What is AI and how do we use it in everyday life?

Target audience – Secondary classes

Age Group – 11-17

Short overview of scenario

Artificial Intelligence (AI) and its multiple sub-domains are being increasingly employed in various industries and businesses to aid in repetitive processes. But there has been a burgeoning interest from established tech giants and startups in using AI to make everyday life a walk in the park. AI has been highly instrumental in optimizing the way we entertain ourselves, interact with our mobile devices, to even drive vehicles for us. We tend to encounter Machine Learning (ML) algorithms and Natural Language Processing (NLP) in several everyday tasks more than we know.

Scenario description

The students will learn about the two Categories for AI Usage, and also will learn how to classify the way AI is utilized. This will be done by examining 10 examples of Applications of AI Capabilities in Everyday Life. 

Scenario Objectives

  • Learn how to distinguish the categories of AI systems.
  • Classify the way AI is utilized. 
  • Students finally will have to search for AI systems they use in everyday life. 


Students should be familiarized with technology aspects as Voice Assistants, Streaming Apps etc.

Outline plan 

ActivityExamining examples of AI systems
Timing60 min
Methodsinquiry learning and discovery learning
What the tutor is doingThe tutor gives the students examples of AI systems in everyday life. Those can be classified according the way AI is utilized to improve the functionalities of everyday life into two broad streams.
i) Software/Methodology: 
Prominent examples of AI software used in everyday life include voice assistants, image recognition for face unlock in mobile phones, and ML-based financial fraud detection. AI software usually involves just downloading software with AI capabilities from an online store and requires no peripheral devices.
The hardware side of AI includes its utilization in drones, self-driven vehicles, assembly-line robots, and the Internet of Things (IoT). This involves the design of specific devices that are based on AI capabilities.
What the learners are doingThe learners will have to distinguish the categories of those AI systems and find their differences.
Equipment and SupportProjector or TV
Link to AI@School CurriculumAI Literacy 
Assessment of/for learningStudents will have to find examples of AI systems in real life and classify them.
Resources/links/relevant content/ExamplesList of examples the tutor can use in classroom

1) Voice Assistants
Digital assistants like Siri, Google Home, and Alexa use AI-backed Voice User Interfaces (VUI) to process and decipher voice commands. AI gives these applications the freedom to not solely rely on voice commands but also leverage vast databases on cloud storage platforms. These applications can then parse thousands of lines of data per second to complete tasks and bring back tailored search engine results. 
There is a sweeping change in consumer awareness and an increase in comfort levels with this technology. Voice assistant interfaces are rapidly advancing, especially finding use in healthcare to identify certain diseases through vocal biomarkers. Voice-based chatbots are also being integrated into telehealth applications for triage and screening.

2) Entertainment Streaming Apps
Streaming giants like Netflix, Spotify, and Hulu are continually feeding data into machine learning algorithms to make the user experience seamless. 
Carefully looking at the user’s interaction with various media, these streaming apps recommend custom content. Using AI to parse through the ever-increasing user data, these apps create catalogs of music, movies, and TV series suited to each individual user’s liking. 
AI also plays an important role in providing uninterrupted streaming by automating the allocation of servers closest to the user. Bandwidth allocation also changes automatically based on the popularity of a particular piece of media.

3) Personalized Marketing
Brands use AI-driven personalization solutions based on customer data to drive more engagement. A report compiled by OneSpot Research revealed that 88% of the surveyed consumers stated that more personalized content makes them feel better about a brand.
Consumers are more likely to make a purchase when they are directed to products through personalized marketing via automated e-mails and feedback forms. Recent innovations with AI claim to use computer vision to predict an advertisement’s performance, helping brands reach the right people and serving those who need those products. AI marketing apps help both prospects and retargeted customers, depending on the marketing stage.

4) Smart Input Keyboards
The latest versions of mobile keyboard apps combine the provisions of autocorrection and language detection to provide a user-friendly experience. 
With the help of AI, these apps can efficiently correct mistakes, help switch between languages, and predict the next word in a non-intrusive manner. Utilizing the principle of the “random forest” machine learning algorithm, AI programmers are teaching these apps to understand the context of the message being typed and make accurate predictions. 
Apps like Typewise and Swiftkey are now integrated with over 300 languages and dialects. Added facilities like real-time translation and integrated search engines are also being introduced lately.

5) Navigation and Travel
The work of AI programmers behind navigation apps like Google Maps and Waze never ends. Yottabytes of geographical data which is updated every second can only be effectively cross-checked by ML algorithms unleashed on satellite images.
Recently, researchers at MIT developed a navigation model that tags road features in digital maps, all in real-time. These digital maps are also created simultaneously based on satellite imagery incorporating information about cycling lanes and parking spots.
Imaging algorithms built on Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN) have simplified regular updates in routes. AI also helps ascertain routes on satellite images covered by natural overgrowth with the help of predictive models.

6) Gamified Therapy
AI had found a place in gaming from the time classics such as Pac-Man and Pong were around for intuitive universe-building. However, until now innovations in gaming AI have focused on presenting more interesting challenges to the gamer and not on gauging the gamer’s mindset.
Gamified applications are now being designed to gauge the gamer’s mental fortitude in the face of certain defeat. This is helping to study methods to mitigate depression and anxiety in gamers and people in general.
Using Virtual Reality (VR) headsets, some of these gaming applications provide Cognitive Behavioral Therapy (CBT) for enhanced engagement with the person. AI helps these games to adapt to the user’s behavior based on examined stimuli as the game progresses.

7) Self-driving Vehicles
The technology of Autonomous Vehicle AI is witnessing large-scale innovation driven by global corporate interest. AI is making innovations beyond cruise-control and blind-spot detection to include fully autonomous capabilities.
Deep Reinforcement Learning (DRL), a subset of machine learning, is being applied to teach vehicles to operate independently. Path planning in the face of static and dynamic obstacles is being made possible through various predictive AI models.
 Predicting accurately when neighboring vehicles will swerve and other such unforeseen events are being taken into account. Simultaneous Localization and Mapping (SLAM) is the technology that makes this possible with real-time orientation to surroundings via sensors.

8) Facial Recognition Technologies
The most popular application of this technology is in the Face ID unlock feature in most of the flagship smartphone models today. The biggest challenge faced by this technology is widespread concern around the racial and gender bias of its use in forensics.
Generative Adversarial Neural Networks (GANN) are being applied to reduce the margin of error in facial recognition software. These neural networks are also being trained to pinpoint the unethical use of Deepfake technology.
Various industries are also developing AI software that picks up facial expressions to identify mood and intention. Emotion AI or Affective Computing is an emerging area of interest to gauge customer experience.

9) Security and Surveillance
It is nearly impossible for a human being to keep a constant eye on too many monitors of a CCTV network at the same time. So, naturally, we have felt the need to automate such surveillance tasks and further enhance them by leveraging machine learning methodologies.
AI frees up human surveillants to focus on the verification of crucial incidents and act upon them. The constant monitoring and detection part of surveillance is taken up by AI video monitoring software. AI can pick up irregular behavior that might sometimes be missed by human eyes.
An extension of AI-based facial recognition software is also being employed in surveillance systems of high-risk public places like government buildings. Currently, liberal governments are finding ways to reduce the risk of privacy breaches from AI surveillance.

10) Internet of Things
The confluence of AI and the Internet of Things (IoT) opens up a plethora of opportunities to develop smarter home appliances that require minimal human interference to operate. While IoT deals with devices interacting with the internet, the AI part helps these devices to learn from data.
The five broad steps involved in IoT-enabling are – create, communicate, aggregate, analyze and act. The efficacy of the ultimate step, “act”, is based on the depth of analysis, and AI adds tons of value to it.
AI unlocks the potential of the data aggregated by IoT devices via sensors. Learnings from this data over multiple iterations enable IoT devices to respond to human stimuli and requirements better.

Our notes from practice

This scenario can and should be used in a secondary classroom. The students should already have some familiarization with technology and they should be capable of using technology devices.


Curriculum Constructs


1.3 Reflective Practice

3.2 Guidance

3.3 Collaborative Learning

4.2 Communication

Bloom’s Taxonomy

Knowledge: Recognize, Describe

Comprehension Explain, Distinguish, Give examples: 

AI@School Curriculum Area

The main terms and technologies used in AI – To raise awareness about the presence of AI in our society

AI and existing technologies – Understand and recognise where AI is already commonly used


Authors; Attwell. G., Bekiaridis. G., Blatsios, S., De Smet, G., Gerrard, A., Orcasitas-Vicandi, M., Rennie, I., Roman, G., Sapountzi, M., Schevernels, M. 

AI@School is a project co-funded by the European Commission via Erasmus+

Creative Commons LicenceThis publication reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

AI@School Scenarios by http://aiatschool.eu is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.Permissions beyond the scope of this license may be available at http://pontydysgu.eu.

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