Abstract In recent years, we have seen a significant proliferation of e-learning platforms. E-learning platforms allow teachers to create digital courses in a more effective and time-saving way, but several flaws hinder their actual success. One main problem is that teachers have difficulties finding and combining open-access learning materials that match their specific needs precisely when there are so many to choose from. This paper proposes a new strategy for creating digital courses that use learning objects (LOs) as primary elements. The idea consists of using an intelligent chatbot to assist teachers in their activities. Defined using RASA technology, the chatbot asks for information about the course the teacher has to create based on her/his profile and needs. It suggests the best LOs and how to combine them according to their prerequisites and outcomes. A chatbot-based recommendation system provides suggestions through BERT, a machine-learning model based on Transformers, to define the semantic similarity between the entered data and the LOs metadata. In addition, the chatbot also suggests how to combine the LOs into a final learning path. Finally, the paper presents some preliminary results about tests carried out by teachers in creating their digital courses.

The use of learning objects (LOs) to create digital courses has been widely advocated by learning strategists and by teachers engaged in the e-learning domain. The ability to combine chunks of learning material as to meet complex educational requirements is still a challenge. This paper explores the idea that a learning assistant advises teachers about the e-learning modules to take into account for their courses. An AI-based digital assistant can provide significant opportunities, but might be perceived as a threat. The paper presents how teacher could perceive a virtual assistant as more trustworthy when it applies interactive visual strategies. To analyze teachers’ acceptance of the digital assistant, our proposal aims at extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model in order to incorporate three new constructors: Communicability, perceived trust and experience. To this end, 14 teachers have been involved in a user tests.

Recent studies in the context of machine learning have shown the effectiveness of deep attentional mechanisms for identifying important communities and relationships within a given input network. These studies can be effectively applied in those contexts where capturing specific dependencies, while downloading useless content, is essential to take decisions and provide accurate inference. This is the case, for example, of current recommender systems that exploit social information as a clever source of recommendations and / or explanations. In this paper we extend the social engine of our educational platform “WhoTeach” to leverage social information for educational services. In particular, we report our work in progress for providing “WhoTeach” with an attentional-based recommander system oriented to the design of programmes and courses for new teachers.