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Fecha
2024-06-28
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editorial
Association for Computing Machinery, Inc
Citas
Resumen
As the demand for personalized and adaptive learning experiences increase, there is a urgent need for providing effective feedback mechanisms within critical systems, such as in psychomotor learning systems. This proposal introduces an approach for the integration of retrieval-augmented generation tools to provide comprehensive and insightful feedback to users. By combining the strengths of retrieval-based techniques and generative models, these tools offer the potential to enhance learning outcomes by delivering tailored feedback that is both informative and engaging. The proposal also emphasises the importance of incorporating explainability and transparency concepts. Following the hybrid intelligence paradigm it is possible to ensure that the feedback provided by these tools is not only accurate but also understandable to humans. This approach fosters trust and promotes a deeper understanding of the psychomotor learning process, empowering users and facilitators to make informed decisions about the psychomotor learning path. The hybrid intelligence paradigm, which combines the strengths of both human and artificial intelligence, plays a crucial role in the deployment of these solutions. By taking advantage of the cognitive capabilities of human experts alongside the computational power of artificial intelligence algorithms, it is possible to offer personalised feedback that takes into account both technical accuracy and pedagogical effectiveness. Through these collaborative efforts it is also possible to create learning environments that are inclusive, adaptable, and beneficial to lifelong learning. In conclusion, this proposal introduces retrieval-augmented generation tools for providing feedback in psychomotor learning systems, which represents a significant step towards in its personalization, and whose ethical implications align with the new regulations on the implementation of intelligent technologies in critical systems.
Descripción
The registered version of this conference paper, first published in "UMAP ’24 Adjunct: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (pp. 380–385)", is available online at the publisher's website: https://doi.org/10.1145/3631700.3664900
Categorías UNESCO
Palabras clave
Hybrid Intelligence, Collaborative Learning, Retrieval Augmented Generation, Intelligent Psychomotor Systems, Human-Centered, Ethics, XAI
Citación
Portaz, M., Manjarrés, Á., & Santos, O. C. (2024, July). Harmonizing ethical principles: Feedback generation approaches in modeling human factors for assisted psychomotor systems. In UMAP ’24 Adjunct: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (pp. 380–385). ACM. https://doi.org/10.1145/3631700.3664900
Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial