IJAIED Journal Scope
The International Journal of Artificial Intelligence in Education (IJAIED) is the official journal of the International AIED Society. IJAIED publishes papers and other items concerned with the application of artificial intelligence techniques and concepts to the design of systems to support learning. The journal is reviewed by a robust Editorial Board of leading experts. Scholars interested in publishing in IAIED should consult the Guidelines for Authors below to ensure relevance to IJAIED before preparing their submission to meet and publisher Springer Publishing requirements. Authors looking to submit to IJAIED should also refer to Springer's LaTex ("all journals" variant) and Word formatting instructions.
Accessing IJAIED Articles
IJAIED is an archival journal. Issues of IJAIED may be accessed through membership in the IAIED Society by logging in with an active IAIED account and accessing articles through the Journal section. Recent editions are published online through Springer Publishing. Volumes prior to the 23rd edition were published by IOS Press and also included a conventional printed version. Selected print copies of later editions may be available from the publisher upon request (for details, contact firstname.lastname@example.org)
IJAIED publishes papers concerned with the application of AI to education. It aims to help the development of principles for the design of computer-based learning systems. Its premise is that such principles involve the modelling and representation of relevant aspects of knowledge, before implementation or during execution, and hence require the application of AI techniques and concepts. IJAIED has a very broad notion of the scope of AI and of a "computer-based learning system", as indicated by the following list of topics considered to be within the scope of IJAIED:
- adaptive and intelligent multimedia and hypermedia systems
- agent-based learning environments
- AIED and teacher education
- architectures for AIED systems
- assessment and testing of learning outcomes
- authoring systems and shells for AIED systems
- bayesian and statistical methods
- case-based systems
- cognitive development
- cognitive models of problem-solving
- cognitive tools for learning
- computer-assisted language learning
- computer-supported collaborative learning
- culturally aware learning systems
- dialogue (argumentation, explanation, negotiation, etc.)
- discovery environments and microworlds
- distributed learning environments
- educational data mining
- educational robotics
- embedded training systems
- empirical studies to inform the design of learning environments
- environments to support the learning of programming
- evaluation of AIED systems
- formal models of components of AIED systems
- game-based learning environments
- gamification for learning
- help and advice systems
- human factors and interface design
- instructional design principles
- instructional planning
- intelligent agents on the internet
- intelligent courseware for computer-based training
- embedded interface agents
- intelligent tutoring systems
- knowledge and skill acquisition
- knowledge representation for instruction
- learning analytics
- modelling metacognitive skills
- modelling pedagogical interactions
- natural language interfaces for instructional systems
- networked learning and teaching systems
- neural models applied to AIED systems
- OLMs, Open Learner Models
- peer learning
- performance support systems
- practical, real-world applications of AIED systems
- qualitative reasoning in simulations
- situated learning and cognitive apprenticeship
- social and cultural aspects of learning
- student modelling and cognitive diagnosis
- support for knowledge building communities
- support for networked communication
- theories of learning and conceptual change
- tools for administration and curriculum integration
- tools for the guided exploration of information resources
- virtual learning environments
- virtual reality based learning systems
- visual, graphical and other innovative interfaces
- web-based AIED systems
Guidelines for Authors
The following guidelines are intended for the benefit of potential IJAIED authors and reviewers, although we must always bear in mind that AIED is still an evolving field and it is not the aim to lay down 'rules of good practice' which inhibit original research.
A major factor is that AIED is an inter-disciplinary field and thus the IJAIED readership includes people with widely differing background knowledge and interests. There is therefore an obligation on all IJAIED authors to make their papers accessible to the broad spectrum of IJAIED readers. Specialist terms should be explained, theories in particular fields should be briefly summarised, etc. Where it is difficult to do this in the space available, references should be provided.
Although AIED itself is inter-disciplinary, when the main focus of a paper is towards one particular established discipline, then that paper must follow the precepts of that discipline. For example, if a paper presents a behavioural study of students using some system to support claims about improved learning, then it must conform to the standards developed in behavioural science, e.g. there should be a comparison with a control group, the variance in the data must be dealt with somehow, and so on. Other examples are discussed below.
On the other hand, it is not reasonable to expect that authors will meet all the standards of all disciplines outside their main focus. Of course a paper should not violate or ignore such standards but it need not go into irrelevant detail to meet them. For example, the behavioural study mentioned above need not include a theoretical analysis of the algorithms underlying the system which is the basis for the study.
There are many different kinds of paper which may be written for IJAIED, each with their own requirements. But first we should try to identify what all IJAIED papers have in common. In general terms, IJAIED papers are concerned with the application of AI techniques and concepts to educational issues. Indeed many IJAIED papers could be titled 'The application of X to Y' where X and Y are clearly within the fields of AI and Education respectively.
However, this is much too simplistic: AI is much more than a collection of techniques and concepts. It is a rather complex methodology (or set of methodologies) concerned with issues many of which (such as the nature of knowledge and learning) are intrinsically related to education. Sometimes an IJAIED paper may not be able to point to specific AI techniques but rather is embued with the spirit of an AI approach to the problem. Naturally, such a paper should make it clear what insights and benefits come from adopting an AI point of view.
In addition, as AIED evolves it develops techniques, concepts and methodologies of its own, which while originally derived from AI may no longer be considered part of AI itself. For example, AIED now routinely uses concepts such as model tracing, mal-rule, etc. and refers to classic AIED systems such as SOPHIE and GUIDON which may well not be discussed at all in standard AI texts. In such cases it is not necessary for IJAIED papers to establish the bona-fide AI pedigree of such terms. It is entirely to be expected and welcomed that AIED research will spawn its own concepts and paradigms which can be assumed common knowledge for AIED researchers.
The phrase 'to educational issues' above is also too simplistic. Education is also a complex subject. Generally, AIED has interpreted 'education' rather narrowly as concerned with learning, teaching, training and the like, rather than with the broader cultural matters which educationalists have in mind. As AIED evolves and its achievements become more significant maybe it will address itself to educational theory and philosophy (and of course such papers would be welcome in IJAIED). In addition, the educational process involves more than the direct concern with learning - there are, for example, various administrative activities, such as timetabling, which when tackled as an AI application would also lie within IJAIED's scope.
Generally, it is necessary for IJAIED papers to identify the (educational) problem they are addressing, to describe the kinds of learning and teaching which a system is intended to provide and if appropriate to give some evidence of benefits. Again, as the field evolves these links may be left implicit or indirect. For example, we can assume that an analysis of student modelling or a description of some new student modelling technique is intended to lead to improvements when applied to a tutoring system or learning environment without this needing to be made explicit. Similarly, as techniques develop and become standard so AI-based authoring tools or shells will be specified which are only indirectly concerned with learning and teaching.
As the journal title 'AI in Education' indicates, the impetus and flow of ideas in AIED has been from AI to education. On the whole, educationalists have been reactive, criticising AI approaches, rather than proactive, using their own perspectives to propose system designs. This will change as the field is successful. Naturally, IJAIED would welcome and encourage educationalists to explain the implications of their own understanding of knowledge, learning and teaching for AIED and AI generally. (It is noticeable that other social scientists such as sociologists, psychologists, anthropologists, etc. have been less reticent in advising the AI field.)
After these general comments, we can now consider the different kinds of IJAIED paper separately (although of course not all papers will fit neatly into one category).
The system described would be an AI-based learning environment, authoring tool or other system with an educational purpose. Some aspect of the system would be novel (or standard components would be combined in some novel way) and the benefits of that novelty would be demonstrated, typically through some study of the system in use. While it should be discussed how the system's design derives from theoretical principles, a design itself is not a research result: it must be demonstrated through an implementation. Merely using conventional AI tools (such as AI programming languages) to support normal educational practice is not AIED research. System description papers should include a rigorous evaluation that assesses how well the system achieves its goals. Teaching AI is not usually AIED research either.
Here the technical details of a new method for implementing some component of an AI-based learning environment would be discussed, including some formal specification or pseudocode. If the focus is on the technical properties then these would be presented following good computer science and HCI practice. The benefits of the method would be shown by some theoretical or experimental study, the latter not necessarily involving the evaluation of a complete system, since the components which are not a focus of the study may not be fully implemented. If the component is not a standard one then some discussion of its relevance should be given.
As AIED research progresses it may become possible to give formal, mathematical analyses of the properties of systems. Such an analysis may just provide a cleansing of the messiness of an actual implementation, but to be of more interest to IJAIED, some new insight should result - for example, some precise clarification of the effect of design choices, a detailed comparison of the power of two systems or techniques, a derivation of some predicted outcome from the use of a system, etc.
There are many kinds of experimental study, ranging from preliminary studies of learners intended to inform the design of a learning environment through to large-scale summative evaluations of completed systems. There are also empirical studies of teachers intended to lead to principles to support the development of systems. In all cases, the relevance to AIED must be made clear and the study itself must follow the standard practices of behavioural science.
An acceptable review would be comprehensive and balanced. To be comprehensive, the review must be of a topic which can be adequately covered in a journal-length paper: a reader must be confident that no significant work in the field discussed has been omitted. Reviews employing standardized methodologies (e.g., systematic reviews, meta-analyses) are preferred for this reason. To be balanced, the review must discuss work in proportion to its importance and derive conclusions justified by the field as a whole. A review is not just a catalogue of relevant work: it is an analysis based on some informative conceptual framework. IJAIED prefers reviews which are timely, that is, are concerned with some emerging issue, not ones which give a historical perspective on some long-standing topic.
Such a paper develops a new conceptualisation of the AIED field, or some aspect of it. It would explore the implications of some educational, sociological, AI or other paradigm for AIED. It may explore the impact or influence of some of the more philosophical aspects of AI within educational theory and practice. It may be more speculative than other kinds of paper but it would at least identify clearly the implications for AIED if the arguments presented are considered sound.
Because AIED is a somewhat controversial field, IJAIED welcomes 'viewpoint' papers in which the normal standards of objectivity and expectations of results are relaxed in order that an author can present a challenge to the prevailing orthodoxy. Viewpoints may be polemical and less balanced than a review but the arguments must be clearly presented and based upon a deep understanding of the relevant issues. Ideally, they should serve to provoke discussion among the readership. They should be well grounded in the literature. Often a viewpoint will be published together with one or more short responses to it in order to initiate such a debate. A paper submitted as a viewpoint should be clearly identified as such because the reviewing criteria are different from 'normal' papers.
Finally, it may help intending authors to know the questions which reviewers are asked to answer. These are:
- Is the subject of the paper suitable for IJAIED?
- Is the content of the paper likely to be of interest to and appropriate for IJAIED readers?
- Is the paper technically sound and accurate in its AI and Education content?
- Is this a new and original contribution? Does the author make clear what this contribution is?
- Are the major claims and conclusions substantiated? Have the ideas or systems been tested or evaluated sensibly?
- Is the paper clear, explicit, and well-organised? Is the length appropriate for the content? Are there any gaps or redundancies?
- Are the title and abstract informative?
- Does the paper adequately refer to related work? Are the references complete and necessary?