Generative artificial intelligence is a type of AI technology capable of generating new data or content, such as text, images, audio, video, etc. This technology typically involves algorithms and models from fields such as machine learning, natural language processing, and computer vision.
The generative curriculum in the era of artificial intelligence refers to the dynamic generation of new learning content by teachers and students through a chain of dialogue with generative AI, including text, images, audio, video, code, etc. This changes the way teaching and learning activities are conducted and reshapes curriculum resources and teaching structures. The learning content and curriculum resources of the generative curriculum are generated during the human-machine interaction dialogue process, which can occur before class (preparation phase), during class, or in personalized learning and evaluation analysis phases. Teachers can make selections and reorganizations during the preparation phase, iteratively optimizing the design, which can be planned in advance. If the content is generated in real-time during class, it is unpredictable. This intelligent generation method subverts teachers’ cognitive models and teaching methods in traditional classrooms. When used appropriately, it will become a “new productive force” for teachers. Here are a few examples for reference:
1. Pre-Class Preparation (Preparation Phase), Generating Teaching Design Through Large-Scale Pre-Trained Models:
Q:Please design a cross-disciplinary learning activity for first-grade students, including subjects like science, art, language, and mathematics, to stimulate students’ positive observation of small animals around them and cultivate values of caring for nature and cherishing life.
Q:The above design is still not operational enough.
Q:This is much better, but how can we better highlight the scientific aspect during the activity?
After human-machine interaction and iterative questioning, a teaching design that meets one’s needs can be generated.
2. Generating Learning Content Based on Teacher and Student Prompts
Q:No birds in March, no eating crucian carp in April.
Q:How many nanometers is a hair?
Q: Earth Day is coming, please create a picture book story educating students to protect birds and not to harm spring birds.
3. Real-Time Dynamic Generation of Infinite Learning Resources, Where Both Teachers and Students Can Ask Questions to Generate Learning Content.
Q:Please list 10 idioms related to dragons and provide an illustration for each idiom.
Q:How to draw a poster advocating the protection of small birds?
Q:Draw a picture of a person preparing to shoot a bird, while another person is advising him, and write “Do not harm the spring birds that are waiting in the nest for their mother to return” beside it.
4. Classroom Management Phase, Letting Digital Humans Replace Teachers to Deliver Repetitive and Unwanted Information.
5. Evaluation Generation:
Q:Please provide feedback and revision suggestions on the above essay.
Q:Please design an evaluation scale for conducting practical cooking activities in elementary school cooking classrooms.
6. Cultivating Learners’ Independent Thinking and Judgment of Content Accuracy.
Finally, generative artificial intelligence is not always correct, so it is essential to cultivate users’ independent thinking and analytical skills to prevent AI from making nonsensical statements. Can you identify which of the following bird nests is generated by AI, and why?
Can you find any errors in the AI-generated poetry? Look closely.
