This work presents a novel method for modeling collaborative learning as multi-issue agent negotiation using fuzzy constraints. Agent negotiation is an iterative process, through which, the proposed method aggregates student marks to reduce personal bias. In the framework, students define individual fuzzy membership functions based on their evaluation concepts and agents facilitate student-student negotiations during the assessment process. By applying the proposed method, agents can achieve mutually acceptable agreements that avoid the subjective judgments and unfair assessments. Thus, the negotiated agreement provides students with superior assessments, thereby enhancing learning effectiveness. To demonstrate the usefulness of the proposed framework, a web-based assessment agent was implemented and used by 49 information management students who submitted assignments for peer review. Experimental results suggested that students using the system had significantly improved learning performance over three rounds of peer assessment. Questionnaire results indicated that students believed that the assessment agent provided increased flexibility and equity during the peer assessment process. (Contains 5 figures and 5 tables.)