I had previously shared about this physical board game that I designed to teach electricity concepts. Now, with ChatGPT’s help, I have managed to produce a simple implementation of the board game so that there is no need to print and cut out the pieces anymore.
However, the game is still unable to detect if the light bulb will light up and automatically change the image colour or add the scores. That will require further complex programming due to the many possible outcomes for this game.
Players will take turns to connect their own bulbs to the terminals while trying to sabotage their opponent’s bulbs.
Players will take turns to place one piece on the 4-by-4 game board by clicking to select the electrical component and clicking on the square on the board to place it.
Upon placing the piece, the player can also turn that piece in any orientation (by clicking on it) within the same turn.
Players can choose to use up to two turns at any point in the game to rotate any piece that had been placed by any player.
In other words, each player has 9 turns: 7 placement turns and 2 rotation turns.
At lower levels, students can compete to see who has the most lit bulbs. However, they will need to be able to identify which light bulbs are lit. Do watch out for short-circuits.
At higher levels, students can compete to see whose light bulbs has the most total electrical power, with some calculations involved.
As evidence of the prowess of generative AI, this graph plotting app was created in about 15 minutes with simple prompts and a text editor. It was tested with an existing set of data and uploaded immediately to the repository.
The user will begin by uploading a set of data from an experiment, with the column header being the labels for the axes, in the form of a CSV file. The app immediately calculates and displays the best-fit straight line using linear regression, providing users with insights into the relationships between the variables in the dataset. This allows for easy analysis of trends, patterns, and relationships within the data. Students can use the app to quickly visualize and analyze data. The screenshot button enables them to save the graph in picture form for submission to their teachers.
It has also been made compatible with the Student Learning Space. The screenshots saved by students can be uploaded as an attachment for a free-response question. This is especially useful when students are doing lab work and the teacher does not want the students to bother too much with configuring the graph. The ZIP file for use in the SLS can be downloaded here.
Prompts used are:
Create a website using html and javascript for users to plot a graph using data from a csv file.
The user can upload a csv file with the first column being the horizontal axis and the second column being the vertical axis.
The column headers, which is the first set of values in the csv file, will form the labels for the axes of the graph.
Plot the points with the data in the csv file and use linear regression to obtain the best-fit straight line of the data.
The best fit line should be a line joining two red dots.
Indicate the gradient and intercept of the best-fit line.
Add a button at the bottom for users to take a screenshot of the graph.
A dice simulation is an excellent tool for teaching probability because it provides a hands-on, visual way for students to understand the concepts of probability through experimentation and observation.
In the simplest demonstration, students should be able to predict and observe that a fair six-sided dice gives equal probability of outcome and hence, almost equal total occurrences given a large-enough sample size.
Students can then be asked to predict the probability distribution if given two dice.
The good thing is that with simulations, students get to compare the experimental probabilities they calculated with the theoretical probabilities and discuss any discrepancies and reasons for them. This allows students to understand the concept of randomness and how experimental results may vary from theoretical expectations due to chance.
Click here to access the simulator and here to download it for SLS.
The prompts used for ChatGPT are as follow:
Provide the code for the following in a single html file:
Create a 6-sided dice simulator where the user can click on the image of a dice, the dice image will change randomly and the final result will be shown on the image.
The dice image will be represented by the file “dice1.png” for the number 1, “dice2.png” for number 2, “dice3.png” for number 3 and so on. The image size is 80 pixels by 80 pixels.
The default setting shows one dice. The initial image shown is “dice1.png”.
Create a button to cast the dice.
Create a bar chart with 6 vertical columns at the bottom that shows the frequency of the numbers obtained by the dice. Adjust the bar chart to show all 6 possible outcomes.
Each time the dice is cast and the result is shown, it is added to a list shown below the chart.
Create a button for an option to toggle between the use of one dice or two die. When this button is clicked, the list of results and the bar chart is cleared. The initial images shown are both “dice1.png”. If two die are used, both die will be shown side by side.
Both die will be cast when clicking on either dice. The bar chart now shows the frequency of the total of the numbers obtained by the die. Adjust the bar chart such that it now has 12 vertical columns show all 12 possible outcomes.
The total number for each toss will be shown in the list.
Despite learning some time ago that ChatGPT can help with coding, I had not had the chance to test it out. Since I had a pocket of time available to explore last week, I keyed in the following prompt:
“Create a simulation of an oscillating particle moving from left to right with simple harmonic motion with a slider to control the period of oscillation using javascript, html and css.”
I then cut and pasted the code in its entirety into a html file and this is the output:
This is what it looks like, in case you do not want to click into the link above.
Of course, more work needs to be done to improve the usability but I believe some of that can be done using ChatGPT as well. A basic knowledge of the programming language will certainly help to refine the user interface or add new functions.
Extending the oscillation to include 100 particles each with a constant phase difference, we can simulate a wave :
For the second simulation, I used the following prompt:
“Create a simulation of particles moving horizontally with simple harmonic motion. The simulation should display 100 particles arranged vertically, each oscillating horizontally at a different phase. The horizontal motion of the particles should simulate simple harmonic motion, with their positions following a sine wave pattern. The amplitude of oscillation should be set to 100 pixels, and the period of oscillation should be controlled by a slider input with a range from 0.1 to 2 seconds. The particles should be confined within a container with a fixed width of 600 pixels and a height of 400 pixels. The slider input should be positioned at the bottom of the container. The simulation should update the positions of the particles at regular intervals to create the illusion of continuous motion.”
Flashcards
Next, I tried creating an webpage that allows students to practise recalling definitions of specific terms that are obtained from a csv file. This is the prompt I gave:
“Create a revision webpage using html, javascript and css that references a csv file in the same folder with three columns: “topic”, “term” and “definition”. There should be a filter for the “topic” field. Each term in the “term” field will be displayed in turn using a left and right button. Another button labelled “Definition” will be used to show or hide the corresponding “definition” field at the bottom. Put all the script and style codes in the html file.”
At first, the button to display the definitions did not appear as ChatGPT misunderstood my instructions.
After making some adjustments, this is the link to the functioning site:
All you have to do is to update the csv file with the topics, terms and definitions and ensure the index file is in the same folder as the data.csv file.
The best part for Singaporean teachers is, the zip file can be uploaded as a file into SLS and students can use it to test their recall of key terms.
Conclusion
The rise of generative AI is indeed creating new opportunities for learning, even for teachers. What used to require long hours of learning can now be condensed into a session with ChatGPT. We will still need to give very specific instructions which require some basic understanding of the product. At the same time, we need to be able to make tweaks here and there, but that should be easier since we have the basic structure of the product already.
It took a while due to the need to adjust the equations used based on the position of the graphs, but here it is: https://www.geogebra.org/m/dfb53dps
The kinematics of a bouncing ball can be explained by considering the dynamics and forces involved in its motion. In this simulation, air resistance is assumed negligible. When a ball is dropped from a certain height and bounces off the ground, several key principles of physics come into play. Let’s break down the process step by step:
Free Fall: When the ball is released, it enters a state of free fall. During free fall, the only force acting on the ball is gravity. This force is directed downward and can be described by W = mg
W is the gravitational force. m is the mass of the ball. g is the acceleration due to gravity (approximately 9.81 m/s² near the surface of the Earth).
Impact with the Ground and Bounce: When the ball reaches the ground, it experiences a force due to the collision with the surface. This force is an example of a contact force and much larger than the gravitational force. This force depends on the elasticity of the ball and the surface it bounces off.
During the collision with the ground, the ball’s momentum changes rapidly. If the ball and the ground are both ideal elastic materials, the ball will bounce back with the same speed it had just before impact. In reality, some energy is lost during the collision, causing the bounce to be less than perfectly elastic. This simulation assumes elastic collisions.
Post-Bounce Motion: After the bounce, the ball starts moving upward. Gravity acts on it as it ascends, decelerating its motion until it reaches its peak height.
Second Descent: The ball then starts descending again, experiencing the force of gravity pulling it back down towards the ground.
This process continues with each bounce. In practice, with each bounce, some energy is lost due to the non-ideal nature of the collision and other dissipative forces like air resistance. As a result, each bounce is typically lower than the previous one until the ball eventually comes to rest. However, for simplicity, the simulation assumes no energy is lost during the collision and to dissipative forces.
An animated gif file is included here for use in powerpoint slides:
