Dinesh Jaliparthi, Luke David Nelson…

RadGame: An AI-Powered Platform for Radiology Education
We introduce RadGame, an AI-powered gamified platform for radiology education that targets two core skills: localizing findings and generating reports. Traditional radiology training is based on passive exposure to cases or active practice with real-time input from supervising radiologists, limiting opportunities for immediate and scalable feedback. RadGame addresses this gap by combining gamification with large-scale public datasets and automated, AI-driven feedback that provides clear, struct…

OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction
Lujie Yang, Xiaoyu Huang, Zhen Wu, Angjoo Kanazawa, Pieter Abbeel, Carmelo Sferrazza, C. Karen Liu, Rocky Duan, Guanya Shi
https://arxiv.org/abs/2509.26633

OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction
A dominant paradigm for teaching humanoid robots complex skills is to retarget human motions as kinematic references to train reinforcement learning (RL) policies. However, existing retargeting pipelines often struggle with the significant embodiment gap between humans and robots, producing physically implausible artifacts like foot-skating and penetration. More importantly, common retargeting methods neglect the rich human-object and human-environment interactions essential for expressive loco…

StarDojo: Benchmarking Open-Ended Behaviors of Agentic Multimodal LLMs in Production-Living Simulations with Stardew Valley
Weihao Tan, Changjiu Jiang, Yu Duan, Mingcong Lei, Jiageng Li, Yitian Hong, Xinrun Wang, Bo An
https://arxiv.org/abs/2507.07445

StarDojo: Benchmarking Open-Ended Behaviors of Agentic Multimodal LLMs in Production-Living Simulations with Stardew Valley
Autonomous agents navigating human society must master both production activities and social interactions, yet existing benchmarks rarely evaluate these skills simultaneously. To bridge this gap, we introduce StarDojo, a novel benchmark based on Stardew Valley, designed to assess AI agents in open-ended production-living simulations. In StarDojo, agents are tasked to perform essential livelihood activities such as farming and crafting, while simultaneously engaging in social interactions to est…

Best Practices and Considerations for Child Speech Corpus Collection and Curation in Educational, Clinical, and Forensic Scenarios
John Hansen, Satwik Dutta, Ellen Grand
https://arxiv.org/abs/2507.12870

Best Practices and Considerations for Child Speech Corpus Collection and Curation in Educational, Clinical, and Forensic Scenarios
A child's spoken ability continues to change until their adult age. Until 7-8yrs, their speech sound development and language structure evolve rapidly. This dynamic shift in their spoken communication skills and data privacy make it challenging to curate technology-ready speech corpora for children. This study aims to bridge this gap and provide researchers and practitioners with the best practices and considerations for developing such a corpus based on an intended goal. Although primarily foc…

Detecting Struggling Student Programmers using Proficiency Taxonomies
Noga Schwartz, Roy Fairstein, Avi Segal, Kobi Gal
https://arxiv.org/abs/2508.17353 https://

Detecting Struggling Student Programmers using Proficiency Taxonomies
Early detection of struggling student programmers is crucial for providing them with personalized support. While multiple AI-based approaches have been proposed for this problem, they do not explicitly reason about students' programming skills in the model. This study addresses this gap by developing in collaboration with educators a taxonomy of proficiencies that categorizes how students solve coding tasks and is embedded in the detection model. Our model, termed the Proficiency Taxonomy Model…

When Avatars Have Personality: Effects on Engagement and Communication in Immersive Medical Training
Julia S. Dollis, Iago A. Brito, Fernanda B. F\"arber, Pedro S. F. B. Ribeiro, Rafael T. Sousa, Arlindo R. Galv\~ao Filho
https://arxiv.org/abs/2509.14132

When Avatars Have Personality: Effects on Engagement and Communication in Immersive Medical Training
While virtual reality (VR) excels at simulating physical environments, its effectiveness for training complex interpersonal skills is limited by a lack of psychologically plausible virtual humans. This is a critical gap in high-stakes domains like medical education, where communication is a core competency. This paper introduces a framework that integrates large language models (LLMs) into immersive VR to create medically coherent virtual patients with distinct, consistent personalities, built …

Learning Robust Motion Skills via Critical Adversarial Attacks for Humanoid Robots
Yang Zhang, Zhanxiang Cao, Buqing Nie, Haoyang Li, Yue Gao
https://arxiv.org/abs/2507.08303

Learning Robust Motion Skills via Critical Adversarial Attacks for Humanoid Robots
Humanoid robots show significant potential in daily tasks. However, reinforcement learning-based motion policies often suffer from robustness degradation due to the sim-to-real dynamics gap, thereby affecting the agility of real robots. In this work, we propose a novel robust adversarial training paradigm designed to enhance the robustness of humanoid motion policies in real worlds. The paradigm introduces a learnable adversarial attack network that precisely identifies vulnerabilities in motio…

SimCoachCorpus: A naturalistic dataset with language and trajectories for embodied teaching
Emily Sumner, Deepak E. Gopinath, Laporsha Dees, Patricio Reyes Gomez, Xiongyi Cui, Andrew Silva, Jean Costa, Allison Morgan, Mariah Schrum, Tiffany L. Chen, Avinash Balachandran, Guy Rosman
https://arxiv.org/abs/2509.14548

SimCoachCorpus: A naturalistic dataset with language and trajectories for embodied teaching
Curated datasets are essential for training and evaluating AI approaches, but are often lacking in domains where language and physical action are deeply intertwined. In particular, few datasets capture how people acquire embodied skills through verbal instruction over time. To address this gap, we introduce SimCoachCorpus: a unique dataset of race car simulator driving that allows for the investigation of rich interactive phenomena during guided and unguided motor skill acquisition. In this dat…