If you want students to really get into AI, it makes a big difference to keep tests and assignments grounded in the real world. Rather than just throwing abstract questions or random coding tasks at them, it’s better to give them things that feel practical—like working with real data, solving everyday problems, or thinking through the ethics of how AI is used. When they can see how what they’re learning actually applies outside the classroom, they’re way more engaged and pick up skills they’ll actually use later on.

Effective assessment in AI education programs should move beyond traditional exams to include project-based assessments that evaluate students' ability to apply AI concepts to real-world problems and portfolios showcasing their coding skills, model development, and ethical considerations in AI. Furthermore, incorporating peer reviews and presentations can assess their collaborative abilities and communication skills, essential for success in the interdisciplinary field of artificial intelligence.