Safe, Transparent, and Ethical Artificial Intelligence
The increasing integration of artificial intelligence (AI) into educational environments necessitates a structured framework to ensure its safe and ethical use. A manifesto outlining seven core principles for safe AI in education has been proposed, emphasizing the protection of student data, alignment with institutional strategies, adherence to didactic practices, minimization of errors, comprehensive user interfaces, human oversight, and ethical transparency. These principles are designed to guide the deployment of AI technologies in educational settings, addressing potential risks such as privacy violations, misuse, and over-reliance on technology. Smart Learning Applications (SLApps) are also introduced, integrating AI into the existing institutional technological ecosystem, with special attention to the learning management systems, enabling secure, role-adaptive, and course-specific learning experiences. While large language models like GPT offer transformative potential in education, they also present challenges related to accuracy, ethical use, and pedagogical alignment. To navigate these complexities, a checklist based on the Safe AI in Education principles is recommended, providing educators and institutions with a framework to evaluate AI tools, ensuring they support academic integrity, enhance learning experiences, and uphold ethical standards.
Authors
- Marc Alier (ORCID: https://orcid.org/0000-0003-3922-1516)
- Francisco José García‐Peñalvo (ORCID: https://orcid.org/0000-0001-9987-5584)
- María José Casany Guerrero (ORCID: https://orcid.org/0000-0002-5072-6745)
- Juanan Pereira (ORCID: https://orcid.org/0000-0002-7935-3612)
Institutions
- University of the Basque Country (ES)
- Universidad de Salamanca (ES)
- Universitat Politècnica de Catalunya (ES)
Publication Details
- Journal
- IJERI International Journal of Educational Research and Innovation
- Published
- 2024-12-03
- DOI
- https://doi.org/10.46661/ijeri.11036
- Citations
- 16
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 6.59
Funders
- Ministerio de Ciencia e Innovación