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Artificial Intelligence

Integrating Digital
Technologies

Artificial Intelligence lesson plans

Humans display natural intelligence in contrast to machines that demonstrate artificial intelligence (AI).

AI has various definitions however for our purposes we are using the definition ‘any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals’ [1]. In addition these devices mimic human capabilities such as communication, learning and problem solving. The devices use a collection of interrelated technologies used to solve problems autonomously and perform tasks to achieve defined objectives without explicit guidance from a human being[2].

Machine learning is a subset of AI. Machine learning is commonly used to enable other fields of AI, such as Natural Language Processing and computer vision.

  • Natural Language Processing (NLP) is the ability of machines to interpret and analyse forms of human communication, such as text and speech. NLP aims to mimic human communication by teaching the machine to read, write, speak and listen by providing it lots of examples of communication data and teaching the machine by example or by letting it discover patterns on its own. NLP is used in technologies such as Google auto-complete and virtual assistants.

    Computer vision is the ability of machines to recognise objects in images or videos. Computer vision aims to mimic human vision by teaching the machine with lots of examples of labelled images or to discover patterns found in images on its own. Examples of computer vision include face tagging on social media photos and vision used by self-driving cars.

Machine learning is a process of achieving Artificial Intelligence. To train a machine we give it a large number of examples of data demonstrating what we would like it to do so that the machine can figure out how to do it on its own. The system learns from patterns. For example, by giving the machine lots of examples of images of cats and dogs, it can recognise a cat or dog without explicit instructions, instead using patterns and inference. Machine learning algorithms primarily uses two approaches to achieve intelligence known as supervised learning and unsupervised learning.

  • Supervised learning is a technique used in machine learning. In this process the human provides the machine with lots of examples of what it is we are wanting it to identify along with a label. By providing lots of examples (repetition), the machine is learning about it (the features) in order to make judgements on its own. Supervised learning involves the classification of data - classifying data based on its features into binary categories (e.g. cats and dogs) or multiple categories (e.g. apple, banana, and orange).

    • Classification is a supervised learning technique used to group data based on attributes or features. Humans can provide labels on the data (e.g. for images or text) that tells the machine about the attributes or features (e.g. colour, size, shape, measurements) in each data and how to group the data. The machine then matches future data based on the similarity of the new data to pre-defined groups. For example, sorting images of kangaroos and wombats based on their number of legs, having a tail or no tail and size of ears.

      Regression is a supervised learning technique used to figure out where data fits along a continuous form (e.g. speed of a car). In contrast to classification where data is assigned to a group (e.g. speed as slow, medium, fast), regression focuses on predicting how data fits in numerical form (e.g. speed in numerics). Humans provide labelled information about features that might be relevant about a given thing (e.g. the height and weight of a runner). The machine takes this data and makes predictions about where something fits along a continuum (e.g. the speed of the runner in comparison to others). Examples of regression include forecasting local weather based on rainfall, humidity, temperature or predicting house prices based on the number of rooms, locality and ease of transport.

  • Unsupervised learning involves providing the machine with a large amount of data and letting it find patterns in the data on its own. The machine then determines the categories. This process is known as clustering - the grouping of similar objects in one category.

    • Clustering is an unsupervised learning technique used to allow the computer to naturally discover patterns in data (e.g. for images or text). The machine groups the data based on the common attributes or features it finds in the data. For example, clustering is used to determine what people might like to watch next based on what they have viewed previously (e.g. YouTube or Netflix).

Deep learning is part of a family of machine learning methods based on artificial neural networks (ANNs). These are inspired by the workings of neural networks in the human brain and process data with structures similar to those in biological brains. In doing so, artificial neurons (called perceptrons) form networks and interact with each other to collectively learn and solve problems. ANNs learn in a similar fashion like we humans do. They use a feedback loop to make adjustments when they make a mistake. This technique is used in Google’s Alpha Go and in self-driving cars.

1 Poole, Mackworth & Goebel 1998,

2 Artificial Intelligence: Solving problems, growing the economy and improving our quality of life, Australian Government 2019

The following lesson ideas cover a range of specialisations and subsets as indicated by the colour coding. Click on the coloured squares to learn more about each definition.

Machine
learning

Supervised
learning

Natural
language
processing

Computer
vision

Classification


Clustering


Regression


F-2

Machine learningSupervised learningComputer visionClassification



Can an AI guess your emotion?

An artificial intelligence (AI) can use a combination of computer vision and classification to extract features from an image. Discuss emotions as a class, and introduce the idea of AI using a fun, easy to use AI tool.

Machine learningSupervised learningComputer visionClassification



Can AI recognise what you are drawing?

This lesson provides an opportunity to incorporate representation of data using a relevant context being studied in the classroom. Students represent an object using a line drawing, focusing on the features of the object that enable it to be easily recognised.

3-4

Machine learningSupervised learningComputer visionClassification



Can an AI guess your emotion?

An artificial intelligence (AI) can use a combination of computer vision and classification to extract features from an image. Discuss emotions as a class, and introduce the idea of AI using a fun, easy to use AI tool.

Machine learningNatural Language ProcessingClustering



Fun projects with language translation

Natural Language Processing is growing in importance. This type of AI interprets text and speech. It can be used in translating a language. Choose from three projects that explore this type of AI catering for student interest and programming skills.

Machine learningSupervised learningClassificationClustering



Note the music

An AI using the technique of clustering, looks for patterns in data, in this case the data is musical notes. Students can hard code a program that plays a particular note for a set beat (non-AI) or instead they can incorporate the random function to mimic AI clustering.

Machine learningSupervised learningComputer visionClassification



How can AI recognise what it see?

This lesson is an introduction to the way in which a computer sees. It focuses on image recognition that involves feature extraction, object detection and classification.

5-6

Machine learningSupervised learningNatural Language ProcessingComputer visionClassificationClustering



Recognising AI

Use the tasks in this lesson to introduce concepts that underpin artificial intelligence (AI). The majority of the tasks are unplugged (do not require a digital device).

Machine learningClassificationClustering



Note the music

An AI using the technique of clustering, looks for patterns in data, in this case the data is musical notes. Students can hard code a program that plays a particular note for a set beat (non-AI) or instead they can incorporate the random function to mimic AI clustering.

Machine learningSupervised learningNatural Language ProcessingComputer visionClassification



AI smartphone security

Biometrics is a form of artificial intelligence that is used to protect users from unauthorised access to their digital devices. Students hard code a program that unlocks a phone using a PIN or they can create a program that incoporates an AI model.

Machine learningSupervised learningNatural Language ProcessingClassification



Home automation with AI

Home automation can take your voice commands using speech recognition AI as you talk to your mobile phone to control the lights, the fan, the air conditioner, or other smart devices. Students investigate the control required to switch lights and fans on or off through an artificial neural network.

Machine learningClassificationClustering



Home automation programming

Investigate home automation systems, including those powered by artificial intelligence (AI) with speech recognition capability. Selecte from tasks that cater for students’ range of programming skills.

Machine learningSupervised learningNatural Language ProcessingClassification



Can a computer recognise your sentiment?

Natural Language Processing can interpret and categorise a user’s online comments using the technique of classification. Students hard code a program using if/then statements or they can create a program that incoporates an AI model.

Machine learningSupervised learningNatural Language ProcessingClassification



Data bias in AI

Artificial intelligence can sometimes be biased to certain shapes or colours. When such AI systems are applied to situations that involve people, then this bias can manifest itself as bias against skin colour or gender. This lesson explores bias in AI.

Machine learningSupervised learningNatural Language ProcessingClassification



Anti-bullying AI

Natural Language Processing interprets text and speech. Explore an Artificial Intelligence application that simulates checking text say for example those from a social media post.

Machine learningSupervised learningNatural Language Processing



Fun projects with language translation

Natural Language Processing is growing in importance. This type of AI interprets text and speech. It can be used in translating a language. Choose from three projects that explore this type of AI catering for student interest and programming skills.

Machine learning



Analysis of AI applications, drawing on ethical understanding

This lesson plan explores the ethical aspects of artificial intelligence and the implications on our future lives.

7-8

Machine learningSupervised learningNatural Language ProcessingComputer visionClassificationClustering



Recognising AI

Use the tasks in this lesson to introduce concepts that underpin artificial intelligence (AI). The majority of the tasks are unplugged (do not require a digital device).

Machine learningSupervised learningNatural Language ProcessingClassification



Home automation with AI

Home automation can take your voice commands using speech recognition AI as you talk to your mobile phone to control the lights, the fan, the air conditioner, or other smart devices. Students investigate the control required to switch lights and fans on or off through an artificial neural network.

Machine learningSupervised learningNatural Language ProcessingClassification



Home automation: General purpose programming

Investigate home automation systems, including those powered by artificial intelligence (AI) with speech recognition capability. These suggested activities for year levels 7-8 are designed for students using general purpose programming languages JavaScript and Python.

Machine learningSupervised learningNatural Language ProcessingClassification



Anti-bullying AI

Natural Language Processing interprets text and speech. Explore an Artificial Intelligence application that simulates checking text say for example those from a social media post.

Machine learningSupervised learningNatural Language ProcessingComputer visionClassification



AI smartphone security

Biometrics is a form of artificial intelligence that is used to protect users from unauthorised access to their digital devices. Students hard code a program that unlocks a phone using a PIN or they can create a program that incoporates an AI model.

Machine learningComputer visionClassification



AI image recognition – exploring limitations and bias

Practise training and testing an artificial intelligence (AI) model, using cartoon faces, including a discussion about sources of potential algorithmic bias and how to respond to these.

Machine learningSupervised learningNatural Language Processing



Coding a sentimental chatbot in Python

Natural Language Processing (NLP) interprets text and speech. Chatbots provide a useful context to explore NLP. In this module students code a chatbot in Python, a conversational program capable of responding in varied ways to user input, including with the use of smart sentiment analysis.

Machine learningSupervised learningNatural Language Processing



Book analysis with AI techniques

Explore text analysis through Natural Language Processing, a significant application of Artificial Intelligence. View a series of video tutorials to develop a Python program that can break down and analyse the content of a complete text, such as Robert Louis Stevenson's Treasure Island, and use smart sentiment analysis to attempt to determine the villain(s) and hero(s).

Machine learning



Analysis of AI applications, drawing on ethical understanding

This lesson plan explores the ethical aspects of artificial intelligence and the implications on our future lives.

Machine learningSupervised learningNatural Language ProcessingClassification



Data bias in AI

Artificial intelligence can sometimes be biased to certain shapes or colours. When such AI systems are applied to situations that involve people, then this bias can manifest itself as bias against skin colour or gender. This lesson explores bias in AI.

9-10

Machine learningSupervised learningNatural Language Processing



Coding a sentimental chatbot in Python

Natural Language Processing (NLP) interprets text and speech. Chatbots provide a useful context to explore NLP. In this module students code a chatbot in Python, a conversational program capable of responding in varied ways to user input, including with the use of smart sentiment analysis.

Machine learningSupervised learningNatural Language Processing



Book analysis with AI techniques

Explore text analysis through Natural Language Processing, a significant application of Artificial Intelligence. View a series of video tutorials to develop a Python program that can break down and analyse the content of a complete text, such as Robert Louis Stevenson's Treasure Island, and use smart sentiment analysis to attempt to determine the villain(s) and hero(s).

Machine learningNatural Language ProcessingComputer visionClassificationClustering



What would my preferred AI future look like?

Malyn Mawby, Head of Personalised Learning at Roseville College, explains how she implemented project-based learning (PBL) with her year 10 class to explore Artificial Intelligence (AI). Through the PBL task, students selected an area of interest and investigated what is possible, probable, and preferred.

Machine learningNatural Language ProcessingComputer visionClassificationClustering



AI ethics – What's possible, probable, and preferred?

The development and ubiquity of Artificial Intelligence raise a number of social and ethical matters that students can explore in the Digital Technologies classroom. This lesson idea outlines a project to help students frame such discussions using the curriculum Key Idea of Creating preferred futures, tying into Critical and Creative Thinking.