Abstract:

E-learning allows access to training without time and space limits. It is an effective tool used to deliver training courses to professionals, regardless of their location, that would otherwise not be able to attend traditional face-to-face training programs, due to the high cost and inconvenience of time and travel. E-learning, however, still does not offer the possibility for learners to customize their own personal training paths. The integration of artificial intelligence with instructional design could provide a possible solution to this issue by opening up new prospects to set up high quality training options.
Incorporating these aspects of e-learning, the open-university “Gugliemo Marconi” designed the T2 project to develop and test a didactic model aimed at delivering customized learning pathways to a specific target group. T2 stands for “Teaching Tutoring - Intelligent Agents to customize lifelong learning pathways in the microfinance sector”. It is implemented in the microfinance sector with a partnership composed of two Open-Universities and two European Microfinance Institution (MFIs) networks with the financial support of the European Commission.
T2 addresses to a target group that often suffers from a lack of training courses available due to a small number of MFI professionals, working in geographically scattered locations causing the economical cost of a face-to-face training course to be extremely high and therefore virtually impossible to implement.
The project focuses on instructional design tools able to support teachers in designing short, customised face-to-face seminars based on individual student self-assessment conducted during a previous e-learning session.
Combining e-learning methods and Intelligent Agents technologies, T2 has developed a blended course. The e-learning session integrates a web-based intelligent tutoring system able to generate individualised assessment pathways for each participant. The system helps guide teachers in designing short, face-to-face seminars, following specific guidelines based on trainees’ needs.

Keywords:

training needs analysis, intelligents agents, distance and in presence learning.