Why are professors so good at training AI?

Professors know how to evaluate 🍎

Teaching is, at its core, an act of evaluation. Every assignment, lab report, or exam is a chance to measure not only whether a student got the “right” answer, but whether they understood why. Professors design rubrics that capture depth of reasoning, application of principles, and clarity of explanation.

Training AI requires the same skill. Models often produce outputs that look convincing but fail when tested against scientific rigor. A professor’s trained eye can spot when:

• An equation is technically correct but irrelevant to the problem

• A simulation output ignores a key assumption or violates a conservation law

• An explanation is incomplete or misleading for learners

These subtle judgments are what turn AI from a generator of words into a learner of science. That is why professors and teachers are uniquely positioned to shape AI systems that respect the standards of STEM education and research.

Teaching experience translates into AI training impact

Professors and teachers also bring something else that cannot be replicated: the ability to guide learning itself. They know how to structure evaluation so it motivates, how to design rubrics that highlight both strengths and weaknesses, and how to build understanding step by step.

One contributor, described it this way:

“As a teacher, I have never had other experiences besides teaching. At Outlier, I have new experiences and gain more knowledge and skills. Besides that, I have chances to meet many people whom I can never imagine to meet and many of them are very supportive.”

This perspective captures why professors and teachers are so valuable in AI training: they bring rigor and pedagogy, but also curiosity, adaptability, and a community spirit.

The irreplaceable role of educators in AI

AI systems can be fast, but without academic standards and teaching insight, speed alone is meaningless. Professors are the ones who ensure these systems become accurate, responsible, and truly useful. 

👉 If you are an educator, your evaluation skills and teaching experience are not just relevant — they are essential to shaping the future of AI.