Coding · 2nd Year · Ethics, Data, and the Future of Coding · 4.º Período

Artificial Intelligence and Society

Explore the historical development of artificial intelligence and debate its current and future impact on employment, bias, and society.

TL;DR:Artificial Intelligence (AI) is no longer science fiction; it is a part of our daily lives. This topic explores the history of AI research, from the Turing Test to modern Large Language Models. Students investigate how AI can be used for good, but also the ethical risks it poses, such as algorithmic bias and the displacement of jobs.

• NCCA Curriculum Specifications
• NCCA Junior Cycle Short Course in Coding, Strand 1: Computer science introduction
• NCCA Junior Cycle Short Course in Coding, Strand 1: Computer science introduction - Computing and society

About This Topic

Artificial Intelligence (AI) is no longer science fiction; it is a part of our daily lives. This topic explores the history of AI research, from the Turing Test to modern Large Language Models. Students investigate how AI can be used for good, but also the ethical risks it poses, such as algorithmic bias and the displacement of jobs.

Aligned with the NCCA's 'Computing and Society' strand, this unit encourages students to think critically about the future they will inherit. They learn that AI is only as good as the data it is trained on. Students grasp this concept faster through structured discussion and peer explanation of how an AI might 'learn' a prejudice from a biased dataset.

Key Questions

1. What are the historical milestones of AI research?
2. How might AI change the future of work and the economy?
3. What ethical guidelines should govern AI development to prevent bias?

Watch Out for These Misconceptions

Common Misconception

AI is 'smart' like a human being.

What to Teach Instead

Students often anthropomorphize AI. Use hands-on modeling to show that AI is actually just very complex pattern matching and probability, not 'thinking' in the human sense.

Common Misconception

AI is always objective and neutral.

What to Teach Instead

Many believe machines can't be biased. Peer-led investigations into biased facial recognition software help students see that AI often mirrors the flaws of its human creators.

Active Learning Ideas

Simulation Game

Training a 'Human' AI

One student acts as an AI that only knows what the class 'feeds' it. The class provides data about 'what a scientist looks like,' and they analyze if the resulting 'AI' is biased.

40 min·Whole Class

Formal Debate

AI and the Future of Work

Students debate whether AI will create more jobs than it destroys. They must research specific industries like healthcare, transport, and the creative arts.

45 min·Whole Class

Inquiry Circle

AI Ethics Board

Groups are given a scenario (e.g., an AI that decides who gets a bank loan) and must write a set of ethical guidelines to ensure the system is fair and transparent.

50 min·Small Groups

Frequently Asked Questions

What are the historical milestones of AI research?

Key milestones include the 1956 Dartmouth Conference where the term 'AI' was coined, IBM's Deep Blue beating Garry Kasparov at chess in 1997, and the recent explosion of generative AI like ChatGPT.

How might AI change the future of work?

AI will likely automate repetitive tasks, but it will also create new roles in AI ethics, data curation, and human-AI collaboration. The NCCA Coding course prepares students for this by focusing on high-level problem-solving.

What ethical guidelines should govern AI?

Common guidelines include transparency (knowing how an AI made a decision), accountability (who is responsible when AI fails), and fairness (ensuring the AI doesn't discriminate).

How can active learning help students understand AI?

AI can feel like 'magic' to students. Active learning strategies, like 'unplugged' machine learning activities where students manually sort data to 'train' a paper algorithm, demystify the process. When they see how a small error in their training data leads to a wrong result, they truly understand the importance of data quality and ethical design.