General Call

The 2024 edition of The International Conference on Learning Analytics & Knowledge (LAK24) will take place in Kyoto, Japan. LAK24 is organized by the Society for Learning Analytics Research (SoLAR) with Kyoto University. LAK24 is a collaborative effort by learning analytics researchers and practitioners to share the most rigorous cutting edge work in learning analytics.

The theme for the 14th annual LAK conference is  Learning Analytics in the Age of Artificial Intelligence. Artificial intelligence has been relevant for learning analytics since the early days of the field. This has mostly been manifested by building upon the algorithms of machine learning to analyze data about learners and learning environments. The conversations about  artificial intelligence in education used to be mostly contained within specialized communities of practitioners and researchers. Since late 2022, this has rapidly changed. Discourse in mainstream media and among the general public has been dominated by the coverage of the developments in generative artificial intelligence. The notable examples are such technologies as ChatGPT and DALL-E that harness the power of deep learning algorithms to generate impressively human-like text and images based on relatively simple human prompts. These technologies have given some glimpses about the emerging age of artificial intelligence. The prominence of artificial intelligence has also opened profound debates about implications on education from the need to develop relevant literacies to work with artificial intelligence to challenging the established notions of assessment in education. Through the theme of the 14th annual LAK conference, we encourage the authors to consider implications for learning analytics and the role the field can play in the age of artificial intelligence. 

The LAK conference is intended for both researchers and practitioners. We invite both researchers and practitioners of learning analytics to come and join a proactive dialogue around the future of learning analytics and its practical adoption. We further extend our invite to educators, leaders, administrators, government and industry professionals interested in the field of learning analytics and its related disciplines.

Authors should note that:

Conference Theme and Topics

We welcome submissions from both research and practice, encompassing different theoretical, methodological, empirical and technical contributions to the learning analytics field. Learning analytics research draws on many distinct academic fields, including psychology, the learning sciences, education, neuroscience, computer science, artificial intelligence, human-computer interaction, and design. We encourage the submission of work conducted in any of these traditions, as long as it is done rigorously. We also welcome research that validates, replicates and examines the generalizability of previously published findings, as well as examines aspects of adoption of existing learning analytics methods and approaches.

Specifically, this year, we encourage contributors to consider learning analytics in the age of artificial intelligence. Artificial intelligence offers novel technologies that can be useful for addressing important problems in learning analytics from analysis of large amounts of data to assessment and to generation of personalized feedback. Artificial intelligence sets the new context in education that has triggered many to take relevant steps to adapt from the ways we generate content to the ways we design assessments and to the types of skills we need to develop. Adaptation to this new context requires fundamental research about implications on learning, teaching, and education, and learning analytics can play a considerable role. 

Thus for our 14th Annual conference, we encourage authors to address some of the following questions in their submissions:

  • How can novel artificial intelligence technologies be used to advance data collection, measurement, analysis and reporting in learning analytics? 
  • What is the role of learning analytics in the changing focus of assessment from product to process that is happening due to the growing adoption of artificial intelligence? 
  • To what extent can learning analytics explain, inform, and support learning and teaching processes that involve the use of generative artificial intelligence technologies?
  • What are the implications for ethics and trustworthiness in learning analytics in the age of artificial intelligence?  
  • What are the skills teachers and students need to have to work with learning analytics in the age of artificial intelligence? 
  • How should educational policies and strategies be adapted to support the use of learning analytics in the age of artificial intelligence? 
  • To what extent can communication of the results of learning analytics be enhanced with the use of artificial intelligence? 
  • How do we establish the right balance between human control and automation of learning analytics in the age of artificial intelligence? 
  • What approaches should we use to design learning analytics for the age of artificial intelligence?

Disclaimer: The authors are not required to address the theme of the conference and the consideration of the papers for acceptance will not be based on their fit to the conference theme. The purpose of every annual conference’s theme is to encourage discussions and debate  on emerging topics and issues related in the field. 

Topics of interest include, but are not limited to, the following:

Implementing Change in Learning & Teaching:

  • Ethical issues around learning analytics: Analysis of issues and approaches to the lawful and ethical capture and use of educational data traces; tackling unintended bias and value judgements in the selection of data and algorithms; perspectives and methods that empower stakeholders.
  • Learning analytics adoption: Discussions and evaluations of strategies to promote and embed learning analytics initiatives in educational institutions and learning organizations. Studies that examine processes of organizational change and practices of professional development that support impactful learning analytics use.
  • Learning analytics strategies for scalability: Discussions and evaluations of strategies to scale capture and analysis of information in useful and ethical ways at the program, institution or national level; critical reflections on organizational structures that promote analytics innovation and impact in an institution.
  • Equity, fairness and transparency in learning analytics: Consideration of how certain practices of data collection, analysis and subsequent action impact particular populations and affect human well-being, specifically groups that have been previously disadvantaged. Discussions of how learning analytics may impact (positively or negatively) social change and transformative social justice.

Understanding Learning & Teaching:

  • Data-informed learning theories: Proposals of new learning/teaching theories or revisions/reinterpretations of existing theories based on large-scale data analysis.
  • Insights into specific learning processes: Studies to understand particular aspects of a learning/teaching process through the use of data science techniques, including negative results.
  • Learning and teaching modeling: Creating mathematical, statistical or computational models of a learning/teaching process, including its actors and context.
  • Systematic reviews: Studies that provide a systematic and methodological synthesis of the available evidence in an area of learning analytics.

Tracing Learning & Teaching:

  • Finding evidence of learning: Studies that identify and explain useful data for analyzing, understanding and optimizing learning and teaching.
  • Assessing student learning: Studies that assess learning progress through the computational analysis of learner actions or artifacts.
  • Analytical and methodological approaches: Studies that introduce analytical techniques, methods, and tools for modeling student learning.
  • Technological infrastructures for data storage and sharing: Proposals of technical and methodological procedures to store, share and preserve learning and teaching traces, taking appropriate ethical considerations into account.

Impacting Learning & Teaching:

  • Human-centered design processes: Research that documents practices of giving an active voice to learners, teachers, and other educational stakeholders in the design process of learning analytics initiatives and enabling technologies.
  • Providing decision support and feedback: Studies that evaluate the use and impact of feedback or decision-support systems based on learning analytics (dashboards, early-alert systems, automated messages, etc.).
  • Data-informed decision-making: Studies that examine how teachers, students or other educational stakeholders come to, work with and make changes using learning analytics information.
  • Personalized and adaptive learning: Studies that evaluate the effectiveness and impact of adaptive technologies based on learning analytics.
  • Practical evaluations of learning analytics efforts:  Empirical evidence about the effectiveness of learning analytics implementations or educational initiatives guided by learning analytics.

Conference Tracks

The conference has three different tracks with distinct types of submissions that are described below. Please see the submission guidelines page for information on paper format and other technical details of submission for each track.

The focus of the research track is on advancing scholarly knowledge in the field of learning analytics through rigorous reports of learning analytics research studies. The primary audience includes academics, research scientists, doctoral students, postdoctoral researchers and other types of educational research staff working in different capacities on learning analytics research projects.

Submission types for the research track are similar to other years, starting for LAK21, LAK follows ACM’s one column format for submissions. Templates and formatting details will be included in the submission guidelines being released soon. Please note that published Proceedings will appear in ACM two column format.

  • Full research papers (up to 16 pages in ACM 1 column format, including references) include a clearly explained substantial conceptual, technical or empirical contribution to learning analytics. The scope of the paper must be placed appropriately with respect to the current state of the field, and the contribution should be clearly described. This includes the conceptual or theoretical aspects at the foundation of the contribution, an explanation of the technical setting (tools used, how are they integrated into the contribution), analysis, and results. See bulleted list of questions above for more detailed ideas on useful elements to include.
  • Short research papers (up to 10 pages in ACM 1 column format, including references) can address on-going work, which may include a briefly described theoretical underpinning, an initial proposal or rationale for a technical solution, and preliminary results, with consideration of stakeholder engagement issues. See bulleted list of questions above for more detailed ideas on useful elements to include.

DEADLINE: All LAK24 submission deadlines can be found here.

NOTE: If you are a newcomer to the LAK conference, it might be helpful to review the LAK23 ACM proceedings, openly available from the SoLAR website via ACM’s OpenTOC service. For Tips on writing LAK papers see here.

Should you have further questions regarding paper length or format, please contact us at

Review Process

LAK24 will use a double-blind peer review process for all submissions except demos and the doctoral consortium (which each require elements that prevent blinding). To continue to strengthen the review process for both authors and reviewers  LAK24 will have a rebuttal phase for full and short research papers in which authors will be given seven days to respond to remarks and comments raised by reviewers in a maximum of 500 words. Rebuttals are optional, and there is no requirement to respond. Authors should keep in mind that papers are being evaluated as submitted and thus, responses should not propose new results or restructuring of the presentation. Therefore, rebuttals should focus on answering specific questions raised by reviewers (if any) and providing clarifications and justifications to reviewers. Meta-reviewers, senior members of the research community, make final recommendations for paper acceptance or rejection with justification to the program committee chairs after the rebuttal phase is concluded. Acceptance decisions are ultimately taken by the program committee chairs based on all available information from the review process in combination with the constraints of the allowable space in the conference program.

Finally, please note that the conference timeline allows for rejected submissions to be re-submitted in revised form as poster, demo and workshop papers.

Proceedings Publication

Accepted full and short research papers will be included in the LAK24 conference proceedings published and archived by ACM. Other types of submissions (posters, demos, workshops, tutorials, practitioner reports and doctoral consortium) will be included in the open access LAK companion proceedings, published on SoLAR’s website. Please note at least one of the authors of each accepted submission must register for the conference by the Early Bird deadline in order for the paper to be included in the ACM or LAK Companion Proceedings.

Important Dates for LAK24

All LAK24 Deadlines are 11:59pm AOE.

Research Track

Full / Short Submission Deadline 2 Oct 2023
Rebuttal submission open 6 Nov 2023
Deadline for rebuttals 13 Nov 2023
Notification of Acceptance 1 Dec 2023
Deadline for camera ready 18 Dec 2023

Practitioner Reports

Practitioner Submission Deadline 2 Oct 2023
Notification of Acceptance 1 Dec 2023
Deadline for camera ready 18 Dec 2023

Poster / Demos

Poster/Demo Submission Deadline 16 Dec 2023
Notification of Acceptance 13 Jan 2024
Deadline for camera ready 29 Jan 2024

Doctoral Consortium

DC Submission Deadline 16 Oct 2023
Notification of Acceptance 24 Nov 2023
Deadline for camera ready 11 Dec 2023

Workshops / Tutorials

Workshop/Tutorial Submission Deadline 25 Sept 2023
Notification of Acceptance for workshop organization 13 Oct 2023
Submission Deadline for papers to individual workshops that issue calls** 16 Dec 2023
Notification of Acceptance for papers submitted to individual workshops 13 Jan 2024
Deadline for camera ready (Workshop organizer proposal docs) (individual workshop papers are not included in LAK proceedings) 29 Jan 2024

**Workshop Paper Submissions - this term refers to papers submitted to be presented within an accepted LAK pre-conference workshop. Many LAK workshops are mini-symposium style and issue calls for papers. Please visit the pre-conference schedule when available to view which workshops have CFP’s that you may submit to.

Conference and Registration Dates

Early-bird registration closes at 11:59pm PST 29 Jan 2024 5 Feb 2024
LAK24 conference, Kyoto Japan 18-22 March 2024

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