Method
KLD courses are built around a simple standard: learners notice clearly, decide responsibly, use AI with discipline, and leave behind evidence that can be reviewed.
Teaching method
The method below is the teaching operating system behind KLD courses and public learning materials.
KLD lessons begin with observation before opinion. Learners are taught to look at a product moment, name what is visible, ask what the user needs, choose a useful improvement, and explain the reason. This keeps the work grounded in evidence instead of taste alone.
Every course is designed to leave a trail of work that can be reviewed. Learners do not simply finish videos or read notes. They build small artifacts that show how their product reasoning developed, where AI helped, and what decision they made.
AI is introduced as a practice partner that helps learners move faster through examples, critique, options, and revision. The standard is not whether AI produced something impressive. The standard is whether the learner can evaluate it and make a better product decision because of the process.
Review is part of the learning design. Work is checked for clarity, usefulness, accessibility, product reasoning, and evidence quality. Learners practice explaining why a decision matters, what risk remains, and what needs testing next.
