CONTRIBUTE TO L@S 2027
Call for Papers
Original research, emerging ideas, and hands-on demonstrations that advance learning at scale.
On this page: Deadlines · Contributions · Topics · Submission guidelines · Review process · Publication
Important dates
All deadlines are final. No extensions. Times are 11:59 PM Anywhere on Earth (AoE).
Research Papers & WiP
| Milestone | Deadline |
|---|---|
| Abstracts (Research Papers & WiP) | January 22, 2027 |
| Papers (Research Papers & WiP) | January 29, 2027 |
| Author notification | March 31, 2027 |
| Camera-ready version | May 5, 2027 |
Demonstrations
| Milestone | Deadline |
|---|---|
| Demonstration paper | April 14, 2027 |
| Author notification | April 28, 2027 |
| Camera-ready version | May 8, 2027 |
Abstracts are required for both Research Papers and Work-in-Progress, with a maximum of 350 words. Demonstrations do not require a separate abstract submission.
Three ways to contribute
Research Papers
UP TO 10 PAGES
Empirical or theoretical work that advances learning at scale. Keep the length proportional to the contribution.
Work-in-Progress
UP TO 4 PAGES
Recent findings and innovative work still in development. Accepted WiP submissions are presented as posters.
Demonstrations
UP TO 2 PAGES
Interactive, hands-on presentations of technologies and environments relevant to learning at scale.
Page limits exclude references. Appendices and supporting materials must be hosted in an online repository and referenced in the paper, using links that preserve anonymous review.
Demonstration proposals
Describe both the merit and nature of the technology and how visitors will interact with the live demonstration. If associated with a Research Paper or WiP submission, cite that work in the two-page proposal. Authors of accepted papers are encouraged to demonstrate their technologies.
Workshops and tutorials
Workshops and tutorials are coordinated by Festival of Learning 2027. Please consult the Festival website for calls and details.
Research scope & topics
L@S investigates large-scale, technology-mediated learning environments that typically have many active learners and few experts on hand to guide their progress or respond to individual needs. Modern learning at scale typically draws on data at scale, collected from current learners and previous cohorts of learners over time. Large-scale learning environments are very diverse. Formal institutional education in K-16 and campus-based courses in popular fields involve many learners, relative to the number of teaching staff, and leverage varying forms of data collection and automated support. Evolving forms of online courses, mobile learning applications, intelligent tutoring systems, open courseware, learning games, citizen science communities, collaborative programming communities, community tutorial systems, shared critique communities, informal communities of learners, and curricular and administrative systems that support learner pathfinding are all examples of learning at scale. All share a common purpose to increase human potential, leveraging data collection, data analysis, human interaction, and varying forms of computational assessment, adaptation and guidance.
Research on learning at scale naturally brings together several research communities. Learning scientists are drawn to study established and emerging forms of knowledge development, transfer, modelling, and co-creation. Computer and data scientists are drawn to the specific and challenging needs for data collection, data sharing, analysis, computation, modeling, and interaction. The cornerstone of L@S is interdisciplinary research and progressive confluence toward more effective and varied future learning.
We invite methodologies, case studies, qualitative and quantitative analyses, tools, and technologies. Topics include, but are not limited to:
- Instruction at scale: studies that examine how teachers and educators scale their instructions, what aspects of instruction could be scaled effectively, and which of these instructional strategies are the most effective for learning.
- Interventions at scale: studies that examine the effects of interventions on student learning and performance when implemented at scale. We welcome studies that use both qualitative and quantitative methods.
- The use of generative AI to scale learning: studies that investigate stakeholders’ experiences with generative AI, students’ and teachers’ interactions with generative AI, the potentials and limitations of using generative AI in education.
- Systems and tools to support learning at scale: research that designs and develops systems supporting the learning process For example, this could involve the design or augmentation of MOOCs, visualization techniques, intelligent tutoring systems, gamification, immersive techniques (AR/VR/MR), mobile technologies, tangible interfaces, or administrative tools that affect student access and pathfinding.
- The evaluation of existing learning at scale systems and online learning environments using but not limited to the above mentioned technologies.
- Methods and algorithms that model learner behavior: research that contributes methods, algorithms, pipelines that process large student data to enhance learning at scale.
- Scaling learning in informal contexts: studies that explore how people take advantage of online environments to pursue their interests informally.
- Review and synthesis of existing literature related to learning at scale.
- Empirical studies and interventions that address equity, trust, algorithmic transparency and explainability, fairness and bias when using AI in education.
- Research that addresses accessibility in learning at scale contexts.
- Design and deployment of learning at scale systems for learners from underrepresented groups.
Submission guidelines
- Submit through EasyChair.
- Submit an English-language PDF using the ACM two-column proceedings template (Word or LaTeX). ACM proceedings template guidance.
- Submissions must contain original work and must not be under review elsewhere while under consideration for L@S.
- Submit a maximum 350-word abstract by January 22 for both Research Papers and WiP, then the paper by January 29.
- Use anonymous repository links for appendices, materials, data, and scripts.
- Accepted works must be presented at the conference and will be included in the proceedings.
Double-blind review & anonymization
Submissions will be reviewed on the basis of originality, research quality, potential impact, and value to the development of future learning at scale. In order to increase high quality papers and independent merit, the evaluation process will be double-blind. All submissions, with the exception of workshop proposals (see below), should be anonymous. Thus, papers submitted for review MUST NOT contain the authors’ names, affiliations, or any information that may disclose the authors’ identity (this includes names or logos of labs/centers, tools, grant numbers, and acknowledgments). Identifying information is to be restored in the camera-ready version upon acceptance. Please replace author names and affiliations with Xs on submitted papers. In particular, in the version submitted for review please avoid explicit self-references, such as “in [1] we show” — consider instead “in [1] it is shown” or “in [1] Smith et al. show …” (citing yourself in the third person just like how you would cite other researchers). You should definitely cite your own relevant previous work, so that a reviewer can access it and see the new contributions. However, the text should not explicitly state that the cited work belongs to the authors (i.e., do not use “Author (year)” to anonymize). Authors are permitted to post their work to public archives (e.g., arXiv, EdArXiv). Reviewers will be asked not to check archives during the review process.
Open science
Authors are encouraged to conduct their scientific inquiry using emerging best practices in open science. Authors are encouraged to pre-register their study design, hypotheses, and analysis plans, and publish these using platforms such as OSF.io (https://osf.io/) or AsPredicted.org (https://aspredicted.org/). Whenever possible, feasible, and ethical, authors are encouraged to make their data, materials, and scripts openly available for inspection, replication, and follow-up analysis. The best way to share these materials is to use an established platform like OSF.io. When you submit your paper, there will be three questions in the submission form where you can say if any part of our paper is pre-registered, gives out data, or gives out your materials.
Pre-review & desk rejection
This year, the L@S program committee is introducing a pre-review process to screen submissions prior to formal review. This process will verify proper anonymization, check for citation hallucinations (fabricated references), ensure structural requirements such as page limits are met, and filter out submissions that are clearly out of scope (e.g., non-education topics). Papers failing to meet these minimum requirements will be desk-rejected. This process will use secure local AI systems that will not share the content of the papers outside the scope of the conference. While AI and machine learning will be used to assist with these checks, all final decisions are made by the PC chairs.
Publication information
The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks prior to the first day of your conference. The official publication date affects the deadline for any patent filings related to published work. (For those rare conferences whose proceedings are published in the ACM Digital Library after the conference is over, the official publication date remains the first day of the conference).
Questions
Program Chairs: Tanja Käser and Kirk Vanacore. Submission inquiries: ls2027@easychair.org.