Introduction: AI for Everyone

AI can examine large datasets, find relationships or patterns, and produce a prediction or identification to support human action. Option A uses attendance and performance patterns to identify students who might need help. Option B only stores data, C ignores individual evidence, and D concerns network performance rather than intelligent data analysis.

In AI, learning generally means using data, examples, or experience to detect useful patterns and improve predictions or decisions on future cases. Option A captures this process. A system that only follows fixed instructions is not learning from experience, and removing rules is not learning. Memorising examples without generalising fails when a new but similar case appears.

Scholarship decisions can strongly affect students, and an AI tool may reflect errors or bias in its data or design. The school should examine the criteria, review recommendations, allow human consideration of context, and provide a fair process. Option B includes human oversight. Blind acceptance, public disclosure, or automatic rejection can produce unjust outcomes.

People are more likely to trust AI when they understand its purpose and limitations and when its outputs can be checked against evidence. Transparency helps reveal how a system is used, while verification helps detect errors or bias. Option A combines both practices. Hiding problems, using AI aimlessly, or exposing private data weakens responsible trust.