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Uso de la inteligencia artificial para la generación de recursos y unidades didácticas en la docencia

Authors: Pardines Murcia, Cristóbal Eduardo;

Uso de la inteligencia artificial para la generación de recursos y unidades didácticas en la docencia

Abstract

En el panorama actual la integración de la inteligencia artificial (IA) en la educación muestra un gran potencial para personalizar el aprendizaje y aumentar la eficiencia en las tareas docentes. Los modelos de IA generativos, como GPT-3.5, GPT-4 o GPT-4o, han llegado a un grado de avance que pueden llegar a cambiar la manera de ejercer la docencia. Entre las posibilidades a destacar y por las que son una propuesta más que interesante para su integración es la generación de unidades didácticas. Estas unidades pueden a su vez adaptarse a las necesidades específicas de cada estudiante permitiendo adaptaciones curriculares no significativas para alumnos con necesidades específicas de apoyo educativo (ANEAE), y promover un entorno más inclusivo. Además, mediante el uso de estos modelos de IA se puede automatizar la evaluación de trabajos académicos, aliviando la carga administrativa de los docentes y permitiéndoles centrarse en el desarrollo de habilidades críticas de los estudiantes. Sin embargo, el sesgo en los sistemas de IA es una preocupación importante. Para mitigar este problema, es esencial implementar medidas de auditoría y control que aseguren la equidad y justicia en estos sistemas. También se debe revisar el contenido generado, especialmente debido a la posibilidad de que la IA genere información incorrecta o "alucinaciones". En general, la IA tiene el potencial de transformar la educación, siempre que su implementación se maneje con cuidado y atención para evitar trabajar con información errónea o no verificada, perpetuar desigualdades y garantizar un impacto positivo para todos los estudiantes.

In the current landscape, the integration of artificial intelligence (AI) in education shows great potential for personalizing learning and increasing efficiency in teaching tasks. Generative AI models, such as GPT-3.5, GPT-4, GPT-4o, have reached a level of advancement that can potentially change the way teaching is conducted. Among the notable possibilities and reasons why they are a highly interesting proposition for integration is the generation of didactic units. These units can be adapted to the specific needs of each student, allowing for non-significant curricular adaptations for students with specific educational support needs (SSESN), and promoting a more inclusive environment. Additionally, the use of these AI models can automate the evaluation of academic work, alleviating the administrative burden on teachers and allowing them to focus on developing critical skills in students. However, bias in AI systems is a significant concern. To mitigate this issue, it is essential to implement auditing and control measures that ensure fairness and justice in these systems. The generated content must also be reviewed, especially due to the possibility of AI generating incorrect information or "hallucinations." Overall, AI has the potential to transform education, provided its implementation is handled carefully and attentively to avoid working with erroneous or unverified information, perpetuating inequalities, and ensuring a positive impact for all students.

Especialidad: Informática

Keywords

Recursos didácticos, CDU::3 - Ciencias sociales::37 - Educación. Enseñanza. Formación. Tiempo libre, Enseñanza personalizada, Herramientas educativas, Adaptación curricular, Inteligencia Artificial

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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