Journal of Higher Education Research

With the expansion of higher education and the increasing complexity of student affairs, the performance evaluation of college counselors needs to become more scientific, dynamic, and evidence-based. Traditional evaluation methods often rely on periodic reports, subjective judgment, and fragmented data, which may weaken fairness and timeliness. This study develops an artificial intelligence-enabled evaluation mechanism for college counselor work performance. Based on public higher education statistics, policy requirements, and an institutional case from Sichuan Vocational College of Culture and Communication, the paper proposes a multidimensional evaluation framework covering workload, responsiveness, student feedback, professional development, and ethics control. A weighted index model is designed to support process-based evaluation and decision feedback. The study argues that AI can improve evaluation accuracy and efficiency, but it should function as a supportive tool rather than a substitute for human judgment. A human-machine collaborative mechanism is therefore necessary for fair and developmental counselor evaluation.