Ambiguity in course moderation process occurs as human experts tend to compete in proving their superiority in knowledge and experience. Virtually collaborating to produce a time-constraint comprehensive report based on multiple sources of information, the course instructor and moderator are challenged to meet a consensus on the moderation findings, which further causes an undecisive result at the management level. Supported by a comparative analysis on five different case institutions, a knowledge-based multi-agent reinforcement learning (MARL) is proposed to be the solution, as it mitigates the ambiguous situation by assigning role-based tasks to the knowledge agents that will decide on the actions based on the multiple existing sources and their human counterparts’ reviewing styles and preferences. Supporting components like Natural Language Processing (NLP) and rewards system are recommended to be included to fortify the proposed MARL system, as it would require agents to understand from unstructured data in documents and profile records. Covering only one role at a time in the proposed system, which does not reflect the real complex case, the function of the reinforcement learning agents could be made as complex as playing interchangeable roles based on the assigned task at a time.