Artificial Intelligence (AI) is becoming ubiquitous in our everyday lives. In the United States, AI has increasingly been recognized as an important part of computer science (CS) standards for elementary school students. Prior research work has shown that AI-related content can be taught at upper elementary grade levels. However, classroom-ready, standards-aligned materials for CS in the context of AI remain limited at the elementary level. This paper showcases how educators may use generative AI to design standards-aligned materials for teaching CS and AI in elementary schools. Specifically, this paper presents a design case in which generative AI (Google Gemini) serves as a collaborative designer to co-author four comic-based stories for grades 4–6. The four comic stories, titled “The AI Journey with Milo and Zada,” “Zappy the AI Robot,” “The AI Adventures,” and “Think Like an Engineer,” are mapped to specific AI standards in the Alabama Digital Literacy and Computer Science (DLCS) Course of Study. This work provides a replicable example of using generative AI to co-design standards-aligned instructional materials with responsible use of AI. This work is also useful for instructional designers, elementary school teachers, and policymakers in elementary computer science education.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
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