The 2021 conference on Artificial Intelligence in Education will take place between June 14 and 18 2021, in Utrecht (Netherlands) and virtually.
Mindful of the current challenges that we face, the conference theme will be:
Mind the Gap: AIED for Equity and Inclusion.
Over the past decades, racial and other bias-driven inequities have persisted or increased, diversity remains low in many educational and vocational contexts, and educational gaps have increased. Despite efforts to address these issues, biases based on factors such as race and gender persist. These issues have come to the forefront with recent crises around the world. In this conference, we invite the AIED community to reflect on issues of equity, diversity, and inclusion in regards to the educational tools and algorithms that we build, how we assess the efficacy and impact of our applications, the theories that we build on and contribute to, and within the AIED society. The use of intelligent educational applications has increased, particularly within the past few years. As a community, development and assessment practices mindful of potential (and likely) inequities are necessary. Likewise, planful diversity, equity, and inclusion practices are necessary within the AIED society and home institutions and companies.
Potential topics related to the conference theme include:
- Promoting equity in research
- Biases in algorithms, AI, or applications
- Multicultural aspects of AI in Ed
- Supporting underachieving students
- Cultural and population differences
- AI in Ed for underserved communities and marginalized populations
- Gender and sex-based biases
- Equity, diversity, and inclusion in the community
- Data mining techniques to measure equity
AIED 2021 will be the 22nd 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 2021 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
For the main track, there are two categories of papers. 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 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 posters.
All papers 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, 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.
Maximum paper length is as follows:
- Full papers (10 pages + references)
- Short papers, presented as posters (4 pages + references)
- Industry and innovation track papers (4 pages + references)
- Doctoral consortium papers (4 pages + references)
All submissions are handled via EasyChair: https://easychair.org/conferences/?conf=aied2021