"Artificial intelligence (AI) is changing how candidates prepare for high-stakes examinations, yet evidence from small-island systems is scarce. This convergent mixed-methods study examined how 171 stakeholders in 15 Caribbean territories perceive AI\u2019s role in examination preparation. Twenty four-point Likert items were analyzed with polychoric exploratory factor analysis, structural equation modeling (SEM), and nonparametric tests. The 137 substantive open-ended responses were thematically coded and integrated via a joint display. 57.3% agreed AI users perform better on high-stakes examinations, versus 88.3% who agreed AI aids preparation. The SEM explained 45% of variance in perceived performance benefit, below the a priori 70% criterion, with perceived pedagogical value as the dominant predictor. Qualitative stance converged with survey scores, and critical-thinking comments converged strongly with the over-reliance item. Some respondents attributed AI-related gains to misuse rather than learning, threatening score interpretation. Implications for assessment design, integrity policy, and instrument refinement are discussed."
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
In Compton Lecture, the distinguished legal scholar warns of potential “loss of the Enlightenment” while suggesting ways innovations can meet our civic and cultural needs.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Microsoft Research Blog· microsoft.comOct 22, 2026
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
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