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AIED 2022 : Artificial Intelligence in Education

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Conference Series : Artificial Intelligence in Education
 
Link: https://aied2022.webspace.durham.ac.uk/
 
When Jul 27, 2022 - Jul 31, 2022
Where Durham University
Abstract Registration Due Jan 31, 2022
Submission Deadline Feb 7, 2022
Notification Due Apr 25, 2022
Final Version Due May 23, 2022
 

Call For Papers

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.

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