Collaborative innovation mechanisms for university artificial intelligence research institutes

University artificial intelligence research institutes are becoming important institutional platforms for connecting academic research, industrial application, technology transfer, and talent development. However, many institutes still face fragmented processes for demand identification, weak project coordination, slow commercialization, and insufficient benefit-sharing arrangements. This review article develops a conceptual mechanism for industry-academia-research collaborative innovation in university AI research institutes. Based on recent literature on university-industry collaboration, knowledge transfer, AI-enabled innovation ecosystems, and Chinese policy developments, the paper analyzes the theoretical foundations, policy background, internal and external drivers, and operational logic of such collaboration. It proposes a four-stage collaborative innovation model composed of demand docking, joint research, achievement transformation, and talent cultivation. It further discusses supporting mechanisms in policy coordination, platform construction, intellectual property management, benefit distribution, evaluation, and ethics. The paper argues that AI research institutes should move beyond loose project-based cooperation and become integrated platforms that align scientific exploration with industrial scenarios, market-oriented transformation, and interdisciplinary training. The proposed framework offers practical guidance for universities seeking to build sustainable AI innovation ecosystems.