Call for Papers
AIED 2024 General Call for Papers (Main Track)
The 25th International Conference on Artificial Intelligence in Education (AIED 2024) will take place between July 8-12, 2024 in Recife, Brazil.
The AIED 2024 theme is “AI in Education for a World in Transition”
Abstracts due: January 29, 2024; Papers due: February 5, 2024
Education has always been about creating opportunities for people to develop new skills, competencies, and productive attitudes. Education goes beyond simply communicating knowledge and aims to teach individuals analytical and critical thinking, social skills, and human values, thus preparing them for society.
Rapid advances in Artificial Intelligence (AI) have created opportunities not only for personalized and immersive experiences but also opportunities for ad hoc learning by engaging with cutting-edge technology continually, extending classroom borders, from engaging in real-time conversations with large language models (LLMs) to creating expressive artifacts such as digital images with generative AI or physically interacting with the environment for a more embodied learning.
As a result, we now need new approaches and measurements to harness this potential and ensure that we can safely and responsibly cope with a world in transition. It is now evident that in order to move forward in terms of AIED practice, we need to consider AI Literacy and AI policy. Furthermore, in the past new technological advancements typically led to broadening social gaps. We envision that the triad of AIED, AI Literacy, and Fair, and Ethical AI will play a fundamental role in this world in transition and be the drivers for shaping meaningful changes in pedagogical practice, educational policies, and regulations.
This year’s theme for AIED is "AIED for a World in Transition". The conference aims to explore how AI can be used to enhance the learning experiences of students and teachers alike when disruptive technologies are turning education upside down. The conference seeks to stimulate discussion of how AI can shape education for all sectors, how to advance the science and engineering of AI-assisted 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 in new, complex, and unknown scenarios.
AIED 2024 will be the 25th edition of this conference series. The AIED Society organises the AIED conference and is aimed at advancing science and engineering of intelligent human-technology ecosystems that support learning. The conference will be the latest of a longstanding series of international conferences, known for high quality and innovative research on AI-assisted systems and cognitive science approaches for educational computing applications. AIED is ranked A in CORE (top 16% of all 783 ranked venues), the well-known ranking of computer science conferences. AIED 2024 solicits empirical and theoretical papers particularly (but not exclusively) in the following lines of research and application:
AI-assisted and Interactive Technologies in an Educational Context: Data-driven processing techniques (educational data mining, deep learning, machine learning); Knowledge representation and reasoning; Generative AI; Semantic web technologies; Multi-agent architectures; Tangible interfaces, Wearables; Natural language processing and speech technologies; Virtual and augmented reality.
Modeling and Representation: Models of learners, including open learner models; facilitators, tasks and problem-solving processes; Models of groups and communities for learning; Modeling motivation, metacognition, and affective aspects of learning; Ontological modeling; Computational thinking and model-building; Representing and analyzing activity flow and discourse during learning; Representing and modeling psychomotor learning.
Models of Teaching and Learning: AI-assisted tutoring and scaffolding; Motivational diagnosis and feedback; Learner engagement; Interactive pedagogical agents and learning companions; Agents that promote metacognition, motivation and positive affect; Adaptive question-answering and dialogue, Data-driven modeling (educational data mining, deep learning, machine learning,...); Learning analytics and teaching support, Learning with simulations; Explainability of models for teaching and learning.
Learning Contexts and Informal Learning: Game-based learning; 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 learning; Learning in informal settings (museum, workplace, etc.); Learning in the physical space; Learning of motor skills.
Evaluation: Studies on human learning, cognition, affect, motivation, engagement, 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, sports and more); Scaling up and large-scale deployment of AIED systems.
Equity and Inclusion in Education: Socio-economic, gender, and racial issues; AI-assisted techniques to support students from under-resourced schools and communities; Sponsorship, scientific validity, participant’s rights and responsibilities, data collection, management and dissemination.
Ethics of AI in Education: Explainability, transparency, accountability, and responsible AIED; learner consent and opt out; surveillance and privacy; the impact of AIED on teachers, learners and classrooms; teacher empowerment and student agency; the community’s responsibility for commercial applications; AIED ethical frameworks and principles for application.
AI Literacy: Skills and knowledge that enable individuals to understand, use, and critically evaluate AI; Definitions of AI literacy; Learning to use AI; Developing a basic understanding of how AI works; Learning to communicate and collaborate with AI; learning to live with AI, Understanding limitations and problems of AI.
AIED for Development: Focuses on leveraging AI technology to address and improve various aspects of development in societies and education, particularly in low and middle-income countries; AIED Divide; AIED Unplugged; Low-cost solutions; Low-tech solutions; Frugal Innovation; Jugaad Innovation.
Explore 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 Learning Spaces: Massive open online courses; Remote learning in k-12 schools; Synchronous and asynchronous learning; Mobile learning; Active learning in virtual settings; Video-based learning; Mixed reality and learning.
Human-AI Partnership: Shared decision making between systems and users that promote agency and improve learning.
AI in Ed for Theory: Using bottom-up and top-down approaches to analyze data in order to inform learning theories and gain better understanding of the socio-cognitive nature of learning.
Diversity, Equity and Inclusion
The AIED Society values diversity, equity, and inclusion (and related principles under this broad umbrella) as essential and fundamental values for the AIED community to uphold. Thus, in AIED 2024, we encourage authors to:
Write with care toward inclusive language (e.g., understanding identify-first vs. person-first language, gender neutral language, appropriate demographic categories and terminology, and avoiding the conflation of distinct dimensions such as race and ethnicity, or sex and gender).
Report methodology including descriptions of sample characteristics (e.g., demographic data), any procedures for inclusive and representative sampling, any barriers to inclusive and representative sampling, and the ethical issues addressed both in the research methodology and the AIED approaches or tools being researched. For example, it is important to report the strategies used to control or reduce bias against populations of any kind (e.g. benefit or bring prejudice to a particular gender, race, or people with different economic status) when collecting, using, or aggregating data both for the research and for the AIED approach or tool being researched.
Consider how their theoretical frameworks and findings are related to diversity, equity, and inclusion. For example, authors may discuss how these issues influence key assumptions, hypotheses, and methods.
Address implications or appropriate interpretations of their findings with respect to diversity, inclusion and equity.
Submission Instructions
We invite submissions, full or short papers, to the main track:
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. Papers submitted as a full paper may be accepted as a short paper.
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.
Submissions must follow Springer policies on publication (including policies on the use of AI in the authoring process): https://tinyurl.com/3rk3zj3v.
Please note that presenters of papers accepted to the main conference are expected to be on-site to give their presentations and to interact with the audience. An online streaming option will be set-up for remote observers. Scholarships are available for researchers who lack funding to present at the conference.
Papers accepted to the conference must have a unique author registration (i.e., one registration per paper).
Authors should note that unlike previous AIED conferences, there will be NO downgrade path from main track submissions to posters. Authors whose main track submissions are not accepted either as full or short paper are encouraged to revise and consider the late-breaking results track or workshops.
Review Process
All submissions will be reviewed by three members of the program committee, followed by a metareview conducted by a senior member of the program committee (all double-masked) to meet rigorous academic standards of publication. 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. The review process is documented in detail at https://tinyurl.com/mprfusvp.
Anonymity
The review process will be double-masked, meaning that both the authors and reviewers will remain anonymous. To this end, authors should:
Eliminate all information that could lead to their identification (names, contact information, affiliations, patents, names of approaches, frameworks, projects and/or systems)
Cite own prior work (if needed) in the third person
Eliminate acknowledgments and references to funding sources
Data collection, reporting, and analysis
Authors should be clear and specific about the composition of human-sourced data. Who were the participants? What was the distribution of gender, race, ethnicity, or related variables? If corpus data or training data were sourced from humans, a similar description could be offered.
Skewed or non-representative samples would not necessarily trigger a "reject" decision, but authors should acknowledge the demographic imbalances and discuss the potential impact on data, results, or conclusions. A more compelling paper would describe steps taken to generate an inclusive and representative sample (this is basic science, but often overlooked for convenience).
Authors are encouraged to discuss/justify how demographic variables are included in the analyses. If they are not included or "covaried out" please justify. If they are included, what are the assumptions? Are there "categorical effects"? Are the effects of different demographic variables independent, interdependent, or intersectional? What valid conclusions can be drawn? What erroneous conclusions need to be avoided or tempered?
Ethics
Authors should demonstrate some awareness of how ethical issues (including but not limited to equity, inclusion, accessibility) impact their data, methods, tools, approaches, products, and findings. How are different demographic groups or communities differentially connected to the work? People who are developing educational technologies need to think about access and use, for example. Corpus analyses need to address the impact of skewed/exclusive datasets and potential outcomes (e.g., algorithmic bias).
Submission Procedure and Publication of Accepted Contributions
All 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. Submissions are handled via EasyChair.
Accepted AIED 2024 papers for the main track will be published by Springer Lecture Notes in Artificial Intelligence (LNAI), a subseries of Lectures Notes in Computer Science (LNCS). Paper lengths for the main track submissions are as follows:
Full papers (14 pages including references; for a long oral presentation)
Short papers (8 pages including references; for a short oral presentation)
Important Dates (full and short papers, main track)
Abstracts due: January 29, 2024
Papers due: February 5, 2024
Notification of acceptance to authors: March 13, 2024
Camera-ready paper due: April 29, 2024
All deadlines are anywhere on earth (AoE). Please adhere to these deadlines as there will NOT be any extensions to the above dates for full and short submissions to the main track of the conference.
Organising Committee
General Chairs
Olga C. Santos, UNED, Spain
Ig Bittencourt, Federal University of Alagoas, Brazil
Program Co-chairs
Andrew Olney, University of Memphis, USA
Irene-Angelica Chounta, University of Duisburg-Essen, Germany
Zitao Liu, Jinan University, China
Local Chairs
Rafael Ferreira Leite de Mello, CESAR and Federal Rural University of Pernambuco, Brazil
Taciana Pontual, Federal Rural University of Pernambuco, Brazil