Call for Late-Breaking Results
We are pleased to invite you to contribute to the program of AIED2022 by submitting your late breaking results. The late-breaking results track offers an opportunity for presenting compelling, preliminary results and innovative work in progress. The goal is to give new, but not necessarily mature work a chance to be seen by other researchers and practitioners and to be discussed at the conference. Accepted submissions will be presented during the conference as posters.
The 23rd international conference on Artificial Intelligence in Education will take place between 27-31 July, 2022 at the University of Durham (UK) and virtually. AIED2022 will be collocated with EDM2022. Its 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 has shaped and can shape education for all sectors, how to advance the science and engineering of intelligent interactive learning systems, and how to promote their broad adoption. Engaging with the various stakeholders – researchers, educational practitioners, entrepreneurs, businesses, policy makers, 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.
AIED 2022 is 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
- Vania Dimitrova. University of Leeds, United Kingdom
- Maria Mercedes (Didith) T. Rodrigo, Ateneo de Manila University
- Noboru Matsuda, North Carolina State University
- Alexandra I. Cristea, Durham University
Posters and Late-Breaking Results Co-chairs
- Carrie Demmans Epp, University of Alberta
- Sergey Sosnovsky, Universiteit Utrecht
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.
Late-breaking results submissions 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.
Maximum paper length is as follows:
- Late-breaking results papers (4 pages including references; for a poster presentation)
All submissions are handled via EasyChair: https://easychair.org/conferences/?conf=aied2022
Please check the dates page.