Adaptive multi‐agent smart academic advising framework

Abstract

Abstract Academic advising is a crucial process in higher education and usually requires better understanding of student capabilities and curriculum structure to achieve its intended goals. Here, the authors propose a framework of integrated environment based on multi‐agents to automate the full process of academic advising. The proposed framework consists of six agents namely, student agent, instructor agent, administrator agent, performance agent, schedule agent, and smart advisor agent. These agents are interacting together with the help of smart advisor agent, which manages the communication between them and provides smart advice based on machine learning techniques. In addition, the analysis of the proposed framework along with the deployment map is discussed by the authors. Moreover, a case study is presented in terms of a sample part of adaptive multi‐agent smart academic advising framework to demonstrate the workflow of the proposed approach.

Keywords

• educational administrative data processing
• learning (artificial intelligence)
• multi‐agent systems