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Skill-Driven Certification Pathways: Measuring Industry Training Impact on Graduate Employability
Anatoli Kovalev, Narelle Stefanac, Marian-Andrei Rizoiu
https://arxiv.org/abs/2506.04588

Skill-Driven Certification Pathways: Measuring Industry Training Impact on Graduate Employability
Australia faces a critical technology skills shortage, requiring approximately $52,000$ new technology professionals annually by 2030, while confronting a widening gap between employer requirements and graduate capabilities. With only $1\%$ of technology graduates considered immediately work-ready, traditional educational pathways alone prove insufficient to meet industry demands. This research examines how industry certifications, such as Microsoft's AI-900 (Azure AI Fundamentals), can bridge …

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…

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…

“What AI sells is vastly different from what it delivers, particularly what it delivers out of the box.”
The post gives some great context on the study of “the difference between work-as-imagined (WAI) and work-as-done (WAD),” and says:
“If what we have to do to be productive with LLMs is to add a lot of scaffolding and invest effort to gain important but poorly defined skills, we should be able to assume that what we’re sold and what we get are rather different things. That gap implies that better designed artifacts could have better affordances, and be more appropriate to the task at hand.”
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Advances in LLMs with Focus on Reasoning, Adaptability, Efficiency and Ethics
Asifullah khan, Muhammad Zaeem Khan, Saleha Jamshed, Sadia Ahmad, Aleesha Zainab, Kaynat Khatib, Faria Bibi, Abdul Rehman
https://arxiv.org/abs/2506.12365

Advances in LLMs with Focus on Reasoning, Adaptability, Efficiency and Ethics
This survey paper outlines the key developments in the field of Large Language Models (LLMs), such as enhancing their reasoning skills, adaptability to various tasks, increased computational efficiency, and ability to make ethical decisions. The techniques that have been most effective in bridging the gap between human and machine communications include the Chain-of-Thought prompting, Instruction Tuning, and Reinforcement Learning from Human Feedback. The improvements in multimodal learning and…

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…

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…

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…