Procrastination is driven by emotional self-regulation failures and cognitive distortions that narratives often mask. We introduce ProcrastiType-ZH, to our knowledge the first Traditional Chinese multi-dimensional procrastination dataset, with 1,000 LLM-generated narratives and human consensus labels for four motives on a 1-5 severity scale (effective range 1-4). We propose ProcrastiCoT, a CBT-informed Chain-of-Thought framework, and inject its LLM-generated reasoning into BERT-size encoders either by concatenation or through a Dual-Encoder Cross-Attention architecture that gives the reasoning its own context window. Across backbones, reasoning injection gives the best single-run QWK on Avoidant, Decisional, and Irrationality. On our primary CKIP-BERT backbone, the Dual-Encoder scores above its pure-encoder counterpart on every dimension in each of four paired training runs, significantly (uncorrected) on Arousal, Decisional, and Irrationality but not Avoidant; on Decisional its mean (0.316) only matches a lexical baseline. A Qwen2.5-7B model fine-tuned on the same labels, with about 70 times more parameters, is strongest on Arousal (QWK 0.709) but below the Dual-Encoder on Irrationality and Avoidant in all four runs. Adding the teacher’s reasoning to its targets lowers QWK on every dimension and pulls its predictions toward the teacher’s ratings, consistent with a conflict between that reasoning and the human labels.