Sep 2026· Australian dental journal· 0 citations· 54 references
Medicine
Abstract
Generative artificial intelligence (AI) is transforming dental education, presenting simultaneous opportunities and challenges to academic integrity, assessment validity and clinical competency development. Faculty perspectives, the most critical yet understudied determinant of successful implementation, remain insufficiently addressed in the current literature. A narrative review of published evidence examining generative AI in dental education was conducted, with particular emphasis on faculty perspectives, assessment redesign, clinical applications, as well as ethical and regulatory frameworks applicable to the Australian context. Evidence reveals widespread gaps in faculty AI knowledge and pedagogical readiness, underscoring the need for comprehensive faculty development. Traditional assessment approaches demonstrate significant vulnerabilities to AI assistance, necessitating a fundamental redesign toward authentic evaluation of competencies that AI cannot or finds very difficult to replicate. If an AI system lacking clinical training, manual dexterity or patient interaction experience can pass examinations meant to ensure practice-readiness, the question is not how such use might be detected but what competencies those assessments are measuring, and this question should drive the choices made about assessment and its associated learning activities. Clinical integration offers genuine diagnostic and simulation benefits but risks over-reliance and deskilling without structured implementation. Australian regulatory guidance provides actionable frameworks for responsible adoption. Successful AI integration requires systematic, phased approaches that prioritise faculty readiness, inclusion of its use as an adjunctive tool, redesign of assessments to evaluate authentic clinical performance, safeguard manual competency development, and uphold ethical principles consistent with Australian professional standards, whilst remaining adaptable as AI capability continues to evolve.
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