Generative artificial intelligence is a type of AI technology that can generate new data or content, such as text, images, audio, video, etc. This technology typically involves algorithms and models from fields like machine learning, natural language processing, and computer vision.
In the era of artificial intelligence, a generative course refers to the dynamic generation of new learning content through a chain of thought dialogue between teachers and students with generative AI, including text, images, audio, video, code, etc., which changes the way teaching and learning activities are conducted and reshapes course resources and teaching structures. The learning content and course resources of generative courses are generated during the human-machine interaction dialogue process, which can occur before classroom teaching (preparation stage), during classroom teaching, or in personalized learning and assessment analysis stages. Teachers can make choices and reorganize during the preparation stage and iteratively optimize, which can be planned in advance. If the generation occurs in real-time during the classroom, it is unpredictable. This intelligent generation method subverts teachers’ cognitive models and teaching methods in traditional classrooms. If used appropriately, it will become a “new productive force” for teachers. Here are a few examples for your reference:
1. Pre-class Preparation (Preparation Stage), Generating Teaching Design Through High-Performance Computing and Pre-trained Large Models:
Q:Please design a cross-disciplinary learning activity for first-grade students, requiring the inclusion of science, art, language, and mathematics, to inspire students to actively observe the small animals around them and cultivate the 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?
Through human-machine interaction and iterative questioning, generate teaching designs that meet one’s own needs.
2. Generating Learning Content Based on Teacher and Student Prompts
Q:In March, do not shoot birds; in April, do not eat crucian carp.
Q:How many nanometers is a single hair?
Q:Earth Day is here, please create a storybook educating students to protect birds and not shoot the spring birds.
3. Real-time Dynamic Generation of Infinite Learning Resources, Where Both Teachers and Students Can Generate Learning Content Through Real-Time Questions.
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 person preparing to shoot a bird, while another person is persuading him, and write beside it “Do not shoot the spring birds; the chicks in the nest are waiting for their mothers to return.”
4. In the Classroom Management Phase, Let Digital Humans Replace Us Teachers to Deliver Repetitive, Unwanted Messages:
5. Evaluation Generation:
Q:Please provide comments and suggestions for improvement on the above composition.
Q:Please design an evaluation scale for conducting labor practice activities in a primary school cooking classroom.
6. Cultivating Learners’ Ability to Independently Think and Judge the Correctness of Content.
Lastly, generative artificial intelligence is not always accurate, so it is essential to cultivate users’ ability to think independently and discern, to prevent AI from misleading users. Can you identify which bird’s nest below is generated by artificial intelligence, and why?
Can you find the errors in the poetry generated by artificial intelligence? Look closely!
This article involves AI from the Zhiyu Qingyan intelligent agent “Smart Education”.
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