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Evaluation methodology Foundational figures Methodologically adjacent

Dragan Gašević

Whether AI tools used in educational contexts actually help the learners they're built for — and what kind of evaluation infrastructure that question requires beyond conventional ML benchmarks.

Gašević is one of the founders of learning analytics as a research discipline, and the founding chair of the Learning Analytics and Knowledge (LAK) conference series. His longer career has built out the methodological infrastructure for evaluating AI-in-education at the scale of actual schools and universities — measuring not benchmark accuracy but learning outcomes, equity effects, and unintended pedagogical consequences across populations of students. For ai100, the parallel is methodologically direct: a deployed AI system whose evaluation has to account for human outcomes in context.

Worth following when
you want LLM evaluation methodology informed by educational-AI deployment experience — a domain where evaluation in real human contexts has been the price of entry for over a decade.
Topics
learning analytics as a research discipline; AI-in-education evaluation methodology; outcome-based assessment of deployed AI systems.
Key works
founding of Learning Analytics and Knowledge (LAK) conference series (2011 onward); body of work on learning analytics methodology; Monash and Society for Learning Analytics Research publications.