Artificial Intelligence in Healthcare and Education
Abstract
Institutional guidance on generative artificial intelligence has expanded rapidly, yet it addresses administrators, instructors, and assessment designers rather than the students who make most decisions about AI use. Students report that the boundary of acceptable use remains unclear at the moment they must act. This conceptual paper proposes ten student-facing guidelines organized around three pillars: Learning and Growth, Ethics and Integrity, and Awareness and Safety. Drawing on a structured interpretive synthesis of research published between 2023 and 2026 and major policy frameworks, the paper argues that the pillars are jointly necessary because each addresses a distinct, empirically documented failure mode, and that a portable set of guidelines may support responsible use by externalizing metacognitive prompts that unguided AI use can weaken. Compared with policy guidance, institutional frameworks, assessment scales, and AI literacy curricula, the framework is distinctive in being student-facing, course-agnostic, and actionable at the point of decision. Five empirically testable propositions are specified for their evaluation.
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
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 18, 2026
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.