The AIED 2024 Conference, with its theme "AI in Education for a World in Transition," invites contributions to the Industry, Innovation, and Practitioner Track. We are offering a comprehensive platform for educators, industry professionals, policymakers, and other stakeholders to collaborate, share, and innovate in the field of AI in Education. This track aims to:

• Encourage involvement from educators, industry professionals, product developers, and policymakers.

• Promote cooperation among these diverse stakeholders.

• Focus on innovative practices, creative applications, and policy implications of AI in education.

• Share practical implementations and reflections on AI in education.

• Cross-pollinate knowledge between academic research, practice, policy and other stakeholders to facilitate effective AI-powered educational practices.

• Encourage discussion and develop a common understanding of AIED in real-world settings.

Abstracts due: March 4, 2024 (Optional)

Papers due: March 18, 2024 (extended)

Notification of acceptance to authors: April 11, 2024 

Camera-ready paper due: April 29, 2024

Conference: July 8 - 12, 2024

Note: the submission deadlines are at 11:59 pm AoE (Anywhere on Earth) time.

System. Please note that the submissions must be written in English. Papers should be submitted electronically, as a PDF file, through the AIED 2024 EasyChair conference system (https://easychair.org/my/conference?conf=aied2024), selecting the "Industry, Innovation and Practitioner" track. 

Types. We encourage two types of submissions (reviewers will comment on whether the size is appropriate for each contribution): 

- Full papers (between 8 and 12 pages including references; for a long oral presentation). 

- Short papers (between 6 and 8 pages including references; for a short oral presentation). 

Format. 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 must follow Springer policies on publication (including policies on using AI during authoring). 

Expectations. Contributions should focus on the practical application of AI technologies in education with due consideration for prior evidence-based research. We encourage diverse formats, including:

• Descriptions of AIEd implementations.

• Reflections on challenges and opportunities in AI-supported education.

• Theoretical contributions and personal experiences in the field of AIEd.

We welcome contributions from:

• Teachers, educators, and learners across all educational levels and contexts.

• Researchers, instructional technologists, learning designers, and educational staff.

• Developers, designers, and representatives from commercial and industry entities.

• Policymakers and leaders from non-profit and government bodies.

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?

Process. All submissions will be reviewed by three members of the program committee or other ad-hoc reviewers, followed by a second round of review conducted by a senior member of the program committee. Papers will be reviewed for relevance to the track, quality of reflection, originality and innovation, significance and potential for influence, multidisciplinarity and societal impact considerations, clarity and coherence 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. 

Anonymity. The process will be double-blind, i.e., both authors and reviewers will remain anonymous, to meet rigorous academic standards of publication. Hence, authors should eliminate all information that could lead to their identification, cite their own prior work (if needed) in third person, and remove acknowledgments and references to funding sources. 

Ethics. Authors should demonstrate awareness of how ethical issues (including but not limited to equity, inclusion, accessibility) impact the content of their paper, also including if available data, methods, tools, approaches, products, and findings.

Each accepted paper within the track must be accompanied by a unique author registration (i.e., one registration per paper), completed by the early registration date cut-off. Please note that presenters of papers accepted to the 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. 

Accepted papers in the track will be published in the second volume of the AIED 2024 proceedings included in Springer Communications in Computer and Information Science (CCIS). Scholarships are available for researchers who lack funding to present at the conference, see website for more information.

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

• Richard Tong, Carnegie Learning, USA (richard.tong@ieee.org)

• Diego Dermeval Medeiros da Cunha Matos, Center for Excellence in Social Technologies, Federal University of Alagoas, Brazil  (diego.matos@famed.ufal.br)

• Sreecharan Sankaranarayanan, Amazon, USA (sree@cmu.edu)

• Insa Reichow, German Research Center for Artificial Intelligence, Germany (insa.reichow@dfki.de)