Artificial Intelligence in Education (AIEd) Symposium 2026

In mathematics, computer science and engineering, AI now produces outputs that look like competence. It returns correct solutions, working code and plausible proofs in seconds. This is where the central risk of AI for education is sharpest. The visible output, the performance, is easily mistaken for the invisible capability, the learning. A correct answer is not evidence of understanding, and the gap between the two is widest in the disciplines where the answer is most checkable. These are also the disciplines where students are adopting AI fastest.

This keynote suggests that the principal risk of AI is misjudged. The danger is not that machines become too capable. It is that we undervalue ourselves and allow performance gains to stand in for learning gains. For STEM universities, the opportunity is to design education that develops what AI cannot: accurate judgement of one’s own knowledge, the disposition to question a confident output, and the metacognitive capacity to know when one has understood something and when one has not.

Prof Luckin sets out what this means for the curriculum, assessment and teaching of STEM disciplines, and why the institutions that learn fast and act more slowly will be the ones best placed to prepare their graduates for an AI world: graduates whose value lies in the judgement, reasoning and self-knowledge that no machine supplies.

As AI continues to evolve at an unprecedented pace, the educational landscape is being transformed in ways that challenge traditional learning paradigms. This keynote talk will address the intersection of rapid AI advancements and the nuanced, reflective nature of human learning. It will discuss the distinction between AI for learning and AI for performance, urging AIED designers to prioritise genuine “slower” effortful learning processes over “faster” learning outcomes and solutions.

From a learning science perspective, Prof Chen Wenli will examine how AI-augmented learning environments can be designed not just as tools for quick answers, but as cognitive partners that enhance human agency, foster self-regulation, critical thinking, and metacognitive skills. Drawing on her empirical research, Prof Chen will share human-centric AIED designs to enhance their cognitive and regulatory capacities, rather than undermining them. This talk advocates a shift in focus from efficiency (faster) to meaningful learning (slower), highlighting the importance of human learners’ deep cognitive engagement and agency in human-AI collaboration for learning.