Recall — Test understanding, not memory.

Recall is an AI learning companion designed to help students move beyond memorisation and build real understanding. Instead of immediately giving answers or accepting confident-sounding responses, Recall asks learners to explain concepts in their own words using either voice or text and actively evaluates the reasoning behind them. If an explanation is incorrect, vague, contradictory, or simply sounds more confident than it is accurate, Recall points that out directly and explains where the logic breaks down. The system then generates adaptive follow-up questions to challenge weak assumptions, expose misconceptions, and test whether the learner can apply the concept in different situations. A student may know the right terminology but still misunderstand the underlying idea; Recall is designed to catch exactly that. Rather than marking answers as simply right or wrong, it pushes the learner to explain, defend, revise, and strengthen their thinking through a natural conversational flow. As the interaction continues, Recall builds a dynamic picture of the learner’s strengths, misconceptions, and knowledge gaps, allowing future questions to become increasingly personalised. Voice input makes the experience feel closer to a real oral discussion or viva, while text input supports more deliberate explanations and problem-solving. The goal is to create a learning experience that is honest, active, and intellectually demanding—one where the AI does not just help students feel like they understand something, but tests whether they actually do.