the world in a different light
I am grateful for the opportunity to share about my recent experience with AI from an education practitioner’s point of view as well as a breakout session about how to use GAI to create simple web applications. The above are the slides.
Photos were taken by Lawrence (see his site: Open Source Physics @ SG here) but I cropped them a little so as not to show others’ faces without their permission.
With a set of questions on the equations of rectilinear motion as an example, the following quiz was generated using javascript by ChatGPT. This type of quiz is not linked to a database unless such integration is done, so the scores are not stored. However, they make for good self-assessment for students at the end of each topic.
The quizzes are easily generated using a set of prompts that are shown below, and hosted on any static page such as github pages or uploaded as a zip file into the Student Learning Space.
The URL for this sample quiz is at https://physicstjc.github.io/sls/kinematics-quiz. In fact, this quiz can be used as a template for quantitative quizzes for other topics as well. All the user has to do is to edit the five fields in each question: the question, correct answer, unit, equation and explanation within the index.html file (right-click to save and edit with plain text editor).
The format of the questions is written in this way:
const problems = [
{
question: "A car starts from rest and accelerates uniformly at 2.0 m/s² for 10 seconds. What is the final velocity of the car?",
correctAnswer: 20,
unit: "m/s",
equation: "v = u + at",
explanation: "Using v = u + at, where u = 0 (starts from rest), a = 2.0 m/s², and t = 10 s, we get v = 0 + 2.0*10 = 20 m/s."
},
{
question: "A car traveling at 15 m/s accelerates uniformly at 3.0 m/s² for 5.0 seconds. What is the final velocity of the car?",
correctAnswer: 30,
unit: "m/s",
equation: "v = u + at",
explanation: "Using v = u + at, where u = 15 m/s, a = 3.0 m/s², and t = 5.0 s, we get v = 15 + 3.0*5.0 = 30 m/s."
}]The initial prompts given to ChatGPT were:
The follow-up or refinement prompts were:
The workflow for converting the generated codes to a web app can be found here.
In yet another demonstration of ChatGPT 4o’s capabilities, I generated the above simulation app without writing a single line of code myself: https://physicstjc.github.io/sls/relative-velocity/index.html
The process took 4 prompts.
Prompt 1: Create a javascript simulation in a single html file that demonstrates the addition of two velocity vectors to give a resultant velocity. The velocity of a boat travelling in still water moving across a river is shown as vertically upward. A slider will change the speed and direction of the water flow. The velocity of the river water is also shown. Finally, show the addition of the two vectors to give a third vector: the resultant velocity. Create a button for animating the boat’s movement.
Response: Here’s an HTML file with embedded JavaScript to create a simulation of the addition of two velocity vectors. This simulation includes a slider to adjust the speed and direction of the river flow, and a button to animate the boat’s movement. (Code is not included to avoid making this post too long.)
Prompt 2: Use two horizontal lines to show the shores of the river, also the starting and ending point of the boat’s journey. The boat should be represented with a pencil-like shape, moving upward from one horizontal line to another. The boat should be moving in the direction of the resultant velocity. The vectors should be represented with arrows, not just lines.
Response: Here’s the updated HTML file with the specified features:
Two horizontal lines representing the shores of the river.
A pencil-like shape representing the boat, moving from one shore to the other in the direction of the resultant velocity.
Arrows to represent the vectors.
Prompt 3: Very good! Now invert the shape of the boat vertically. Also, draw some light blue wavy curves between the two horizontal lines that are stationary when the river speed is zero but move at the river velocity otherwise.
Response: Here’s the updated HTML file with the specified changes:
The boat shape is inverted vertically.
Light blue wavy curves are drawn between the two horizontal lines to represent the river water. These waves are stationary when the river speed is zero but move at the river velocity otherwise.
Prompt 4: Keep the canvas to 640 pixels wide and 480 pixels tall. Resize everything else accordingly.
And what you see in the beginning of this post was the result.
I built a customised AI chatbot using OpenAI’s API, which could run on GPT3.5 or GPT4 and used it to facilitate socratic questioning for students to arrive at the correct answer to a commonly misunderstood Physics concept. The process was not too complicated. One would need a OpenAI developer account to obtain the API key, and a Streamlit account to deploy the app. To input the API key, update the settings of the Streamlit app with this line: OPENAI_API_KEY=”{key}”. The codes can be forked from here.
This video shows a simulation of the interaction that I had with it.
I also stored the interactions that my students had with the bot in csv format, saved in an AWS S3 bucket.
Here are some samples, in which you can see the system prompt given: Speak like a teacher who assesses the response of the student based on clarity, precision, accuracy, logic, relevance and significance. Help the user get to the answer by asking guiding questions to scaffold the learning. The question is: Two balls are placed at the back of a truck that is moving at constant velocity. The blue ball is twice the mass of the red ball. The floor of the truck is perfectly smooth. Compare the movement of the two balls when the truck comes to an abrupt stop. The success criteria for the user is to be able to explain that both balls will move at the same speed once the truck comes to an abrupt stop, according to Newton’s first law, since there will be no net force acting on them since the floor of the truck is smooth and there is no friction.
It was rather entertaining for the students as some of them tried to trick the bot to giving them the answers. I did a pre-test for this question before the test and only 3-5% of the students in both classes got the answer correct but almost all of them (ascertained by an informal poll) knew the correct answer after going through the discussion with the bot.
I hope to continue experimenting with variations of the system prompts when I have time. However, it’s back to marking and preparing for staff PD now!
Fleming’s Left-Hand Rule is a visual way to remember the direction of force on a current-carrying conductor in a magnetic field. In this rule, the thumb, forefinger, and middle finger of your left hand are held at right angles to each other. The thumb represents the direction of the force, the forefinger the direction of the magnetic field, and the middle finger the direction of the current.
To visualize this with GlowScript’s VPython, I used ChatGPT to generate a 3D scene with arrows representing each of these directions.
The prompts used were
LinkGenerate a glowscript python code for visualising fleming’s left-hand rule.
Use mouse or finger interactions to rotate the scene
Include a toggle to switch from the left-hand rule to the right hand rule.
First of all, let me introduce the 5C approach to unpacking a science question. This was developed in collaboration with a few other Science senior teachers in Singapore, namely Dr Bernard Ng (ST Chem, Edgefield Sec), Mr Shah Dhaval Prahhaker (ST Physics, Hai Sing Catholic High), Mrs Kim Loh-Teo (ST Physics, Guangyang Sec) and Ms Jacqueline Ng (ST Biology, Nan Hua High School). This question-answering approach is meant to serve as a scaffold for students to draw connections between new contexts and prior knowledge in order to answer higher order thinking questions.
While exploring customised GPTs, I attempted to integrate the AI’s capabilities with the 5C thinking process in order to flesh out the 5 components of the process. I also uploaded the syllabus document for the O level sciences for retrieval augmented generation so that the responses can make specific mentions of the topic titles and concepts’ key ideas. The prompts given to the GPT was also quite detailed. Here’s how it works:
When a question is presented, either in text or image form, the AI first simplifies the question. This involves summarising the main ideas or concepts present in the question. For example, if the question is about the principles of thermodynamics, the AI might summarize this as “a problem relating to heat transfer and energy conservation.”
The AI, using its understanding of the syllabus and educational material, will then identify the most likely chapter or topic that the question is based on. This helps in narrowing down the scope of the content and focusing on relevant material.
Next, the AI lists out the key concepts or keywords relevant to the topic, as found in the syllabus document. For instance, in a question about cellular respiration, keywords like “mitochondria,” “ATP,” and “glycolysis” might be identified.
The AI will then demonstrate how these keywords and concepts can be applied to answer the question. This involves showing the connection between theoretical knowledge and practical application. For example, linking the concept of “ATP” to the energy requirements in cellular processes.
Rather than providing a direct answer, the AI guides the user in constructing their solution. This might involve suggesting steps to solve the problem, such as breaking down the question into smaller parts, applying formulas, or suggesting methods for analysis.
As an example, the image of a question on Dynamics was given. The AI was able to correctly simplify the context, identify the relevant topic and concepts and suggest an approach to solving the problem.
The AI will only provide a direct solution if explicitly prompted by the user. In such cases, if the solution involves mathematical problems, it will be presented using LaTeX generated math working to ensure clarity and precision. After providing the solution, the AI encourages the user to try a different problem for practice and consolidation of learning.
Finally, if the user desires, the AI can generate similar questions. This allows for further practice and helps in reinforcing the concepts learned. The limitation for this last task is that it cannot produce similar images if the original question involves graphs or diagrams. Therefore, it will only work for word problems such as the following.
This approach not only makes the learning process interactive and engaging but also ensures that the understanding of concepts is deep and thorough. It empowers students to be active learners, enhancing their problem-solving skills and conceptual understanding.
I then “trained” the AI with the ‘A’ level Physics syllabus and tested it out with questions of the same standard.
It was able to answer a question on how stationary waves is formed, but over-delivered unnecessarily by elaborating on concepts like wavelength being twice the distance between adjacent nodes when the question did not ask for it.
The next question on the path of a moving object entering a uniform field and moving in a parabolic path requires the AI to learn that from the diagram. It did so impressively.
The next attempt did not go so well. It involves an object travelling down a rough slope in a two-part question. This required the AI to infer that in the stem of the question, the object is held in place with friction opposing the parallel component of weight. The label for the rough slope could have hinted at it but the AI failed to see that. Therefore, it was not able to answer part (ii) of the question accurately.
Despite the occasional gaffes, customized GPTs hold significant potential in aiding students to unpack science questions, making the learning experience more interactive and engaging. These AI models, tailored for educational purposes, can offer customised scaffolds provided by the teacher and assistance to the students, personalised according to learning preference and pace. Other advantages include:
These AI models with the ability to process questions in the form of images can be a game-changer. One platform for teachers to build and deploy the AI is using the paid version of Poe.com allows teachers to customise a GPT-3.5 bot and deploy it to students. To deploy a GPT-4 bot, students will have to pay as well.
If one is not willing to pay, Poe.com’s Gemini-Pro GPT can do something similar for a limited number of responses but might be less accurate. Do try it out if you are an educator who is curious about how education can be radically transformed with AI.