IJAIED Special Issues (2019-2020)

Current special issue opportunities include:

  • Special Issue: THE FATE of AIED: Fairness, Accountability, Transparency, and Ethics (abstracts due: Jan 31, 2020; papers due: Mar 30, 2020)
  • Special Issue: Creating and Improving Adaptive Learning: Smart Authoring Tools and Processes (Submission of Complete Manuscripts due: Feb 1, 2020, First decision due to authors: Apr 1, 2020, Revisions due: May 15, 2020, Final decisions to authors: July 15, 2020)
  • Fast Review Track: Journal submissions that follow defined deadlines for submission and review
  • Special Issue: AI4MOOCs: Artificial Intelligence, Sensoring, Modeling and Assessment for MOOCs. A Step Beyond.
  • CFP for Special Issues: Proposals for future special issues in IJAIED

Details on these opportunties are below.

Special Issue: The FATE of AIED

Kaśka Porayska-Pomsta, University College London

Beverly Woolf, The University of Massachusetts Amherst

Wayne Holmes, The Open University

Ken Holstein, Carnegie Mellon University

Additional information can be found here

This special track is titled the FATE of AIED: Fairness, Accountability, Transparency, and Ethics. This IJAIED special issue seeks to move conversations about the FATE (Fairness, Accountability, Transparency, and Ethics) of AI in Education forward and to situate the field of AIED and EdTech within the broader developments and debates around the ethical dimensions of AI. In this context, we invite researchers who work on AI-supported education and EdTech to directly engage with concerns about the present and future roles of AI in education. The aim of the special issue is to develop a better understanding of how past and present AIED and EdTech efforts can contribute to rigorous and forward thinking about and practices related to FATE dimensions of AI and data sciences more broadly. Educational technologies that authors may not conceptualize as "AI" per se are still within scope. In this context, we seek contributions that:

  1. Situate AIED and EdTech research to date in the context of FATE of AI and that explicate why knowledge generated through AIED and EdTech research might affect how we interpret and operationalise FATE for education-oriented applications;
  2. Address how the definitions and concrete operationalisation of FATE dimensions can be informed by AIED and EdTech research; explore and explicate the relationship between AI interventions, human cognition, pedagogy, and FATE considerations and practices;
  3. Provide an evidence-based commentary on the future outlook and practices needed in relation to FATE of AIED and EdTech more specifically, and/or on the role of AIED and EdTech research in informing the creation of AI applications in other than education-oriented contexts.

Given the above, we seek the following types of papers:

  • Empirical work demonstrating how FATE is achieved in AIED or EdTech applications;
  • Methodological contributions showing how FATE considerations are taken into account during the design, implementation and deployment of AIED or EdTech applications;
  • Historical reflections on AIED or EdTech research or a subarea of AIED and EdTech research that provide concrete and evidence-based examples of how the AIED and EdTech perspective can and should be reflected in the FATE of AI more broadly;
  • Theoretical frameworks explicating (i) the nature of FATE as dimensions of relevance to AIED or EdTech, and/or (ii) how FATE are or can be instantiated in different AIED or EdTech contexts and applications;
  • Papers that identify interdisciplinary research and practices of critical importance to FATE of AIED or EdTech and that demonstrate how this research can or ought to be operationalised in AIED or EdTech approaches.

The list of “types of papers” above is not intended to be exhaustive. If you have an idea for a paper that does not fall within one of the above categories, we would strongly encourage you to contact us to discuss the potential fit (see “Submission Process and Deadline” for more details).

Topics of Interest

Topics of interest include but are not strictly limited to:

Bridging between AIED, EdTech and FATE

  • Understanding the nature of FATE (fairness, accountability, transparency and ethics) within AIED or EdTech, as distinguished from FATE in AI more broadly
  • FATE of AIED or EdTech algorithms and modelling, including explainable systems
  • AIED and EdTech, inclusion and equity (e.g., United Nations Sustainable Development Goal 4)
  • AIED and EdTech data ethics (including privacy and data governance)
  • Technical robustness and safety in AIED or EdTech systems
  • AIED and EdTech and the law
  • AIED and EdTech and FATE: existing principles and the development of new principles

The FATE of AIED & EdTech systems in real world contexts

  • Ethics of and trust in AIED or EdTech development and deployment
  • Human autonomy and agency in the context of AIED and EdTech
  • Human oversight and social impact in AIED & EdTech
  • AIED and EdTech and human diversity, including identity, socio-economic, cultural, and neuro- diversity, with considerations to different forms of potential harm (e.g., harms of allocation and representation) Deepening our understanding of the FATE of AIED and EdTech
  • FATE of particular pedagogical features (e.g., the use of chatbots and agents)
  • FATE of learning and teaching interventions, including specific pedagogical assumptions embedded in different types of AIED or EdTech approaches (e.g., instructionism vs constructivism, personalisation vs collaboration, knowledge acquisition vs self-actualisation)
  • The ethics of AIED and EdTech, including the ethics of educational practices (e.g., potential impacts on students, teachers, classrooms and educational institutions)

The FATE of research in AIED

  • Ethics of AIED and EdTech research
  • FATE, AIED and EdTech research methods (e.g., the balance between research objectives and informed consents, such as the use of WOZ methods for ecological validity and informedness/deception of WOZ participants)

Submission Process and Deadline

Prospective authors are encouraged to submit a brief abstract (no more than 350 words) in advance in order to check fit with this special issue. The editors will reply with feedback on submitted abstracts within two weeks.

Deadline for abstract (not required): Friday, Jan 31, 2020.

Deadline for submission of special issue paper: Monday, March 30, 2020.

First round of reviews returned to authors: Monday, June 1, 2020.

Deadline for submission of revised versions: Friday, July 3, 2020.

Special Issue: Creating and Improving Adaptive Learning: Smart Authoring Tools and Processes

Motivation and Scope

Cyberlearning technologies increasingly seek to offer personalized learning experiences via adaptive systems that customize pedagogy, content, feedback, pace, and tone according to the just-in-time needs of a learner. However, it is historically difficult to 1) create these smart learning environments, 2) continuously improve them based on student learning data, 3) facilitate their alignment with learning objectives and government standards, and 4) customize them to particular environments or users. For this special issue, we invite paper submissions that highlight successful tools used to create, improve, align, and customize adaptive learning environments. Authoring systems may range from fully human-authored to fully automated; they might incorporate human input at the student, instructor, administrator, or external levels; and they might use data-driven techniques to create or improve aspects of adaptive learning environments. Papers with rigorous evaluations are particularly encouraged.

Topics of Interest Topics of interest arise from the themes of adaptability and learning technology, and can include (but are not limited to):


  • Authoring tools that create personalized and adaptive learning experiences or smart learning environments
  • Creation and management of MOOCs with adaptive content
  • Tools to support creation and improvement of computerized adaptive testing (CAT)
  • Creation of educational agents
  • Authoring systems for science centers and other informal learning environments
  • Authoring for emerging educational technologies like VR/AR/XR if content is adaptive
  • Crowd-sourcing, teacher-sourcing, learner-sourcing of adaptive learning technologies
  • Data-driven improvement of adaptive learning technologies, real-time and not real-time, including data acquired by ongoing A/B experiments
  • Data-driven approaches and machine learning for creating adaptive learning technologies
  • Teachers and trainers as authors for adaptive systems
  • Technologies that aid adaptation of systems for specific demographics, e.g., a particular country or underserved population
  • Quality assurance for adaptive learning environments
  • Ensuring that learners who engaged a learning environment via different paths still met learning objectives
  • Facilitation of alignment with learning objectives and government standards
  • Adaptability and configurability of systems, to tailor them to local contexts, teacher preferences, student interest, and so forth
  • Systems that control learning experiences via real-time instructor control


Special Issue Associate Editors

Stephen B. Gilbert Iowa State University

Andrew M. Olney University of Memphis

Kelly Rivers Carnegie Mellon University

Important Dates

  • Submission of Complete Manuscripts - February 1, 2020
  • First decision due to authors - April 1, 2020
  • Revisions due - May 15, 2020
  • Final decisions to authors - July 15, 2020

Camera-ready version - August 31, 2020

Publication of Special Issue - Each paper will appear on the Online First soon after it has been accepted and processed.

The full Special Issue will be assembled in the third quarter of 2020.

Submission instructions Submit papers here using “Special Issue: Smart Authoring Tools and Processes”

Call for Papers for the IJAIED Fast Review Track Cycles

The International Journal of Artificial Intelligence in Education (IJAIED) invites submissions for the 2019 Fast Cycle. This track means that authors and reviewers get the benefits of predictable deadlines. This track maintains rigorous journal reviewing, including revision cycles which lead to papers of the highest quality.

Compared to standard submissions, this track uses 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.

Fast track review submissions for 2018 have concluded, with these accepted articles also presented in the journal track of AIED 2019. The first cycle for 2019 is in progress.

Submission Process and Deadline

  • Paper deadline: 7 Oct 2019
  • Authors submit paper to EM + abstract on EasyChair
  • Decision to authors: 15 Nov 2019
  • Revised paper due: 15 Dec 2019
  • Decision: 31 Jan 2020

Instructions about scope, format and advice for authors.

Please see the advice for authors at the IJAIED Society website and the IJAIED Springer website.

Recent metrics for IJAIED.

  • Scopus 2018 Citescore 3.68.
  • This is 35/1040 in Social Sciences / Education, putting it in the 96th percentile.
  • It s 12/113 in Computer Science/Computational Theory and mathematics which is the 89th percentile.

Special Issue: AI4MOOCs: Artificial Intelligence, Sensoring, Modeling and Assessment for MOOCs. A Step Beyond.

• Lead Guest Editor: Filippo Sciarrone, ROMA TRE University, Engineering Department

• Carla Limongelli, ROMA TRE University, Engineering Department

• Olga C. Santos, aDeNu Research Group. UNED

• Marco Temperini, DIAG-Department of Computer Science, Sapienza University of Rome

Motivation and Scope

The demand for Distance Education has been dramatically growing in recent years, also as a consequence of the huge and increasing availability of systems supporting e-learning through the internet. People, geographically and culturally spread across the globe, companies, practitioners, students, and Communities of Practice with thousands of learners, are involved in networked learning programs. Thanks to the Internet, the 21st century seems to be the Century of lifelong learning.

Massive Open Online Courses (MOOCs), are courses characterized by having a very high number (in the thousands, or more) of students. These courses, mostly free, are offered through special web-based platforms, with the provision of video-based teaching materials, interactive assessment tools, and some social interaction or collaboration means. While, on the one hand, these courses help thousands of students, on the other hand they introduce a strong problem for tutors, who can have a hard life at monitoring the learning process of such an extended class of students. This tutoring support presents challenges that relate to cognitive, affective and even psychomotor aspects (in this last case, towards supporting embodied learning in massive online learning contexts).

Artificial Intelligence in general and Machine Learning in particular propose techniques and tools to study, model, and manage such a complex reality.

We encourage in particular the submission of articles where AI techniques and methods are used to provide automated support to the cognitive, affective and even when possible, psychomotor modeling of students and to the assessing of competences in a MOOC scenario that can be enriched with traditional interaction devices and/or emerging sensors, which are available in traditional computerized learning scenarios (such as webcam, keyboard and mouse), in mobile learning context (which in addition to mobile cameras can also collect information from the inertial and physiological sensors available in smart phones) and even in smarter learning scenarios which can also make use of other sensors such as smart bracelets or virtual reality head mounted displays. Such support would be directed to teachers, in order to allow monitoring the learning process; to students, such as in the case of course adaptation, or for the support to individual self-reflection, about own performances, and decision-taking about what learning experience to select; to course managers, or teachers again, to appreciate the MOOC’s inner dynamics, and wisely guide them.

Topics of interest

Topics of interest include but are not strictly limited to:

  • Technology-enhanced learning and Peer Assessment in MOOCs
  • Deep learning and MOOCs
  • Learners’ Evaluation in MOOCs
  • MOOCs dynamics and Modeling
  • Recommendation of learning units in MOOCs
  • Technology-Enhanced learning and MOOCs
  • Mobile-based technologies for recommendations and adaptivity in MOOCs
  • Learning Analytics Models for MOOCs
  • Visual presentation of data in MOOCs
  • Design and implementation of adaptive e-learning systems in MOOCs
  • Teacher and student modeling in Technology-enhanced learning for MOOCs
  • Artificial Intelligence and Embodied Learning in MOOCs

Important Dates

First round: extended abstract submission (easychair.org)

Second round: full paper submission (IJAIED editorial manager)

  • File format: follow the instructions at the Editorial Manager of the JAIED
  • Submission deadline: January 31th, 2020
  • Acceptance notification: March 31th, 2020
  • Revisions submission: May 15th, 2020
  • Publication: All papers will appear online as soon as they have been accepted

Additional information can be found here

Special Issue Editors

Filippo Sciarrone is a fellow researcher since 1994, at the Roma Tre University, where he has been collaborating in many research activities of the Artificial Intelligence research group and received his Ph.D. with a dissertation on user modeling. Since several years he has also been collaborating with Sapienza University in Rome, giving his main contribute in the application of Machine Learning techniques to educational research projects. He has led several research laboratories of private companies for the production of algorithms and innovative systems for human resource management and for teaching-oriented recommendation systems. His research interests are in the design of hybrid architectures, machine learning and systems to support learning and teaching.

OLGA C. SANTOS current research focuses on combining Artificial Intelligence with Ambient Intelligence and Internet of Things to support personalized affective psychomotor learning that ubiquitously and dynamically adapts to the evolving user needs. She has participated in 16 research projects (UE, National), published over 150 papers and co-chaired several workshop series (TUMAS-A, RecSysTEL/EdRecSys, PALE, RSyL) and conferences (AIED, EDM, UMAP, EC-TEL). She received the Best Doctoral Thesis Award by the IEEE Spanish Chapter of the Education Society and the 2014 Young Researcher Award of the IEEE Technical Committee on Learning Technology. She has been involved in the AIED community since 2003, with diverse contributions both at AIED conference and IJAIED.

Marco Temperini is a tenured Associate Professor of Engineering in Computer Science, with the Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Italy. His research activity is focused on Technology Enhanced Learning, and on the design and implementation of computer and network based systems for personalized and adaptive learning, social collaborative learning, game based learning, automated evaluation of programming tasks, peer assessment, and on the pedagogical aspects of such systems. Since 1999 he is involved in international (EU funded) research projects, as National Unit leader, and workshop/Intellectual Output leader.

Carla Limongelli is a tenured Associate Professor of Engineering in Computer Science at ROMA TRE University. Her research focuses on Intelligent Adaptive Learning Environments, User Modeling and User-Adapted Interaction, collaborative learning environments, and intelligent and adaptive retrieval of didactic materials from the Internet. She has been developing social-based approach for retrieving and sequencing didactic materials from the web and from Learning Objects Repositories. Currently, she leads the AI research group in Education, at Roma Tre University, Engineering Department.

Call for Proposals - Special Issue Themes

We are now accepting proposals for new thematic special 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
  • 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
  • Additional details of the topics that will help indicate the scope of the special issue
  • Overview of potential authors and the work they are doing in the area.

The following 2017-2018 thematic issues have closed for submissions and the submissions are under review. You can view these in the recent issues of the journal:

  • 2017 Themed CFP: Beyond Cognitive, Beyond Classrooms, Beyond Positive
  • Learning at Scale: What Works & Lessons Learned
  • Generalized Intelligent Framework for Tutoring (GIFT): Creating a stable and flexible platform for innovations in AIED research