The rapid maturation of large language models has opened new opportunities for capable of enhancing learning outcomes, enriching instructional practice, and supporting large-scale computing education with high reliability through personalized, scalable, and data-driven instructional support. The ChatGPT Learning Companion (ChatGPT-LC) introduces a multimodal framework that integrates conversational scaffolding, code reasoning, misconception diagnostics, and learner analytics into a unified system capable of adapting instruction in real time. Deployed across 260 undergraduate learners in three programming courses, ChatGPT-LC produced substantial performance gains, including a 35.20% increase in concept mastery, 27.90% improvement in debugging accuracy, and error-type reductions ranging from 53.60% to 65.50%. Behavioral analytics revealed strong correlations between engagement intensity and performance (up to r = 0.740), with reflective and exploratory learners achieving scores above 88–90%. Instructor workload decreased by more than 32 hours per week, supported by high expert-verified accuracy (92–96%) of AI-generated feedback. System-level benchmarks demonstrated robust scalability, maintaining 97.00% success rates at 500 concurrent users and reducing latency from 450 ms to under 100 ms after optimization. Collectively, these results show that ChatGPT-LC functions not only as an automated tutor but as an adaptive cognitive partner capable of enhancing learning outcomes, enriching instructional practice, and supporting large-scale computing education with high reliability and pedagogical fidelity.