Special and Thematic Call for Papers

We are now accepting proposals for new thematic issues. The submission should include similar information to that shown below for recent special issues:
  • Names of the proposed Guest Editors, their affiliations
  • The title and brief description, as in the examples below.
  • Rationale and motivation for the issue in terms of the importance and timeliness of the topic and how it relates to the scope and vision of IJAIED
  • Planned timing of the process, as in the examples below.
  • Additional details of the topics that will help indicate the scope of the special issue, as below
  • Overview of potential authors and the work they are doing in the area.

The following thematic issues have closed for submissions and the submissions are under review. You can look forward to these in the coming issues of the journal.

2018 Fast Review Cycle

Motivation and Scope

This track maintains rigorous journal reviewing, typically involving revision cycles which lead to papers of the highest quality.

At the same time, we gain the benefits of strict deadlines for paper submission and review common to peer-reviewed conference proceedings. The benefit to reviewers is the predictability of workload since they pre-commit to do reviews right after the submission deadline. For authors, the benefit is that we can achieve tighter decision cycles, while maintaining high quality reviews.

Schedule for Fast Review Cycle 1 in 2018:

  • Paper deadline: 7 Sep 2018
  • Decision to authors: 15 Oct 2018
  • Revised paper due: 15 Nov 2018
  • Decision: 15 Dec, 2018

Authors of accepted papers from this cycle will be invited to present in the journal track of AIED 2019

Schedule for Fast Review Cycle 2 in 2018:

  • Paper deadline: 7 Oct 2018
  • Decision to authors: 15 Nov 2018
  • Revised paper due: 15 Dec 2018
  • Decision: 31 Jan, 2018

Authors of accepted papers from this cycle will be invited to present in the journal track of AIED 2019

Schedule for Fast Review Cycle 1 in 2019:

  • Paper deadline: 1 May 2019
  • Decision to authors: 15 June 2019
  • Revised paper due: 15 July 2019
  • Decision: 15 Aug, 2019

Authors of accepted papers from this cycle will be invited to present in the journal track of AIED 2020

2017 International Journal of Artificial Intelligence in Education Themed Issue

Motivation and Scope

International Journal of Artificial Intelligence in Education publishes interdisciplinary work at the frontiers of the fields of computer science, education and psychology. It welcomes papers that report on rigorous research and development of interactive and adaptive learning environments for learners of all ages, across all domains.

This CFP invites submissions in the following three thematic areas that the Editors have identified as particularly timely:

  • Beyond cognitive: This reflects the importance of research that tackles challenges of creating of technology and pedagogies that tackle the many aspects of learning that go beyond the cognitive to include affect, motivation and emotion. We particularly welcome work that aims to link pedagogic theory to the growing work that uses various sensors to model the affective state of each learner on a moment by moment basis.
  • Beyond the classroom: This theme is for the growing body of work that tackled life-long and life-wide learning, beyond the classroom to include informal learning, games and learning for health and wellness.
  • Beyond positives: There is a danger that publications are biased against work that results in negative and null findings and this theme invites papers reporting rigorous research that does report such findings.

Learning at Scale: What Works & Lessons Learned

Thematic Issue Associate Editors

Daniel M. Russell email Google, Inc., USA
Ido Roll email University of British Columbia, Canada
Dragan Gasevic email University of Edinburgh, UK

Motivation and Scope

The annual Learning at Scale conference series (http://learningatscale.acm.org/las2016/) has created a track record of high quality papers about the practices, methods, and science for teaching large numbers of students. This special issue will consolidate what we have found that works well in terms of pedagogy, technology, analytics, and theory. Overall, this special issue will make a contribution to the science and practice of large online learning/teaching systems.

For this special issue we solicit paper submissions reporting on rigorous research on methodologies, studies, analyses, tools, pedagogies, or technologies for learning at scale. Learning at Scale includes MOOCs, games (including massively multiplayer online games), citizen science communities, and other types of learning environments which (a) provide learning experiences to large number of learners and/or (b) produce detailed, high volume data about the learning process.

Papers that tackle specific aspects of scale are particularly encouraged, for example, papers that deal with learning or educational phenomena that can only occur, be supported, or be observed with very large numbers of students, or in which the system improves after being exposed to data from previous use by many students.

Topic of Interests

The scope includes (but is not limited to) the topics:
  • Usability studies and effectiveness studies of design elements for students or instructors, including:
    • Status indicators of student progress
    • Status indicators of instructor effectiveness
    • Tools and pedagogy to promote community, support learning, or increase retention in at-scale environments
  • Analysis of log data about student behavior, e.g.:
    • Assessing reasons for student outcome as determined by modifying tool design
    • Modeling students based on responses to variations in tool design
    • Evaluation strategies such as quizzes or discussion forum design
    • Instrumenting systems and data representation to capture relevant indicators of learning.
  • Personalization and adaptation, based on log data, user modeling, or choice.
  • Studies of applications of existing and newly developed learning theories to the MOOC context (peer learning, project based learning, etc.).
  • Large online learning in the developing world
  • New tools and techniques for learning at scale, including:
    • Games for learning at scale
    • Automated feedback tools (for essay writing, programming, etc)
    • Automated grading tools
    • Tools for interactive tutoring
    • Tools for learner modeling
    • Interfaces for harnessing learning data at scale
    • Innovations in platforms for supporting learning at scale
    • Tools to support for capturing, managing learning data
    • Tools and techniques for managing privacy of learning data
  • Investigation of observable student behaviors and their correlation with learning, e.g.:
    • What do more successful learners do more of?
    • What do more successful instructors do more of?
    • Self- and co-regulation of learning at scale
    • Collaborative learning in courses that have scale
    • Depth and retention of learning and understanding
  • Improvements to learning, community, and pedagogy in large-scale in-person and blended online and in-person courses
    • Instructional principles for learning at scale
    • Facilitation of informal subcommunities

Generalized Intelligent Framework for Tutoring (GIFT): Creating a stable and flexible platform for innovations in AIED research

Thematic Issue Associate Editors

Robert Sottilare, email Army Research Laboratory, USA
Arthur Graesser, email University of Memphis, USA
James Lester, email North Carolina State University, USA
Ryan Baker, email Teachers College of Columbia University, USA

Motivation and Scope

Over the last five years, the Generalized Intelligent Framework for Tutoring (GIFT) has emerged as a standard for authoring, deploying, managing, and evaluating Intelligent Tutoring System (ITS) technologies. A goal for GIFT is to capture best practices across the spectrum of automated instruction to reduce the time and skills needed to author tutors, to enhance the effectiveness of instructional strategies implemented by tutors, and to provide a testbed for ITS researchers to evaluate various adaptive instructional tools and methods. GIFT has been used to construct and evaluate tutors in various domains including management of interaction with learners in existing external simulations, serious games, and computer-based training environments to teach physics (e.g., Newtonian Talk), training military tasks and tactics (e.g., Virtual BattleSpace and Virtual Medic), and solve cognitive problems (e.g., logic and Sudoku puzzles). To date, nearly 700 users in 50 countries and 70 organizations have used and helped improve the authoring tools, individual learner and team models, instructional management techniques, domain models, learning effectiveness evaluation tools, and architectural services in GIFT, but there is a long way to go to realize a fully generalizable architecture for cognitive, affective, physical, and social training and educational environments.

A catalyst for this special issue is the GIFT Symposium (GIFTSym) series which was originally organized as a workshop at the AIED 2013 in Memphis. GIFTSym continues annually with published proceedings and provides a forum for GIFT users and stakeholders to discuss their successes and challenges in using and evaluating GIFT across domains and learner populations. Scientists outside the GIFT user community have also participated in GIFTSym to provide critique on both the design and implementation of GIFT as a generalized tutoring architecture.

A goal of this special issue is to identify new best practices for GIFT and the ITS community. We also seek innovative AI contributions which provide the community a platform or testbed in which to conduct their research and guide them through the experimentation and analysis processes. There remain challenges with authoring ITSs (e.g., time and specialized skills required), delivering and consumption of instruction (e.g., remote sensing and intermittent connectivity), instructional management (e.g., methods to tailor instruction and selection of optimal strategies), and evaluation methods (e.g., time and skill required to set up evaluations, and consistency of evaluation methods).

This special issue also seeks innovative contributions for AI-based tools and methods which reduce experimental workload and facilitate the evaluation of ITS technology from a researcher’s point-of-view. In addition to specific designs and implementations in GIFT, we are seeking opportunities to enhance GIFT tools and methods to more efficiently acquire and analyze leaner and environment data, assess learner and team states, reduce authoring burden, and select optimal strategies and tactics. Literature reviews and meta-analyses that provide a thorough overview of the state of the art related to some aspects of the above-mentioned problems are also welcome.

Topic of Interests

The scope includes (but is not limited to) the topics:
  • Architectural topics
    • Service-oriented architectural design features for GIFT (or similar ITS frameworks)
    • Multi-agent architectural designs to support learner assessment in GIFT (or similar ITS frameworks)
    • ITS interoperability standards for reuse
    • Team tutoring architectures
  • Authoring tools and methods
    • AI-based authoring tools for tutoring tasks in various domains (cognitive, affective, physical, and social/collaborative)
    • Integrating interactive environments (e.g., simulations and serious games) with GIFT for adaptive training
    • Augmentation technologies for adaptive instruction
    • Enhancing user experiences (UXs) for ITS authoring tasks
  • Individual learner and team modeling
    • Real-time vs. long-term modeling of individual learner and team knowledge acquisition, skill development and performance
    • Interoperable learner models
    • Low cost, unobtrusive sensing and learner state classification
    • Intelligent support develop critical thinking and problem solving skills
  • Instructional management strategies
    • AI-based learning and instructional strategies
    • Cognitive and metacognitive support strategies
  • Domain modeling
    • AI-based adaptation and personalization methods for learning environments
  • Effectiveness Measures
    • Measures of learning and performance effect for individual learners and teams
    • Tools for educational informatics in GIFT (or similar ITS frameworks) to support learning at scale