General Call for Papers (Main Track)
The 24th International Conference on Artificial Intelligence in Education (AIED 2023)
will take place between July 3-7, 2023 in Tokyo, Japan and virtually.
The AIED 2023 theme is
“AI in Education for Sustainable Society”
Abstracts due: January 9, 2023; Papers due: January 16, 2023
A sustainable society is driven by the principle of realising peace and prosperity for all people and the planet with an inclusiveness that “leaves no one behind.” As the AIED community, we concern ourselves with the mission of using Artificial Intelligence (AI) to contribute to a world with equitable and universal access to quality education at all levels (UN Sustainable Development Goal 4: https://sdgs.un.org/goals) . Towards this goal, we set this year's theme as "AI in Education for Sustainable Society" and invite authors to present research on how AI in education can help our society meet its need to provide inclusive and equitable quality education and to promote lifelong learning opportunities for all. The conference sets the ambitious goal to stimulate discussions on how AI shapes and can shape education for all sectors, how to advance the science and engineering of AI-assisted interactive learning systems, and how to promote broad adoption. Engaging with the various stakeholders – researchers, educational practitioners, businesses, policy makers, as well as teachers and students – the conference will set a wider agenda on how novel research ideas can meet practical needs to build effective AI-assisted human-technology ecosystems that support learning.
AIED 2023 will be the 24th edition and the 30th anniversary of the International AIED Society. The AIED Society organises the AIED conference and is aimed at advancing science and engineering of intelligent human-technology ecosystems that support learning. The conference will be the latest of a longstanding series of international conferences, known for high quality and innovative research on intelligent systems and cognitive science approaches for educational computing applications. To celebrate the 30th anniversary of the AIED Society, we invite papers exploring how researchers envision the way AIED can shape the future of education in the next 30 years. AIED 2023 solicits empirical and theoretical papers particularly (but not exclusively) in the following lines of research and application:
- AI-assisted and Interactive Technologies in an Educational Context
- Natural language processing and speech technologies; Data-driven processing techniques (educational data mining, deep learning, machine learning,...); Knowledge representation and reasoning; Semantic web technologies; Multi-agent architectures; Tangible interfaces, Wearables; Virtual and augmented reality.
- Modelling and Representation
- Models of learners, including open learner models; facilitators, tasks and problem-solving processes; Models of groups and communities for learning; Modelling motivation, metacognition, and affective aspects of learning; Ontological modelling; Computational thinking and model-building; Representing and analysing activity flow and discourse during learning; Representing and modelling psychomotor learning.
- Models of Teaching and Learning
- AI-assisted tutoring and scaffolding; Motivational diagnosis and feedback; Learner engagement; Interactive pedagogical agents and learning companions; Agents that promote metacognition, motivation and positive affect; Adaptive question-answering and dialogue; Data-driven modelling (educational data mining, deep learning, machine learning,...); Learning analytics and teaching support; Learning with simulations; Explainability of models for teaching and learning.
- Learning Contexts and Informal Learning
- Game-based learning; Collaborative and group learning; Social networks; Inquiry learning; Social dimensions of learning; Communities of practice; Ubiquitous learning environments; Learning through construction and making; Learning grid; Lifelong learning; Learning in informal settings (museum, workplace, etc.); Learning in the physical space; Learning of motor skills.
- Studies on human learning, cognition, affect, motivation, engagement, and attitudes; Design and formative studies of AIED systems; Evaluation techniques relying on computational analyses.
- Innovative Applications
- Domain-specific learning applications (e.g. language, science, engineering, mathematics, medicine, military, industry, sports and more); Scaling up and large-scale deployment of AIED systems.
- Equity and Inclusion in Education
- Socio-economic, gender, and racial issues; Intelligent techniques to support students from under resourced schools and communities; Sponsorship, scientific validity, participant’s rights and responsibilities, data collection, management and dissemination.
- Ethics and AI in Education
- explainability, transparency, accountability, responsible AIED, adoption, involvement of teachers and learners.
- Explore Design, Use, and Evaluation of Human-AI Hybrid Systems for Learning
- Research that explores the potential of human-AI interaction in educational contexts; Systems and approaches in which educational stakeholders and AI tools build upon each other’s complementary strengths to achieve educational outcomes and/or improve mutually.
- Online Learning Spaces
- Massive open online courses; Remote learning in k-12 schools; Synchronous and asynchronous learning; Mobile learning; Active learning in virtual settings; Video-based learning; Mixed reality and learning.
Diversity, Equity and Inclusion
The AIED Society values diversity, equity, and inclusion (and related principles under this broad umbrella) as essential and fundamental values for the AIED community to uphold. Thus, in AIED 2023, we incentivize authors to write with care toward inclusive language (e.g., understanding identify-first vs. person-first language, gender neutral language, appropriate demographic categories and terminology, and avoiding the conflation of distinct dimensions such as race and ethnicity, or sex and gender). In addition, reporting of methodology should explicitly describe sample characteristics (e.g., demographic data), any procedures for inclusive and representative sampling, and any barriers to inclusive and representative sampling. It is also important to use strategies to control or reduce bias against populations of any kind (e.g. benefit or bring prejudice to a particular gender, race, or people with different economic status) when collecting, using, or aggregating data. Finally, authors are encouraged to consider how their theoretical frameworks and findings are related to diversity, equity, and inclusion. For example, authors may discuss how these issues influence key assumptions, hypotheses, and methods. Likewise, authors might address implications or appropriate interpretations of their findings with respect to diversity, inclusion and equity.
We invite submission, full or short papers, to the main track:
- Full papers
- should present integrative reviews or original reports of substantive new work: theoretical, empirical, and/or in the design, development and/or deployment of novel concepts, systems, and mechanisms. Full papers will be presented as long oral talks.
- Short papers
- are expected to describe novel and interesting results to the overall community at large. The goal is to give novel but not necessarily mature work a chance to be seen by other researchers and practitioners and to be discussed at the conference. Short papers will be presented as short oral talks. Papers submitted as the full paper might be accepted as a short paper.
We will announce further details for other tracks and sessions within AIED 2023 including:
- Invited papers from the International Journal of AIED
- Industry and innovation track
- Practitioner track
- Doctoral consortium
- Interactive events and demos
Following the successful presentation format in AIED 2022, giving opportunities for synchronous, remote presentations, during AIED 2023 we will allow synchronous participation for researchers who cannot attend in person. Each accepted paper should be presented at the conference either online or face-to-face. Each accepted paper will be expected to have at least one author registered to the conference, called the registered author, for either online or face-to-face attendance. Any author can only be the registered author for one and only one paper.
All submissions will be reviewed by the program committee to meet rigorous academic standards of publication. The review process will be double-masked, meaning that both the authors and reviewers will remain anonymous. To this end, authors should: (a) eliminate all information that could lead to their identification (names, contact information, affiliations, patents, names of approaches, frameworks, projects and/or systems); (b) cite own prior work (if needed) in the third person; and (c) eliminate acknowledgments and references to funding sources. Papers will be reviewed for relevance, novelty, technical soundness, significance and clarity of presentation. It is important to note that the work presented should not have been published previously or be under consideration in other conferences of journals. Any paper caught in double submission will be rejected without review. Please consider the following criteria when reporting samples:
(1) Authors should be clear and specific about the composition of human-sourced data. Who were the participants? What was the distribution of gender, race, ethnicity, or related variables? If corpus data or training data were sourced from humans, a similar description could be offered.
(2) Skewed or non-representative samples would not necessarily trigger a "reject" decision, but authors should acknowledge the demographic imbalances and discuss the potential impact on data, results, or conclusions. A more compelling paper would describe steps taken to generate an inclusive and representative sample (this is basic science, but often overlooked for convenience).
(3) Authors should demonstrate some awareness of how equity, inclusion, accessibility issues impact their data, methods, products, or findings. How are different demographic groups or communities differentially connected to the work? People who are developing educational technologies need to think about access and use, for example. Corpus analyses need to address the impact of skewed/exclusive datasets and potential outcomes (e.g., algorithmic bias).
(4) Authors are encouraged to discuss/justify how demographic variables are included in the analyses. If they are not included or "covaried out" please justify. If they are included, what are the assumptions? Are there "categorical effects"? Are the effects of different demographic variables independent, interdependent, or intersectional? What valid conclusions can be drawn? What erroneous conclusions need to be avoided or tempered?
Submission Procedure and Publication of Accepted Contributions
All submissions must be in Springer format. Papers that do not use the required format may be rejected without review. Authors should consult Springer’s authors’ guidelines and use their proceedings templates, either for LaTeX or for Word, for the preparation of their papers. Springer encourages authors to include their ORCIDs in their papers. Submissions are handled via EasyChair:
Accepted AIED 2023 papers for the main track will be published by Springer Lecture Notes in Artificial Intelligence (LNAI), a subseries of Lectures Notes in Computer Science (LNCS). Paper lengths for the main track submissions are as follows:
- Full papers (12 pages including references; for a long oral presentation)
- Short papers (6 pages including references; for a short oral presentation)
Important Dates (full and short papers, main track)
Abstracts (mandatory) due: January 9, 2023
Papers due: January 16, 2023
Notification: April 3, 2023
All deadlines are Anywhere on earth (AoE).
- Noboru Matsuda, North Carolina State University, USA
- Vania Dimitrova, University of Leeds, UK
- Olga C. Santos, UNED, Spain
- Ning Wang, University of Southern California, USA
- Genaro Rebolledo-Mendez, University of British Columbia, Canada
- Maomi Ueno, University of Electro-Communications, Japan