Artificial Intelligence, Applications and Image Recognition for SS 1

Class: SS 1
Term: Second Term
Week: 5
Age: 15 years
Duration: 60 minutes
Subject: Digital Technologies
Curriculum Theme: Emerging Technologies
Focal competence: Identifying the key stages of AI model training process
Key competencies/values: ICT and Digital Competencies; Digital Competencies; Innovation
Skills:

• Training an AI image classification model

Previous Lesson: Steps
Topic: Artificial Intelligence (AI) And Image Recognition
Subject Matter: Meaning and Applications of AI, Meaning and importance of the following concepts in AI

Specific Objectives

By the end of the lesson, pupils/students should be able to:

Cognitive Domain

• Define Artificial Intelligence (AI).
• Explain the applications of AI in various fields.
• Explain the importance of machine learning, model, data training, and prediction in AI.
• Describe how AI uses data to recognise patterns in images.
• Evaluate the accuracy of an AI model.

Psychomotor Domain

• Train a simple image classification model using online tools.
• Test the accuracy of their trained AI model.
• Use simple online tools to train and test AI models.

Reference Materials

The following resources were used in planning this lesson:

• 2025 New Revised Senior Secondary Education Curriculum (SSEC)
• Relevant State Unified Scheme of Work
• Digital Technologies for Senior Secondary Schools 1 Textbook
• The HeadTeacher Scheme of work For The New Revised Senior Secondary Education Curriculum (SSEC)

Instructional Materials

The teacher will teach this lesson with the aid of:

• Computers with internet access
• Webcam
• Projector or smartboard
• Access to machine learning tools such as Teachable Machine or Microsoft Lobe
• Short video clips demonstrating AI applications
• Chart showing stages of AI image interpretation
• Markers and flip charts for group activities

Rationale for the Lesson

This lesson is important because Artificial Intelligence is rapidly transforming various aspects of modern life and industries. Understanding AI and its applications, especially in image recognition, equips students with foundational knowledge for future digital literacy and potential career paths in technology. It also helps them appreciate how digital technologies are used to solve real-world problems.

Prerequisite/Previous Knowledge

Students should have basic knowledge of computer operations, internet usage, and digital data concepts.

Lesson Content/Board Summary

Artificial Intelligence (AI) and Image Recognition

Meaning of Artificial Intelligence (AI)

Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction. AI aims to enable machines to perform tasks that typically require human intelligence, such as problem-solving, understanding language, and recognising patterns.

Applications of Artificial Intelligence (AI)

AI is used in many areas to make tasks easier and more efficient. Some common applications include:

1. Healthcare: AI assists in diagnosing diseases, developing new drugs, and personalising treatment plans.
2. Self-driving Cars: AI enables vehicles to perceive their environment, navigate, and make driving decisions without human intervention.
3. Virtual Assistants: Tools like Siri, Alexa, and Google Assistant use AI to understand voice commands and perform tasks.
4. Fraud Detection: AI systems analyse financial transactions to identify and flag suspicious activities.
5. Image Recognition: AI is used to identify and classify objects, people, places, and actions in images and videos.
6. Recommendation Systems: AI powers suggestions for products, movies, or music on platforms like Netflix and Amazon.
7. Natural Language Processing (NLP): AI helps computers understand, interpret, and generate human language, used in translation and chatbots.

Key Concepts in AI

Machine Learning

Machine Learning (ML) is a subset of AI that allows computer systems to learn from data without being explicitly programmed. Instead of following strict instructions, ML algorithms build a model from sample data, known as “training data,” to make predictions or decisions without being explicitly programmed to perform the task. It is fundamental to how AI systems improve over time.

Model

In AI and Machine Learning, a “model” is a mathematical representation or algorithm that has been trained on a dataset to recognise patterns, make predictions, or perform specific tasks. Once trained, the model can take new, unseen data as input and produce an output based on the patterns it learned during training. For example, an image recognition model learns to distinguish between different objects after being shown many labelled images.

Data Training

Data training is the process of feeding a large amount of relevant data to an AI model so that it can learn patterns, relationships, and features within that data. During training, the model adjusts its internal parameters to minimise errors in its predictions. The quality and quantity of the training data significantly impact the model’s performance and accuracy. For image recognition, this involves showing the model many images with their correct labels (e.g., “cat,” “dog,” “car”).

Prediction

Prediction, in the context of AI, is the output generated by a trained AI model when it is given new, unseen input data. Based on the patterns and knowledge acquired during data training, the model makes an educated guess or classification about the new input. For example, an image recognition model, after training, can predict whether a new image contains a “cat” or a “dog.”

Key Stages of AI Image Interpretation Process

AI uses a systematic process to interpret and recognise patterns in images. The key stages are:

1. Data Acquisition and Preprocessing:
   • Data Acquisition: This involves collecting a large dataset of images relevant to the task. For example, if the AI needs to recognise different types of fruits, many images of apples, oranges, bananas, etc., are collected.
   • Preprocessing: Raw images often need to be cleaned and prepared. This stage involves resizing images to a uniform dimension, normalising pixel values (adjusting brightness and contrast), removing noise, and augmenting data (creating variations of existing images) to improve the model’s ability to generalise.
2. Feature Extraction:
   • After preprocessing, the AI system identifies and extracts important features or characteristics from the images. These features could be edges, corners, textures, shapes, colours, or specific patterns that help distinguish one object from another. Modern AI often uses deep learning, where the model automatically learns to extract these features.
3. Model Training:
   • In this stage, the extracted features and their corresponding labels (e.g., “apple,” “orange”) are fed into a machine learning algorithm. The algorithm learns to associate specific features with specific labels. Through repeated exposure to the training data, the model adjusts its internal parameters to accurately classify images. The goal is for the model to learn to identify objects correctly.
4. Interpretation (Prediction/Classification):
   • Once the model is trained, it can be used to interpret new, unseen images. When a new image is presented, the model processes it through the same feature extraction steps and then uses its learned knowledge to make a prediction or classification. For example, it might output “apple” with a certain probability, indicating its confidence in the classification.

Teaching Methods/Instructional Techniques

Discussion, Demonstration, Guided Practice, Question and Answer, Explanation, Group Work, Practical Activity

Instructional Procedures

Step 1: Introduction

Time: 5 minutes

Teaching Skill: Questioning/Engagement

Teacher’s Activity: The teacher greets the students and asks them to share examples of how computers or phones seem to “think” or “understand” things, like facial recognition on phones or voice commands. The teacher then introduces the topic: Artificial Intelligence and Image Recognition.

Pupils’ Activity: Pupils respond with examples like phone unlocking with face, voice assistants, or smart recommendations.

Learning Point: Introduction to AI concepts

Step 2: Meaning and Applications of AI

Time: 10 minutes

Teaching Skill: Explanation/Discussion

Teacher’s Activity: The teacher explains the meaning of Artificial Intelligence (AI) and discusses various real-world applications, using examples from the board summary and asking students for more. The teacher may show a short video clip demonstrating an AI application.

Pupils’ Activity: Pupils listen, ask questions, and contribute additional examples of AI applications they know.

Learning Point: AI meaning and uses

Step 3: Key Concepts in AI

Time: 10 minutes

Teaching Skill: Explanation/Definition

Teacher’s Activity: The teacher introduces and explains the key concepts of Machine Learning, Model, Data Training, and Prediction, highlighting their importance in how AI systems function. The teacher uses simple analogies to make the concepts clear.

Pupils’ Activity: Pupils listen, take notes, and ask clarifying questions about the terms.

Learning Point: Understanding AI terminology

Step 4: Key Stages of AI Image Interpretation Process

Time: 10 minutes

Teaching Skill: Explanation/Illustration

Teacher’s Activity: The teacher describes the key stages of AI image interpretation: Data Acquisition and Preprocessing, Feature Extraction, Model Training, and Interpretation. The teacher uses a chart or diagrams to illustrate the process and explains how AI uses data to recognise patterns in images.

Pupils’ Activity: Pupils observe the illustrations, listen to the explanations, and ask questions about each stage.

Learning Point: Image interpretation stages

Step 5: Practical Activity – Training an AI Model (Part 1)

Time: 10 minutes

Teaching Skill: Demonstration/Guided Practice

Teacher’s Activity: The teacher guides students to begin training an image classification AI model using Teachable Machine. The teacher demonstrates how to access the platform, choose “Image Project,” and start collecting data for different classes using the webcam.

Pupils’ Activity: Students follow the teacher’s instructions, access Teachable Machine, and begin collecting image data for their chosen categories (e.g., “Hand Up,” “Hand Down”).

Learning Point: Data acquisition for AI

Step 6: Practical Activity – Training an AI Model (Part 2)

Time: 5 minutes

Teaching Skill: Guided Practice/Facilitation

Teacher’s Activity: The teacher further guides students to train their model on Teachable Machine and then test its accuracy. The teacher encourages students to observe how the model makes predictions and to evaluate its performance.

Pupils’ Activity: Students complete the training process, test their models, and observe the predictions, noting the accuracy.

Learning Point: AI model training and testing

Step 7: Evaluation/Review

Time: 5 minutes

Teaching Skill: Questioning/Assessment

Teacher’s Activity: The teacher evaluates the learning by asking the following questions:

1. What is Artificial Intelligence (AI)?
2. Mention two applications of AI.
3. Explain the importance of “Data Training” in AI.
4. Briefly describe one stage of AI image interpretation.

Pupils’ Activity: Pupils answer orally and in writing.

Learning Point: Understanding AI concepts

Step 8: Note-Taking

Time: 10 minutes

Teaching Skill: Guided Writing

Teacher’s Activity: The teacher guides pupils/students to copy the essential Board Summary notes on Artificial Intelligence, its applications, key concepts, and image interpretation stages into their notebooks.

Pupils’ Activity: Pupils/students copy the notes carefully into their notebooks.

Learning Point: Recording lesson content

Step 9: Conclusion

Time: 5 minutes

Teaching Skill: Summarisation

Teacher’s Activity: The teacher summarises the key points of the lesson, reiterating the importance of AI and image recognition in modern technology and daily life. The teacher also encourages students to explore more about AI.

Pupils’ Activity: Pupils listen and ask any final questions.

Learning Point: AI concept consolidation

Continuous Assessment/Further Study

Type: Group Work/Project

Instruction: In groups of 3-4, create a digital poster or a short presentation (using tools like PowerPoint or Google Slides) explaining one of the following concepts in AI. Use simple language and include real-world examples:

1. What is Machine Learning and why is it important for AI?
2. How does an AI “Model” learn from “Data Training”?
3. The role of “Prediction” in AI systems.
4. How AI image recognition works in everyday devices (e.g., face unlock, photo tagging).

Lesson Keywords

• Artificial Intelligence (AI) – The simulation of human intelligence processes by machines.
• Machine Learning (ML) – A subset of AI that allows systems to learn from data without explicit programming.
• Model – A trained algorithm that makes predictions or decisions based on learned patterns.
• Data Training – The process of feeding data to an AI model for it to learn.
• Prediction – The output or educated guess made by a trained AI model on new data.
• Image Recognition – AI’s ability to identify objects, people, and patterns in images.
• Feature Extraction – Identifying important characteristics from data (like images).

Differentiation

For students needing support: Provide pre-selected image categories for the Teachable Machine activity and offer more direct guidance during the training process. Pair them with more proficient students for group work.

For advanced students: Challenge them to explore more complex image classification tasks on Teachable Machine or research different types of AI models (e.g., neural networks). Encourage them to think about ethical implications of AI.

Suggested Lesson Videos

For further understanding of Artificial Intelligence and Image Recognition, search on YouTube for:

• “what is artificial intelligence for students”
• “how image recognition works for beginners”
• “teachable machine tutorial for beginners”