Sep 2026· Journal of Educational Management and Instruction (JEMIN)· 0 citations· 38 references
Educational Leadership and Innovation
Abstract
The rapid development of Artificial Intelligence (AI) has reshaped education and intensified the need for effective digital leadership. This study examines the relationships among digital leadership, AI utilisation, and innovative and humanistic educational transformation, with particular attention to the moderating role of AI. The study was conducted at a private higher education institution in Bandar Lampung, Indonesia. Using a sequential explanatory mixed-methods design, quantitative data were collected from 150 students drawn from a population of 200 through a five-point Likert-scale questionnaire and analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The qualitative phase involved semi-structured interviews with 10 purposively selected students from different study programs to explain the quantitative findings. The results show that digital leadership is positively related to AI utilisation (β = 0.570) and to innovative and humanistic educational transformation (β = 0.326). AI utilisation is also positively related to educational transformation (β = 0.333). In addition, the interaction between digital leadership and AI utilisation shows a small positive moderating effect (β = 0.085), suggesting that AI may modestly strengthen the contribution of digital leadership to educational transformation. Qualitative findings indicate that digital leadership supports institutional adaptation to technological change, while AI can improve learning effectiveness, administrative efficiency, and personalised learning when used ethically and purposefully. The study concludes that innovative and humanistic educational transformation requires strong digital leadership, responsible AI integration, and careful alignment between technological innovation and humanistic educational values.
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
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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