Abstract: To address the challenges of limited resources, rigid teaching models, and simplistic evaluation methods in inorganic chemistry laboratory instruction at local universities, this study investigates the application of artificial intelligence (AI) technologies. The paper analyzes AI's advantages in supporting instruction, virtual simulation, and intelligent assessment systems. A phased "AI + Laboratory Teaching" implementation framework is proposed, with detailed demonstration using acid-base titration experiments as a case study. The findings suggest that AI technology can potentially overcome resource constraints, enable personalized learning, facilitate precise performance evaluation, and provide innovative solutions for laboratory teaching reform in regional higher education institutions.

Key words: Artificial intelligence, Inorganic chemistry, Laboratory teaching, Teaching reform, Local university