ReviewFrontiers in artificial intelligence2024
A conceptual ethical framework to preserve natural human presence in the use of AI systems in education.
Review in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Artificial Intelligence Integration in Radiation Therapy Education: A Multi-Modal Approach.Journal of medical radiation sciences · 2026Article
- Open Auditorium-A human-centric extended reality blended learning tool.Frontiers in artificial intelligence · 2026Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, there has been a remarkable increase of interest in the ethical use of AI systems in education. On one hand, the potential for such systems is undeniable. Used responsibly, they can meaningfully support and enhance the interactive process of teaching and learning. On the other hand, there is a risk that natural human presence may be gradually replaced by arbitrarily created AI systems, particularly due to their rapidly increasing yet partially unguided capabilities. State-of-the-art ethical frameworks suggest high-level principles, requirements, and guidelines, but lack detailed low-level models of concrete processes and according properties of the involved actors in education. In response, this article introduces a detailed Unified Modeling Language (UML)-based ancillary framework that includes a novel set of low-level properties. Whilst not incorporated in related work, particularly the ethical behavior and visual representation of the actors are intended to improve transparency and reduce the potential for misinterpretation and misuse of AIS. The framework primarily focuses on school education, resulting in a more restrictive model, however, reflects on potentials and challenges in terms of improving flexibility toward different educational levels. The article concludes with a discussion of key findings and implications of the presented framework, its limitations, and potential future research directions to sustainably preserve natural human presence in the use of AI systems in education.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.