Recently, teachers and institutions have looked for different ways to incorporate artificial intelligence (AI) into their curriculums, whether it be teaching about machine learning (ML) or incorporating it into creating lesson plans, grading, or other educational applications. Generative AI models, in particular large language models (LLMs), have dramatically sped up AI’s impact on education. Generative AI and natural language programming (NLP) models have great potential to enhance teaching and learning by generating personalized learning content and providing engaging learning experiences for students.
In this post, we create a generative AI solution for teachers to create course materials and for students to learn English words and sentences. When students provide answers, the solution provides real-time assessments and offers personalized feedback and guidance for students to improve their answers.
Specifically, teachers can use the solution to do the following:
Students can use the solution to do the following:
We walk you through the steps of creating the solution using Amazon Bedrock, Amazon Elastic Container Service (Amazon ECS), Amazon CloudFront, Elastic Load Balancing (ELB), Amazon DynamoDB, Amazon Simple Storage Service (Amazon S3), and AWS Cloud Development Kit (AWS CDK).
The following diagram shows the resources and services used in the solution.
The solution runs as a scalable service. Teachers and students use their browsers to access the application. The content is served through an Amazon CloudFront distribution with an Application Load Balancer as its origin. It saves the generated images to an S3 bucket, and saves the teacher’s assignments and the students’ answers and scores to separate DynamoDB tables.
The solution uses Amazon Bedrock to generate questions, answers, assignment images and to grade students’ answers. Amazon Bedrock is a fully managed service that makes foundation models from leading AI startups and Amazon available via easy-to-use API interfaces. The solution also uses the grammatical error correction API and the paraphrase API from AI21 to recommend word and sentence corrections.
You can find the implementation details in the following sections. The source code is available in the GitHub repository.
You should have some knowledge of generative AI, ML, and the services used in this solution, including Amazon Bedrock, Amazon ECS, Amazon CloudFront, Elastic Load Balancing, Amazon DynamoDB and Amazon S3
We use AWS CDK to build and deploy the solution. You can find the setup instructions in the readme file.
Teachers can create an assignment from an input text using the following GUI page. An assignment comprises an input text, the questions and answers generated from the text, and an image generated from the input text to represent the assignment.
For our example, a teacher inputs the Kids and Bicycle Safety guidelines from the United States Department of Transportation. For the input text, we use the file bike.safe.riding.tips.txt.
The following is the generated image output.
The following are the generated questions and answers:"question": "What should you always wear when riding a bicycle?",
"answer": "You should always wear a properly fitted bicycle helmet when riding a bicycle. A helmet protects your brain and can save your life in a crash.""question": "How can you make sure drivers can see you when you are bicycling?",
"answer": "To make sure drivers can see you, wear bright neon or fluorescent colors. Also use reflective tape, markings or flashing lights so you are visible.""question": "What should you do before riding your bicycle?",
"answer": "Before riding, you should inspect your bicycle to make sure all parts are secure and working properly. Check that tires are inflated, brakes work properly, and reflectors are in place.""question": "Why is it more dangerous to ride a bicycle at night?",
"answer": "It is more dangerous to ride at night because it is harder for other people in vehicles to see you in the dark.""question": "How can you avoid hazards while bicycling?",
"answer": "Look ahead for hazards like potholes, broken glass, and dogs. Point out and yell about hazards to bicyclists behind you. Avoid riding at night when it is harder to see hazards."
The teacher expects the students to complete the assignment by reading the input text and then answering the generated questions.
The portal uses Amazon Bedrock to create questions, answers, and images. Amazon Bedrock speeds up the development of generative AI solutions by exposing the foundation models through API interfaces. You can find the source code in the file 1_Create_Assignments.py.
The portal invokes two foundation models:
The portal saves generated images to an S3 bucket using the function load_file_to_s3. It creates an assignment based on the input text, the teacher ID, the generated questions and answers, and the S3 bucket link for the loaded image. It saves the assignment to the DynamoDB table assignments using the function insert_record_to_dynamodb.
You can find the AWS CDK code that creates the DynamoDB table in the file cdk_stack.py.
Teachers can browse assignments and the generated artifacts using the following GUI page.
The portal uses the function get_records_from_dynamodb to retrieve the assignments from the DynamoDB table assignments. It uses the function download_image to download an image from the S3 bucket. You can find the source code in the file 2_Show_Assignments.py.
A student selects and reads a teacher’s assignment and then answers the questions of the assignment.
The portal provides an engaging learning experience. For example, when the student provides the answer “I should waer hat protect brain in crash” the portal grades the answer in real time by comparing the answer with the correct answer. The portal also ranks all students’ answers to the same question and shows the top three scores. You can find the source code in the file 3_Complete_Assignments.py.
The portal saves the student’s answers to a DynamoDB table called answers. You can find the AWS CDK code that creates the DynamoDB table in the file cdk_stack.py.
To grade a student’s answer, the portal invokes the Amazon Titan Embeddings model to translate the student’s answer and the correct answer into numerical representations and then compute their similarity as a score. You can find the solution in the file 3_Complete_Assignments.py.
The portal generates suggested grammatical corrections and sentence improvements for the student’s answer. Finally, the portal shows the correct answer to the question.
The portal uses the grammatical error correction API and the paraphrase API from AI21 to generate the recommended grammatical and sentence improvements. The AI21 paraphrase model is available as a foundation model in SageMaker. You can deploy the AI21 paraphrase model as an inference point in SageMaker and invoke the inference point to generate sentence improvements.
The functions generate_suggestions_sentence_improvements and generate_suggestions_word_improvements in the file 3_Complete_Assignments.py show an alternative way of using the AI21 REST API endpoints. You need to create an AI21 account and find the API key associated with your account to invoke the APIs. You will have to pay for the invocations after the trial period.
This post showed you how to use an AI-assisted solution to improve the teaching and learning experience by using multiple generative AI and NLP models. You can use the same approach to develop other generative AI prototypes and applications.
If you’re interested in the fundamentals of generative AI and how to work with foundation models, including advanced prompting techniques, check out the hands-on course Generative AI with LLMs. It’s an on-demand, 3-week course for data scientists and engineers who want to learn how to build generative AI applications with LLMs. It’s a good foundation to start building with Amazon Bedrock. Visit the Amazon Bedrock Features page and sign up to learn more about Amazon Bedrock.
Jeff Li is a Senior Cloud Application Architect with the Professional Services team at AWS. He is passionate about diving deep with customers to create solutions and modernize applications that support business innovations. In his spare time, he enjoys playing tennis, listening to music, and reading.
Isaac Privitera is a Senior Data Scientist at the Generative AI Innovation Center, where he develops bespoke generative AI based solutions to address customers’ business problems. He works primarily on building responsible AI systems using retrieval augmented generation (RAG) and chain of thought reasoning. In his spare time he enjoys golf, football, and walking with his dog Barry.
Harish Vaswani is a Principal Cloud Application Architect at Amazon Web Services. He specializes in architecting and building cloud native applications and enables customers with best practices in their cloud transformation journey. Outside of work, Harish and his wife, Simin, are award-winning independent short film producers and love spending their time with their 5-year old son, Karan.
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