Best Gemini prompts for Art, Drama, and Music Teachers, Postsecondary

Create a Google Apps Script that automatically generates student progress reports based on data from a Google Sheets spreadsheet, using the following columns: Student Name, Assignment Name, Grade, and Date Completed. The script should iterate over each row in the spreadsheet, create a new document for each student, and populate it with their assignment grades and overall progress. The document should be saved in a Google Drive folder named 'Student Progress Reports' and shared with the student's email address, which is listed in the 'Student Email' column. Use the following formatting: Arial font, size 12, with a header row that includes the student's name and the current date.

Use Google Data Studio to create a dashboard that visualizes the practice habits of music students, based on data from a Google Forms survey that collects information on the number of minutes practiced per day, the type of exercise practiced, and the student's self-assessed level of difficulty. The dashboard should include a bar chart showing the average practice time per day, a line chart showing the trend of practice time over the semester, and a scatter plot showing the relationship between practice time and self-assessed difficulty. Use a color scheme that is accessible for students with visual impairments and include a filter that allows the teacher to select a specific student or group of students to view.

Develop a Google Classroom integration that allows teachers to create and assign drama lesson plans, including scripts, videos, and discussion prompts, and tracks student engagement and completion of assignments. The integration should use the Google Classroom API to create a new assignment for each lesson plan, with a due date and point value, and should also create a new topic in the classroom stream for each lesson plan, with a brief summary and relevant attachments. Use a template that includes the following fields: Lesson Title, Objective, Materials, Procedure, and Assessment, and allow teachers to customize the template with their own specific requirements.

Train a machine learning model using Google's AutoML platform to predict the success of art students based on their portfolio submissions, using a dataset that includes features such as color palette, composition, and technique, as well as student demographic information and prior academic performance. The model should be trained on a labeled dataset of past student portfolios, with a target variable of 'Success' (defined as completion of the program with a GPA above 3.0). Use a neural network architecture with the following layers: input, convolutional, pooling, flatten, dense, and output, and tune the hyperparameters using a grid search algorithm to optimize the model's performance on a holdout test set.