The SCALE taxonomy offers a future-oriented framework of educational objectives for the age of artificial intelligence (AI). This conceptual paper asks how these objectives can be enacted through a pedagogy that remains embodied, creative, and connected to human life. We thus integrate SCALE with Arts-Based Pedagogy (ABP), drawing on embodied cognition to conceptualize ABP as a life-connected learning ecology organized through four interrelated dimensions: perception, representation, creative iteration, and resonance. Rather than fixed stages, these dimensions describe how artistic experience enables learners to notice lived phenomena, give ideas perceptible form, revise meaning through making, and reconnect creation with personal social life. Building on this conceptual integration, we propose the AI-empowered ABP for K-12 education and beyond. The pedagogy moves from sensorial perception and artistic representation to feedback and self-reflection, creative making and AI-empowered iteration, and sharing for resonance. We argue that AI may generate text, images, music alternatives, or feedback, but learners retain primary responsibility for intention, interpretation, judgment, and transformation. AI-empowered ABP therefore positions AI as a generative collaborator and facilitator within a human-led, life-connected process of meaning-making, through which SCALE habits of mind can be mobilized within shared inquiry.
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