User Experience and Human Centered Design · Term 4

The Future of Work

Analyzing which industries are most susceptible to automation and what new roles might emerge.

About This Topic

The Future of Work topic has Year 10 students analyze industries vulnerable to automation, such as manufacturing and logistics, where repetitive tasks suit algorithms and robots. They also identify emerging roles like AI ethics officers and data curators, which demand human judgment. This content directly supports AC9DT10K01 by examining digital systems' impacts on current and future societal needs, including employment structures.

Students tackle key questions on technology's role in wealth distribution, where productivity gains concentrate among tech owners; vital skills like critical thinking and collaboration in AI-driven economies; and whether universal basic income counters widespread job loss. These discussions build systems thinking and ethical reasoning, essential for user-centered design units.

Active learning excels in this topic because future scenarios feel distant and speculative. Simulations of automated job markets or structured debates on policy responses let students test ideas collaboratively, gather evidence from real data, and refine arguments through peer feedback. This approach turns passive prediction into engaged foresight, making concepts relevant and memorable.

Learning Objectives

• Analyze the impact of automation on specific industries, classifying them by their susceptibility to job displacement.
• Evaluate the ethical implications of AI-driven wealth distribution and propose potential societal responses.
• Synthesize information to predict emerging job roles and the skills required to fill them in an AI-augmented economy.
• Critique the potential effectiveness of universal basic income as a solution to widespread automation-induced unemployment.

Before You Start

Digital Systems and Society

Why: Students need a foundational understanding of how digital technologies operate and their broad societal influence before analyzing specific impacts on work.

Economic Principles

Why: A basic grasp of concepts like supply, demand, and labor markets is necessary to understand wealth distribution and the economic effects of automation.

Key Vocabulary

Automation	The use of technology, such as robots and artificial intelligence, to perform tasks previously done by humans.
Artificial Intelligence (AI)	Computer systems designed to perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.
Job Displacement	The loss of employment for workers when their jobs are eliminated due to technological advancements or economic changes.
Universal Basic Income (UBI)	A government program that provides a regular, unconditional sum of money to all citizens, regardless of their employment status or income level.
Gig Economy	A labor market characterized by the prevalence of short-term contracts or freelance work, often facilitated by digital platforms.

Active Learning Ideas

Jigsaw: Industry Automation Risks

Assign small groups one industry, such as retail or healthcare. They research automatable tasks using provided sources, note resilient human roles, and create infographics. Groups then teach their findings to the class, who compile a shared matrix of risks and opportunities.

⏱ 50 min·Small Groups

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Pairs Pitch: Emerging Future Jobs

In pairs, students brainstorm three new roles from AI advancements, like virtual reality therapists. They outline required skills and societal benefits, then pitch to the class for feedback and voting on viability.

⏱ 30 min·Pairs

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Debate Carousel: UBI and Wealth Gaps

Divide class into affirm/negate teams on 'Universal basic income is essential for automation.' Teams rotate stations to argue, rebut, and note evidence from wealth distribution data. Conclude with a class vote and reflection.

⏱ 45 min·Whole Class

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Simulation Game: AI Economy Shifts

Small groups manage a simulated economy with cards representing jobs, automation events, and skill upgrades. They track wealth changes over rounds, discuss outcomes, and propose adaptations like retraining programs.

⏱ 40 min·Small Groups

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Real-World Connections

Amazon's fulfillment centers utilize a significant number of robots to assist human workers with tasks like picking and packing, illustrating automation in logistics and the potential for job reallocation.

The rise of autonomous vehicle technology, being tested by companies like Waymo and Tesla, directly impacts professions such as truck drivers and taxi operators, prompting discussions about future employment needs.

The development of AI-powered diagnostic tools in healthcare, such as those used by radiologists to analyze medical images, highlights how technology can augment, rather than replace, certain skilled professions.

Watch Out for These Misconceptions

Common Misconception

Automation will eliminate all jobs forever.

What to Teach Instead

Technology historically displaces some jobs but creates more overall, as seen with the internet boom. Jigsaw activities where groups share historical examples help students build evidence-based views, replacing fear with patterns of adaptation.

Common Misconception

Only low-skill manual jobs face automation risks.

What to Teach Instead

Routine cognitive tasks in accounting or law are also vulnerable to AI. Paired case studies across sectors reveal broad impacts, prompting students to reassess assumptions through comparative discussion.

Common Misconception

Future economies need no new human skills.

What to Teach Instead

AI amplifies needs for creativity, empathy, and ethics. Skill-mapping exercises in simulations let students identify personal strengths, fostering proactive mindset shifts via hands-on application.

Assessment Ideas

Discussion Prompt

Pose the question: 'Which three jobs do you believe are most vulnerable to automation in the next 20 years, and why?' Students should provide specific reasons, referencing task types and technological capabilities, and then discuss potential new roles that might emerge in those sectors.

Quick Check

Provide students with a list of 10 job titles. Ask them to categorize each job as 'High Risk of Automation', 'Medium Risk', or 'Low Risk', and for two of their choices, write a brief justification explaining their reasoning based on task repetitiveness and AI capabilities.

Exit Ticket

On an index card, have students write one skill they believe will be essential for future employment in an AI-dominated economy and explain in one sentence why that skill is important. They should also list one potential societal challenge arising from widespread automation.

Frequently Asked Questions

How does automation affect wealth distribution in society?

Automation boosts productivity but often funnels gains to capital owners, widening inequality as displaced workers face wage stagnation. Students explore this through data on firm profits versus worker pay. Discussions link to Australian contexts like mining robotics, emphasizing policy needs like retraining taxes for equitable sharing.

What skills will be most valuable in an AI-dominated economy?

Core skills include adaptability, problem-solving, emotional intelligence, and digital ethics, which AI struggles to replicate. Curriculum activities like job pitches help students self-assess and plan development. Real-world examples from reports like those from ACARA highlight interdisciplinary abilities over rote tasks.

How can active learning help students understand the future of work?

Active strategies like debates and simulations make abstract trends tangible: students role-play job losses and inventions, debate UBI with data, and track simulated economies. This builds empathy for affected workers, sharpens argumentation, and reveals skills gaps personally. Peer teaching in jigsaws ensures deep processing over rote memorization.

Is universal basic income a realistic response to automation?

UBI trials in places like Finland show potential to reduce poverty amid job shifts, but critics cite work disincentives and funding challenges. Class debates with Australian cost models encourage balanced views. Ties to curriculum by weighing tech ethics against economic design for human-centered outcomes.