Strategies for Higher Education Institutions: Resilience and Adaptability provides higher education leaders with a structured methodology for strategic decision-making under deep uncertainty. The book constructs four plausible scenarios for higher education to 2035, organized around two critical uncertainties: coordination logic (state-led versus market-led) and human-AI centricity (human-centric versus AI-first). It introduces a five-mechanism typology that explains how AI and emerging technologies reshape institutional value propositions, operating models, and capability sets, moving analysis from individual tools to structural forces. The book then translates scenarios into strategic action through the 7C Strategy Wheel and 7C Strategy Selection Framework, connecting environmental diagnosis to strategic posture selection, initiative classification, and signpost monitoring. The primary readers are senior institutional leaders, including vice-chancellors, presidents, rectors, deans, governing board members, and strategy executives. Policymakers, ecosystem players, and researchers in strategic management and higher education will also find applicable frameworks. The book functions as both an analysis and toolkit. Readers gain methods for stress-testing existing strategies against multiple futures, identifying robust actions that hold value across scenarios, building contingent strategies tied to specific conditions, and establishing signpost-based monitoring systems for strategic recalibration. The scenarios apply globally and are designed for application to specific national, regional, and institutional contexts.
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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