Held in conjunction with the 18th International Conference on
Artificial Intelligence in Education, AIED2017
Wuhan, China, June 28- July 2, 2017
Mentoring is crucial for professional development and lifelong learning. It is seen by organisations as the most cost-effective and sustainable method for developing talent, for building transferable skills, for increasing motivation and confidence, for assisting with transitions across formal and informal education, for learning across workplace contexts, and for continuous career development. Studies show that investment in virtual mentors can help companies enhance workers’ skills, productivity, engagement, and loyalty.
Crucial for intelligent mentors will be the ability to help learners connect their real world experience with learning that is usually acquired through digital resources. Virtual mentors would be able to facilitate self-actualisation, helping learners realise their full potential. They would require a multi-faceted learner experience modelling mechanisms to get sufficient understanding of the learner, his/her current situation, and relevance to past experiences by the same learner (or by other people). Furthermore, they would embed new pedagogic strategies for promoting reflection and self-awareness through interactive nudges, as well as new knowledge models formed by establishing connections and associations.
The IMS workshop series aims to lay the foundations of this research stream, by forming an international research community and drawing a research roadmap. It will provide a forum to explore opportunities and challenges, identify relevant existing research, and point at new research avenues. These include (but are not limited to):
- contextual understanding and broadening the scope of learner modelling (learning attitudes, cultural diversity, gender diversity, experience);
- support/scaffolding for metacognitive skills and contextualising self-regulated learning in the real world;
- integration of long term learner modelling with short-term, session-based learner modelling (macro vs micro level of learner modelling);
- methods/models that can be used for behaviour change;
- appropriate technologies (social spaces, digital media, pervasive systems, e-books/hypertext); and
- techniques for intelligent support and crowdsourcing wisdom.