AIED2022のご案内です。今回のAIEDは、日本にゆかりの方々がコミッティーメンバに入られています。かつて電気通信大学大学院におられた松田昇先生がPCチェアを担当されます。また、LOCは（これまた） かつて電気通信大学大学院におられたアレクサンドラ・クリステアさんが担当されます。

ふるってのご投稿・ご参加をお待ちしております。

General Call for Papers

The 2022 conference on Artificial Intelligence in Education will take place between July 27 and 31, 2022, at the University of Durham (UK) and possibly virtually.

The conference theme will be: 

AI in Education: Bridging the gap between academia, business, and non-profit in preparing future-proof generations towards ubiquitous AI.

The conference sets the ambitious goal to stimulate discussion on how AI shapes and can shape education for all sectors, how to advance the science and engineering of intelligent 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 intelligent human-technology ecosystems that support learning.

Potential topics related to the conference theme include (but not limited to):

• Ubiquitous AI in Education
• Alliances and partnerships between sectors to develop or use AI in Education
• Multicultural aspects of AI in Education
• Supporting underachieving students
• Cultural and population differences
• AI in Education for underserved communities and contexts
• Addressing gender and sex-based biases
• Equity, diversity, and inclusion in the community

AIED 2022 will be the 23rd edition 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. AIED 2022 solicits empirical and theoretical papers, particularly (but not exclusively) in the following lines of research and application:

• Intelligent and Interactive Technologies in an Educational Context: Natural language processing and speech technologies; Data mining and machine learning; Knowledge representation and reasoning; Semantic web technologies; Multi-agent architectures; Tangible interfaces, wearables 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 analyzing activity flow and discourse during learning.
• Models of Teaching and Learning: Intelligent tutoring and scaffolding; Motivational diagnosis and feedback; Interactive pedagogical agents and learning companions; Agents that promote metacognition, motivation and affect; Adaptive question-answering and dialogue, Educational data mining, Learning analytics and teaching support, Learning with simulations 
• Learning Contexts and Informal Learning: Educational games and gamification; 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, museum, out-of-school, and workplace learning.
• Evaluation: Studies on human learning, cognition, affect, motivation, 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); Scaling up and large-scale deployment of AIED systems.
• Inequity and inequality in education: socio-economic, gender, and racial issues. Intelligent techniques to support disadvantaged schools and students.  Ethics in educational research: sponsorship, scientific validity, participant’s rights and responsibilities, data collection, management and dissemination.
• 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 and distance learning: massive open online courses; remote learning, in k-12 schools; synchronous and asynchronous learning; mobile learning; active learning in virtual settings.

Submission Instructions

There are three tracks where you can submit your work: (1) the main track, (2) the industry and innovation track, and (3) the doctoral consortium track.

The main track is a collection of technical papers.  For the main track, there are three categories of submissions 

(1) Full paper submission: 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. 

(2) Short paper submission: 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. 

(3) Extended abstract submission: Extended abstracts describe the early stage of research.  The goal is to receive constructive input to further develop research ideas. Extended abstract will be presented as posters.

The industry and innovation track is a collection of technical papers with a particular goal to support connections between industry (both for-profit and non-profit) and the research community and to share experiences about how to bridge the gap between research and innovation in the field of AI and education. The introductory and innovation track papers will be presented as short oral presentations and also as posters/interactive events.

The doctoral consortium track is an interactive event to support PhD candidate scholars working in domains relevant to the interdisciplinary research areas of AIED. In the consortium, doctoral students will share and discuss their research ideas and plans with more experienced colleagues (or “mentors” if you will), who will provide feedback on various aspects of student’s work including the theoretical framing and the methodological approaches.

All submissions will be reviewed by the program committee to meet rigorous academic standards of publication. The review process will be double-blind review process, 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 to your 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.

Full papers, short papers, extended abstract, industry and innovation track papers, and doctoral consortium papers will be published by Springer Lecture Notes in Artificial Intelligence (LNAI), a subseries of Lectures Notes in Computer Science (LNCS). 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. In addition, the corresponding author of each paper, acting on behalf of all of the authors of that paper, must complete and sign a Consent-to-Publish form. The corresponding author signing the copyright form should match the corresponding author marked on the paper. Once the files have been sent to Springer, changes relating to the authorship of the papers cannot be made. For further details about the format, please see

https://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0.

Springer encourages authors to include their ORCIDs in their papers.

Maximum paper length is as follows:

• Full papers (10 pages + references; for a long oral presentation)
• Short papers (4 pages + references; for a short oral presentation)
• Extended abstract (2 pages, including references; for a poster presentation)
• Industry and innovation track papers (4 pages + references; for a short oral presentation and a poster/interactive event)
• Doctoral consortium papers (4 pages + references; for roundtable sessions with group discussions)

See important dates (including submission dates) at: Dates – AIED2022 (durham.ac.uk)

All submissions are handled via EasyChair:  https://easychair.org/conferences/?conf=aied2022