AIED 2020 : Artificial Intelligence in Education

  in Conferences   Posted on June 4, 2020

Conference Information

Submission Deadline Thursday 20 Feb 2020 Proceedings indexed by :
Conference Dates Jul 6, 2020 - Jul 10, 2020
Conference Address Virtual Event, Online
Conference & Submission Link
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Ranking & Metrics (This is a TOP Conference)

Guide2Research Overall Ranking: 300
Category Rankings
Machine Learning & Arti. Intelligence 70
Image Processing & Computer Vision 37
Number of Editions: 21
CORE 2018 Rating: A

Google Scholar H5-index:

Conference Call for Papers

The AIED 2020 conference theme will be “Augmented Intelligence to Empower Education”. As AI in Education systems get more mature and are implemented at scale in real-world contexts, the value of using AI systems in the service of human decision making, rather than automated personalisation, becomes more apparent than ever. While this paradigm of augmented intelligence is not new to the field,solid theoretical and/or empirical work in the area is limited. Further work is needed to understand the balance of human and artificial aspects in intelligence systems that involve human and AI partnerships. Developing and implementing AI-human hybrid systems requires new research and many questions remain to be answered. In this conference, we invite the community to think about intelligence augmentation opportunities that would empower key stakeholders of education and provide innovative and creative solutions supported with empirical evidence.

AIED 2020 will be the 21th edition of a longstanding series of international conferences, formerly bi-annual and now annual, known for high quality research on intelligent systems and cognitive science approaches for educational computing applications. AIED 2020 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.
Intelligent techniques to support disadvantaged schools and students. Inequity and inequality in education: socio-economic, gender, and racial issues. 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.

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