Exploiting agents in e-learning and skills management context

Nowadays, it is quite agreed that organizations gain limited advantages in adopting e-learning platforms that only provide educational contents. An advantageous e-learning platform should have instead the capability to help enrich, share and circulate organization knowledge, thus contributing to making the organization dynamic and flexible. In this paper MASEL, a Multi-Agent System for E-Learning and Skill Management is described. MASEL performs the following tasks: (i) supports Chief Learning Officers in defining roles, associated competencies and required knowledge level; (ii) manages the skill map of the organization; (iii) evaluates human resources competence gaps; (iv) supports employees in filling the competence gaps related to their roles; (v) creates personalized learning paths according to feedbacks that users provide to optimize the acquisition of required competencies; (vi) assists Chief Learning Officers in selecting the most appropriate employee for a given role; (vii) assists a Project Manager in building teamwork. A prototype tool implementing MASEL using JADE (Java Agent DEvelopment Framework) was developed. The reasoning capability of MASEL agents involved in the learning paths building process and in the team building process is implemented using DLV, a disjunctive logic programming system.