Module 4 short reflection
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Machine learning, as a branch of artificial intelligence, has numerous benefits for learning, since the amount of information it can provide can be considered infinite. However, this same artificial intelligence causes the dehumanization of learning itself, failing to focus on such important aspects as the person. The machine and the artificial are placed above the person themselves.
I found the machine learning process really intriguing and had a lot of fun experimenting with Teachable Machine. Seeing how the model “learned” from my training data made the process feel very hands-on and clear. The class discussion about bias in data was especially helpful — it made me realise how easily a model can be thrown off by poor or limited data. Linking this back to the model we built really drove home how important diverse, high-quality data is for accurate predictions. I think this would make a fantastic lesson in my classroom and could really help children understand not just how AI works, but also the importance of fairness, and ethical thinking when using technology.
Machine learning tools can create interactive, immersive learning experiences that increase student engagement and motivation. These tools can help make education more accesible for students with disabilities, language barriers, or other challenges.
By leveraging machine learning, educators can create more effective, efficient, and personalyzed learning experiences that benefit students and improve educational outcomes.
By incorporating these approaches, students can develop a deeper understanding of AI/ML concepts while learning to think critically about their applications and implications.Ensuring that the module works properly
Tras realizar las actividades del módulo, comprendí mejor cómo los modelos de aprendizaje automático aprenden a partir de datos de entrenamiento y cómo la diversidad de datos influye directamente en la precisión de las predicciones. Me llamó especialmente la atención cómo los sesgos en los datos pueden generar resultados injustos o erróneos, lo que plantea desafíos éticos importantes, especialmente al trabajar con datos de estudiantes. Considero fundamental integrar estos temas en la educación STEAM para fomentar un uso ético y consciente de la IA. A través de proyectos reales, podemos promover el pensamiento crítico y el compromiso cívico del alumnado.
I think the best way to learn how a machine learns, from what I observe with students in my Computer Science and Robotics classes, is to use applications like Machine Learning. They at first think that the machine has more skills than it actually has, but as they use the application they realise that the only thing the machine does is handle data. Its advantage is that it is capable of using large volumes of data in a very short time, but they also realise that it is not infallible and that details as small as offering a single point of view as training, makes it incapable of differentiating between simple things.
In higher grades they learn about the bias that comes from being trained on majority data, and how the machine’s predictions are based on frequency. Inequalities or injustices can be perpetuated.
I think these reflections are very necessary, because they make them less manipulable people, who critically approach the use of these new technologies, but also of their data or their personal image.
I think the best way to learn how a machine learns, from what I observe with students in my Computer Science and Robotics classes, is to use applications like Machine Learning. The boys and girls at first think that the machine has more skills than it actually has, but as they use the application they realise that the only thing the machine actually does is handle data. Its advantage is that it is capable of using large volumes of data in a very short time, but they also realise that it is not infallible and that details as small as offering a single point of view as training, makes it incapable of differentiating between simple things.
In higher grades they learn about the bias that comes from being trained on majority data, and how the machine’s predictions are based on frequency. This can perpetuate inequalities or injustices, for example when using artificial intelligence to select personnel or make decisions where ethics may be compromised.
I think these reflections are very necessary, because they make them less manipulable people, who critically approach the use of these new technologies, but also of their data or their personal image.
I think the best way to learn how a machine learns, from what I observe with students in my Computer Science and Robotics classes, is to use applications like Machine Learning. The boys and girls at first think that the machine has more skills than it actually has, but as they use the application they realise that the only thing the machine actually does is handle data. Its advantage is that it is capable of using large volumes of data in a very short time, but they also realise that it is not infallible and that details as small as offering a single point of view as training, makes it incapable of differentiating between simple things.
In higher grades they learn about the bias that comes from being trained on majority data, and how the machine’s predictions are based on frequency. This can perpetuate inequalities or injustices, for example when using artificial intelligence to select personnel or make decisions where ethics may be compromised.
I firmly believe these reflections are very necessary, because they make them less manipulable people, who critically approach the use of these new technologies, but also of their data or their personal image.
This is a trial that it is working correctly
I think the best way to learn how a machine learns, from what I observe with students in my Computer Science and Robotics classes, is to use applications like Machine Learning. The boys and girls at first think that the machine has more skills than it actually has, but as they use the application they realise that the only thing the machine actually does is handle data. Its advantage is that it is capable of using large volumes of data in a very short time, but they also realise that it is not infallible and that details as small as offering a single point of view as training, makes it incapable of differentiating between simple things.
In higher grades they learn about the bias that comes from being trained on majority data, and how the machine’s predictions are based on frequency. This can perpetuate inequalities or injustices, for example when using artificial intelligence to select personnel or make decisions where ethics may be compromised.
I think these reflections are very necessary, because they make them less manipulable people, who critically approach the use of these new technologies, but also of their data or their personal image.
Probe that it is working properly
El uso de la Inteligencia artificial es una actividad que en la sociedad actual está cada vez más de moda. Este tipo de herramientas facilitan la gestión de actividades. En el caso de los estudiantes, en la etapa de secundaria, debería ser una herramienta que ellos empelasen para el aprendizaje, pero desgraciadamente, se emplea para el trabajo fácil. En la materia de Lengua Castellana y Literatura es algo complicado, ya que este tipo de herramientas pueden suplir el trabajo de comprensión lectora, por ejemplo, de cada una de las actividades a realizar. Por ejemplo, este curso académico elaboré un cuestionario sobre un libro de lectura. Eran preguntas de comprensión lectora, y el alumnado utilizó la IA para su elaboración. Al ser el título del libro igual que otro, el alumnado empleó de manera errónea las respuestas. Es importante añadir numerosos ejemplos de textos y ser muy concreto en las preguntas para que las respuestas sea cada vez más exacta.
Un modelo de IA aprende como un alumno, observando ejemplos (como fotos del aula) para reconocer patrones y hacer predicciones. Es fundamental que esos datos sean diversos —distintos ángulos, objetos, iluminación— para mejorar su precisión y evitar sesgos. Si el modelo solo ve ejemplos limitados, puede cometer errores o excluir realidades importantes. En el aula, hay que cuidar la privacidad al usar datos del alumnado. Para trabajar estos temas en STEAM, se pueden usar proyectos reales con datos cercanos, que inviten a reflexionar sobre justicia, ética e inclusión en el uso de la tecnología.
1. El modelo aprende continuamente identificando patrones. Una vez entrenado, utiliza estos patrones para predecir o usar estos datos.
2. Un modelo entrenado en diversidad de datos, ayuda a realizar generalizaciones en lugar de memorizar casos específicos. El modelo es más apropiado y certero en condiciones de la vida real.
3. Si el modelo carece de diversidad, el modelo puede correr el peligro de no realizar un adecuado análisis de datos en los casos en que no se sigan exactamente los parámetros.
4. Todos los alumnos/as y sus responsables legales deben ser informados adecuadamente de los términos e implicaciones legales del uso de estos modelos en el aprendizaje. Por otra parte, igualmente hay que concienciar al alumnado del buen y correcto uso de estos modelos, creando alumnos/as responsables en su uso. No valen siempre y su uso no reemplaza a ootros modos de aprendizaje. Crear conciencia es importante.
5. Se pueden trabajar con el alumnado las implicaciones éticas que este uso supone realizando por ejemplo debates. Igualmente, se puede trabajar de manera interdisciplinar conectando AI a estudios sociales, ética, justicia, derecho… así como a matemáticas..de manera que el alumnado se haga consciente de cómo los datos impactan en la sociedad.Although I initially found some theoretical concepts—such as data diversity, data bias, and data literacy—quite complex, I recognize that they are essential to our teaching practice. Despite the difficulties, I understand how important it is to educate students on these topics so they can become critical and informed citizens. The tools themselves may be accessible, but we need to teach students how to use them responsibly, integrating them into the STEAM approach and guiding them with clear goals that promote ethical and social commitment.
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