We would have the option to send the poster to print it in Recife. If this is the case for you, please follow the instructions below:

1. Prepare your banner according to the guidelines below.

2. Sent the final PDF to Contato@josimendes.com.br

3. The deadline for sending the PDF is June 30th.

4. Pay R$150,00 (Brazilian Reais) to our organization team during the conference.

The poster size should be A0 and the orientation Portrait. 

You will find here some useful tips on how to create a good poster: https://phdposters.com/howto#howto_ppt 

We look forward to seeing your poster at AIED2024!

We would offer an option for printing your poster in Recife, information about how to do this will be circulated as soon as possible. 

We are pleased to invite you to contribute to the program of AIED2024 by submitting your late breaking results (LBR). The LBR 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 25th international conference on Artificial Intelligence in Education (AIED) will take place between 8-12 July, 2024 in Recife, Brazil. Its theme is: AI in Education for a World in Transition. 

The conference will be the latest 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 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; 

• Modeling and Representation; Models of Teaching and Learning; 

• Learning Contexts and Informal Learning; 

• Evaluation; 

• Innovative Applications; Equity and Inclusion in Education; 

• Ethics and AI in Education; AI Literacy; AIED for Development; 

• Explore Design, Use, and Evaluation of Human-AI Hybrid Systems for Learning; 

• Online Learning Spaces; 

• Human-AI Partnership; 

• AI in Ed for Theory.

Please see the main call for details about each of these topics.

Submission due: March 25, 2024

Notification of acceptance to authors: April 29, 2024 

Camera-ready paper due: May 6, 2024

Conference: July 8 - 12, 2024

Note: the submission deadlines are at 11:59 pm AoE (Anywhere on Earth) time. Please adhere to these deadlines as there will NOT be any extensions to the above dates for LBRs track submissions.

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 incentivize authors to carefully consider diversity, equity, and inclusion when reporting on your work. 

When preparing your paper, please consider the following: 

• Authors should write with care toward inclusive language. This includes 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. 

• Authors are encouraged to 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. Likewise, authors might address implications or appropriate interpretations of their findings with respect to diversity, inclusion and equity. 

Please consider the following criteria when reporting samples: 

• 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 barriers to inclusive and representative sampling and the steps taken to generate an inclusive and representative sample (this is basic science, but often overlooked for convenience). 

• Authors should demonstrate some awareness of how equity, inclusion, accessibility issues impact their data, methods, products, or 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). It is also important to use strategies 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. 

• 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?

All submissions must be in Springer format and follow Springer policies on publication (including policies on the use of AI in the authoring process):  https://tinyurl.com/3rk3zj3v. 

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. 

Paper length must be between 6 (minimum) and 8 pages including references. 

Submissions will be handled via EasyChair: https://easychair.org/conferences/?conf=aied2024

All submissions will be reviewed by the program committee to meet rigorous academic standards of publication. 

The review process will be double-masked, 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 own 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.

Each accepted paper within the LBR track must be accompanied by a unique author registration (i.e., one registration per paper).  Participants who complete an author registration for their short or full paper and have been accepted in the Interactive Events track submission, do not need a separate registration for the event. Please note that presenters of papers accepted to the LBR track are expected to be on-site to give their presentations and interact with the audience, to have the paper included in the proceedings. An online streaming option will be set-up for remote observers. 

Note that a Best Interactive Events Award will take place. There is also an opportunity to be featured in the IAIED website showcase page, which will be redesigned to highlight recent work.

If you have any further questions, please, contact the LBR Chairs: 

• Marie-Luce Bourguet, Queen Mary University of London, UK (marie-luce.bourguet@qmul.ac.uk)

• Qianru Liang, Jinan University, China (liangqr@jnu.edu.cn)

• Jingyun Wang, Durham university, UK (jingyun.wang@durham.ac.uk)