From the lab to the real world, discover how data, algorithms, and people work together to create the AI tools that solve real-world problems and expand human creativity.
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Students learn how AI systems perform tasks that typically require human thinking — like recognizing patterns, making decisions, and understanding language — by learning from large amounts of data rather than following rigid scripts. They discover that algorithms are the step-by-step instructions at the heart of every AI system.
Students learn that every AI system is built on three essential building blocks: data (large, organized collections of information), models (which learn patterns from data), and computing power (special hardware that trains and runs AI). They explore how the size and quality of a dataset directly affects AI performance.
Students learn how AI improves through reinforcement learning — a process of trying things, getting feedback, and gradually getting better — and how simulations provide safe, virtual environments where AI can practice tasks and make mistakes without causing real-world harm.
Students learn how User Experience (UX) and design shape the way people interact with AI products. They discover that good design makes technology easy to understand, enjoyable to use, and inclusive for as many people as possible — and that testing with real users and gathering feedback is essential.
Students learn how AI agents can independently take actions — like browsing websites, filling out forms, and completing multi-step tasks — and how automation handles repetitive work so humans can focus on creative thinking, complex problem-solving, and decisions that require human judgment.
Students learn why ethics and guardrails are essential for building safe, fair, and beneficial AI systems. They discover that humans must stay in the loop — reviewing AI recommendations and making final decisions — and that careers in AI policy, law, and social science are just as important as technical roles.
Students learn that AI needs many different talents — engineers, designers, researchers, data scientists, and many others all work together to build AI. They reflect on the overarching question: “What has this tour taught you about AI, and how might the careers you’ve seen today connect to your own interests and future?” The key takeaway: there is no single path into AI. Curiosity, creativity, problem-solving, and teamwork can be just as important as technical skills — and the AI of the future will be built by their generation.
Go behind the scenes with Amazon Future Engineer to discover how data science, artificial intelligence, and creative problem-solving come together to build intelligent systems that shape the future! From massive datasets and powerful hardware to AI agents that act on your behalf, see how technology expands human potential in ways you never imagined.
On this tour, students learn the answer to the following questions:
🧠 How do AI systems use algorithms and massive amounts of data to learn patterns, make decisions, and solve complex problems - without being told every step?
🖥️ How do datasets, hardware, and computing power work together as the essential building blocks that make it possible to create and run AI systems?
🎨 How do UX designers make sure AI tools feel easy, enjoyable, and trustworthy for the real people who rely on them every day?
🤖 How do engineers use reinforcement learning and simulations to teach AI systems to improve over time - practicing safely in virtual environments before entering the real world?
📱 How are AI agents and automation changing the way we work by taking on real tasks, browsing the web, and carrying out multi-step instructions on their own?
⚖️ How do teams build ethics and guardrails into AI systems to make sure they are safe, fair, and used in ways that benefit people rather than harm them?
In this tour, students explore the following careers: Applied AI Architect, Product Designer (UX Lead), Technical Project Manager, Technical Program Manager, Engineering Manager, Risk Manager
Aligned to Standards
The interactive tour is aligned to Common Core ELA, Common Core Math, Next Generation Science Standards (NGSS) and CSTA K-12 Computer Science Standards. The Teacher Toolkit includes a facilitation guide, worksheets, and other resources to support learning during the tour. Recommended for grades 6-10.
Careers Behind the Code Tour is aligned to the following standards:
CSTA Standards:
1B-IC-18: Discuss computing technologies that have changed the world, and express how those technologies influence, and are influenced by, cultural practices.
2-IC-20: Compare tradeoffs associated with computing technologies that affect people’s everyday activities and career options.
3A-CS-02: Explain how data is stored and processed in computing systems, and identify factors that affect how reliably data is transmitted and interpreted.
3A-DA-09: Refine computational models based on the results of testing the models on real-world data.
3A-AP-13: Create prototypes that use algorithms to solve computational problems, considering technical and usability constraints.
3A-AP-16: Design and iteratively develop computational artifacts for practical intent, personal expression, or to address a societal need.
3A-AP-17: Systematically test and refine programs using a range of test cases, based on anticipating common errors and corner cases.
3A-IC-24: Evaluate the ways computing impacts personal, ethical, social, economic, and cultural practices.
3A-IC-25: Evaluate the beneficial and harmful effects of computing innovations in order to make informed decisions about their use.
3A-IC-26: Demonstrate ways that people participate in problem-solving processes at scale and how computing enhances human capabilities.
3A-IC-27: Predict how computational innovations that have revolutionized aspects of our culture might evolve; analyze tradeoffs for individuals and society.
3A-IC-28: Evaluate career paths in computing fields and analyze the skills and education needed to pursue them.
Next Generation Science Standards (NGSS)
HS-ETS1-1: Analyze complex real-world problems, considering criteria, constraints, societal implications, and ethical dimensions of technology design.
HS-ETS1-2: Design solutions to complex problems by breaking them into smaller components (datasets as essential inputs for AI).
HS-ETS1-3: Evaluate solutions to a complex real-world problem based on prioritized criteria and trade-offs (e.g., efficiency vs. human oversight in automation).
HS-ETS1-4: Use a computer simulation to model the impact of proposed solutions to a complex real-world problem.
HS-ETS2-1: Analyze how technology influences society and careers, and how the needs of society shape technological development.
Crosscutting Concepts: Systems and system models (how AI functions as an interconnected system of data, algorithms, and hardware).
Crosscutting Concept: Scale, proportion, and quantity (understanding how dataset size affects AI performance).
Science and Engineering Practice: Using mathematics and computational thinking to analyze and predict outcomes.
Common Core Standards
MP.1: Make sense of problems and persevere in solving them (analyzing how AI uses algorithms to solve complex tasks).
MP.3: Construct viable arguments and critique the reasoning of others (evaluating the fairness of AI systems and their decision processes).
MP.4: Model with mathematics (understanding how AI agents use models to make decisions in real-world digital environments).
MP.6: Attend to precision in mathematical communication and calculation (designing clear, accurate AI interfaces).
CCSS.MATH.CONTENT.6.SP.A.1: Recognize statistical questions and the role of data collection in understanding real-world phenomena.
CCSS.MATH.CONTENT.7.SP.A.2: Use data from a random sample to draw inferences (parallel to how reinforcement learning uses feedback data).
CCSS.ELA-LITERACY.RI.6-8.3: Analyze how a text makes connections among and distinctions between individuals, ideas, or events.
CCSS.ELA-LITERACY.RI.6-8.4: Determine the meaning of domain-specific words and phrases as they are used in a text.
CCSS.ELA-LITERACY.RI.6-8.6: Determine an author’s point of view or purpose; analyze how the author acknowledges and responds to conflicting evidence.
CCSS.ELA-LITERACY.RI.6-8.7: Integrate information presented in different formats (comparing datasets to traditional research methods).
CCSS.ELA-LITERACY.RI.6-8.8: Trace and evaluate the argument and specific claims in a text (analyzing how AI agents are presented in media and industry).
CCSS.ELA-LITERACY.SL.6-8.1: Engage in collaborative discussions about how technology shapes culture and careers.
CCSS.ELA-LITERACY.SL.6-8.4: Present claims and findings, emphasizing relevant evidence to support main ideas (constructing arguments about AI ethics).
CCSS.ELA-LITERACY.SL.6-8.5: Use multimedia components to clarify information and add interest.
CCSS.ELA-LITERACY.W.6-8.2: Write informative explanatory texts to examine a topic and convey ideas (writing about AI careers explored on the tour).
CCSS.ELA-LITERACY.W.6-8.4: Produce clear and coherent writing appropriate to task and audience.
Play it on Kahoot!
The fun, game-like tour will be available for FREE on Kahoot! to all interested classrooms and families. Students can play against each other while learning about getting data from the stadium to devices, troubleshooting, and creating new features to enhance sports streams. No account required.
Teacher Toolkit
Our Teacher Toolkit provides educators with guides, worksheets, and other documents aligned to CSTA K-12 Computer Science Standards, Common Core, and Next Generation Science Standards (NGSS). Use these materials to discover all the possibilities with computer science learning and careers of the future, and to set students up for success before, during, and after the tour.
Student Worksheet
Distribute graphic organizers for students to capture their biggest learnings and wonderings on the tour.
Student WorksheetDistribute graphic organizers for students to capture their biggest learnings and wonderings on the tour.
Key Student LearningsDiscover the vocabulary, key points, and standards covered on the tour by tour stop.
Facilitation GuideRead instructions for setting up the Kahoot! tour with classes. Slide by slide script for delivering the tour to students.
Discussion QuestionsDeepen student engagement by using these prompts to spark written reflections or classroom discussions after each stop or upon completing the tour.
CertificatePrint and distribute student certificates to celebrate completing the tour!
