ED 2015 - International Workshop on Should AI stay married to ED?

At its origin, the field of Artificial Intelligence in Education (AIEd) aimed to employ Artificial Intelligence techniques in the design of computer systems for learning. This aim is still upheld by the AIEd Society, with numerous learning environments having been built, and some having been deployed successfully in real schools, since the field’s inception over 25 years ago. This provides a testament to the field’s vigour, especially when it is viewed in a wider context of other cognate domains such as technology-enhanced learning and learning sciences. The defining characteristic of AIEd that differentiates it from those other cognate domains is that by definition any work conducted under its banner has to derive from and/or feed back to theory and practice, ideally in both Artificial Intelligence and in Education. However, with the advent of smart, ubiquitous and pervasive technologies, the exact role of the AI in Education as a field may need to be interrogated afresh. After all, all smart technologies currently in use (also in the classrooms), from tablet computers to smart phones, from internet search engines to social networking sites, have a growing reliance on techniques derived from AI. AI is in common use, even if unbeknown to many of its users, with AI-based technology forming an inherent part of education and contemporary classrooms.
The 25th anniversary of the IJAIEd journal is a good opportunity to interrogate the aims and aspirations of the field, its past and current achievements, and the AIED 2015 conference constitutes a timely forum for such an interrogation. This is necessary to reassert the field’s position in the wider scientific and societal contexts and if necessary to recast it to reflect the lessons learned, the important forces within both AI and Education that impact the field, and the changing needs of the direct beneficiaries of the research conducted therein: the researchers, educators and learners. Many questions arise:
(1) What is and what should be the role of AI in Education and conversely of Education in AI? Specifically, in the early days of AIEd there seemed to be a lot of AI in AIEd, but now AI is more often a placeholder for any kind of advanced technology. Is there a unique role specifically for AI in building learning systems? If so, what is it and what particular AI methodologies should AIEd be drawing on? If not, why not? Has AIEd moved on to different issues? Or has AI changed in its aims and scope, making it less relevant to AIEd?
(2)What is and what should be the motivation for AIEd as a field? Supporting learning has been considered a great "challenge domain" for AI in that many of the big AI questions must be answered, at least to some extent, to build a sophisticated learning environment. But, are the ideas generated in AIEd influencing AI or Education in any major way? If not, why not?
(3) What is and what should be the balance of respective contributions to AIEd from AI and Education as distinct fields of research and practice? Both fields have well-established methodologies and practices, but the extent to which these are cross-fertilising under the AIEd banner is not clear. For example, it is often the case that despite the popular participatory research rhetoric, teachers and learners still act mostly as informants and study participants, and the actual benefits of AIEd to them and to real-world education are not always easily identifiable in the longer term. Should the definition of AIEd’s role in Education be extended, for example, to include some AI design methods (e.g. knowledge elicitation and engineering) as a means for enhancing pedagogical practices themselves, regardless of whether or not an intelligent system is available, in order to make AI as a methodology and as a way of thinking more immediately relevant and accessible to educators?
(4) A related question focuses on the extent to which the results of AIEd research are meaningful to real educational practice? Does the education community even care? Similar to many fields aspiring to scientific rigour, the AIEd community can showcase hundreds of studies demonstrating the statistical significance of this or that approach or system or their individual components through rigorously designed studies, but it is not always clear how the results of many of those studies actually translate into real educational teaching and learning practices raising a question as to whether all this rigour may not be happening in a vacuum. ?
(5) What are the future directions for the field that could justify and maintain its unique identity? How does AIEd differ from related disciplines such as Learning Sciences, ITS, and CSCL? Or are these just labels for essentially the same research discipline? ?