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 build the skills, productivity, engagement, and loyalty of their workforces.
The time is ripe for the emergence of a new breed of intelligent learning systems that provide mentor-like features. 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.
This workshop series aims to lay the foundations of this research stream, by forming an international research community and drawing research roadmap. It will provide a forum to explore opportunities and challenges, identify relevant existing research, and point at new research avenues including:
– 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.
The workshop will be organised around main themes including:
What are the key mentoring features and what theories are they underpinned by? Example topics include:
* Main definitions – mentoring, coaching, advising;
* Mentoring features – contextual understanding, nudging, challenging, motivating;
* Scope – difference between virtual mentors and virtual tutors;
* Methodologies – design-based research, ecological validity, evaluation;
* Pedagogical models – self-regulated learning, reflexive learning, social learning, vicarious learning, crossover learning, transitions
What computational models are required to realise mentor-like features; and what are the opportunities and challenges brought by these models? Example computational models include:
* Social interaction spaces
* Situational simulations
* Open/interactive learner models
* Interactive pedagogical agents
* Contextualised nudges
* Mobile assistants
* Cognitive computing
* Wearable technology and sensors
DOMAINS AND CONTEXTS:
What challenges are faced in traditional and emerging domains and contexts; and how mentor-like features can address these challenges?
Example domains/contexts include, but are not limited to:
* Peer mentoring
* Personalised assistants/buddies
* Social learning
* Flipped classroom
* Workplace learning
* Career advisors
* Transferable skills
* Tutor/mentor support
Workshop Format and Participation
The workshop will be organised in a highly interactive manner combining presentations and discussions.
We invite papers (max 10 pages) or extended abstracts (max 2 pages) describing:
* research activity focusing on specific questions and lessons learnt;
* position statements that point at key challenges, potential techniques, or possible domains/contexts;
* descriptions of learning systems that include mentor-like features or requirements for mentor-like features to augment existing systems.
The submissions should follow Springer, LNCS format.
Follow the Easychair submission link.
– submission deadline: 8 May 2017
– notification of acceptance: 22 May 2017
– workshop date: TBC (28 or 29 June 2017)
Vania Dimitrova, University of Leeds, UK
Art Graesser, University of Memphis, USA
Lydia Lau, University of Leeds, UK
Antonija Mitrovic, University of Canterbury, New Zealand
David Shaffer, University of Wisconsin-Madison, USA
Amali Weerasinghe, University of Adelaide, Australia