Enhancing Conceptual Resilience Through Timed Lateral Scaffolds: A Study of AI-Supported Learning

         Advances in Artificial Intelligence (AI) technologies have made it possible for us to develop intelligent tutoring systems that are capable of adapting dynamically to learners’ needs. But existing systems are able to provide step-by-step or hierarchical support, they often do not take into account-the lateral dimensions of problem solving, which is how learners form horizontal conceptual connections as they work through complex tasks. This proposed system would be continuously analysing the problem-solving trajectory of the learner in real-time by monitoring indicators, such as response latency and error clustering, to detect points of stagnation. Following the inference of a learning impasse, the AI would interfere but not by simplifying the task; instead, it reorients the student toward related or adjacent concepts to encourage associative reasoning rather than linear hint-giving. Thus, the horizontal guidance allows the learner to draw conceptual parallels, recognize underlying patterns, and reconstruct understanding without disrupting autonomy. A controlled experiment of 360 undergraduate mathematics students tested three scaffold delivery modes, namely early, mid process, and late, to examine how timing influences learning. Quantitative data on accuracy, time-on-task, and cognitive load were complemented by interview data on metacognitive awareness and confidence. The results indicated that timing-sensitive lateral scaffolding enhances conceptual retention and problem-solving resilience. Rather than providing answers, the AI tutor acts like a cognitive companion by aligning support with the learner’s thought process. This study advances scaffolding theory by adding a temporal-lateral dimension, showing how AI can assist at the precise moment of conceptual struggle, shifting tutoring from reactive to proactive support.