Submissions

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Submission Preparation Checklist

As part of the submission process, authors are required to check off their submission's compliance with all of the following items, and submissions may be returned to authors that do not adhere to these guidelines.
  • If your paper is not part of a special issue, and is a stand-alone article, please select 'Articles' when submitting your paper when prompted to choose a 'Section'. If your paper is part of a Special Issue, then select the appropriate 'Special Issue' when prompted under Section.
  • The submission has not been previously published, nor is it before another journal for consideration (or an explanation has been provided in Comments to the Editor).
  • The submission file has applied the article template and is in Microsoft Word file format. Template available here
  • Where available, DOIs/URLs for the references have been provided.
  • The text is single-spaced; uses a 12-point font; employs italics, rather than underlining (except with URL addresses); and all illustrations, figures, and tables are placed within the text at the appropriate points, rather than at the end.
  • The text adheres to the stylistic and bibliographic requirements outlined in the Author Guidelines.
  • The submission is blinded.

Author Guidelines

Publishing format

Articles are limited to 2,500 words, aiming to present succinct research. The word count is strictly enforced.  Supplementary material can be supplied in electronic format, including publication of data, scripts, and additional methodological procedures. Larger datasets must be deposited in recognised public domain databases by the author. The 2,500 word limit is for the manuscript body, excluding the abstract and references. ALL other text is included in the word count. 

The journal applies current APA referencing style. Please use British/Australian spelling conventions.

Please prepare your manuscript using the Learning Letters Template and Author Guidelines. The template provides further formatting details.

Review process

Learning Letters uses a double-blind peer review process, meaning that the author(s) and the reviewers are anonymous to each other. Please ensure that any identifying details (such as the manuscript file name, author names, self-citations and references, and project web-links) are anonymised in the submitted manuscript.

Where a manuscript is accepted for peer review, the Editors will assign a minimum of two reviewers. Articles may be accepted, returned for revision (major or minor) or rejected.

A manuscript may be rejected if it is outside the journal scope, does not meet the required standard of quality or ethics, or if two reviewers do not recommend the manuscript for publication.

If a manuscript is resubmitted after minor revision, it is unlikely to undergo further peer review. If a manuscript is resubmitted after major revision, it will be returned to the previous or new reviewers (depending on reviewer availability).

The Editor’s decision is final.

The use of AI during the writing process: Policy

Learning Letters acknowledges that generative AI and large language models (LLMs) can assist in the writing of academic manuscripts. Nevertheless, authors who use such technologies must do so with human oversight.

Authors are responsible for reviewing any AI-generated text to ensure that it is accurate, appropriate, and free from bias. Any citations generated through AI-assisted technologies must be valid. Do not list or cite an AI-assisted tool or service as an author. Authors who use generative AI or AI-assisted technologies during the writing process must disclose this via the statement below or similar:

     Disclosure of the use of AI-assisted technologies in the writing process

The author(s) used [tool / service] for the purpose of [specify purpose] throughout the article / in sections [state sections]. The author(s) take(s) full responsibility for the content.

No disclosure is required for the use of word processing or referencing tools during the writing process, or for the use of AI-assisted technologies during the research process.

This policy will be reviewed regularly and adapted as required.

Special Issue on Navigating Complexity in STEM Education: Adaptive and Experiential Learning

Learning Letters is excited to announce a call for papers for a special issue on "Navigating Complexity in STEM Education: Adaptive and Experiential Learning." This special issue will explore how principles of complexity science—such as self-organisation, emergence, adaptive learning, and feedback loops—can be leveraged to make STEM education more responsive to students’ evolving needs and the dynamic demands of professional practice.

Aim of the Special Issue

As STEM education continues evolving to meet real-world complexities, traditional structured approaches often struggle to accommodate the adaptive and emergent nature of learning environments. Despite advancements in experiential and problem-based learning, significant gaps remain in research on the effectiveness of flexible, adaptive teaching strategies, alternative assessment models for emergent learning outcomes, and the integration of AI-driven adaptive technologies to support dynamic, personalised learning. This special issue seeks to address these gaps by showcasing empirical, conceptual, and methodological contributions that explore how complexity science principles can inform STEM education, enhance assessment practices, and leverage technological innovations to create more responsive and student-centred learning environments

Suggested Topics of Interest

Authors are invited to submit papers on the following topics, although other relevant topics will also be considered:

1. Complex Adaptive Systems in STEM Education

  • Understanding emergent learning pathways in project-based and experiential STEM education.
  • Case studies of self-organising student teams in STEM projects.
  • The role of peer interactions, collaboration, and feedback loops in emergent learning.
  • Scaffolding uncertainty and failure: How students learn from unpredictability.
  • Measuring complexity: New methods for evaluating learning in self-organising environments.

2. Educational Technology, AI, and Data-Driven Approaches to Adaptive STEM Learning

  • AI-driven adaptive learning platforms supporting emergent knowledge acquisition.
  • Alternative assessment strategies for dynamic and emergent learning.
  • Personalised learning pathways through data-driven educational technologies.
  • Evaluating student engagement and performance in adaptive learning environments.
  • The impact of AI on student agency and self-directed learning in STEM.

Educational Contexts

We welcome submissions from diverse educational contexts, including:

  • Higher and further education
  • STEM-focused vocational education and training
  • Online and hybrid learning environments
  • Professional development in STEM
  • Educational technology implementation in STEM disciplines

Submission Guidelines

  • Articles should be concise, with a maximum of 2,500 words, focusing on results and outputs rather than extensive literature reviews and background discussions. (The 2,500 word limit is for the manuscript body. The abstract and references are not included in the word count. ALL other text is included. Please keep figures and tables to a minimum). The word count is strictly enforced. We accept:
    • Empirical studies (qualitative, quantitative, and mixed methods)
    • Theoretical and conceptual papers
    • Case studies demonstrating complexity-informed STEM teaching, and
    • learning strategies

Deadline for Submissions

  • 18 May 2025 – Title, keywords, and 200-250-word abstract must be submitted directly by email to the guest editors. Early submissions are strongly encouraged.
    • 4 July 2025 – Drafts of full papers due. Please submit your full paper through the Learning Letters submission system.
    • November 2025 – Publication of the special issue.

Where and How to Submit

For any inquiries, please contact the guest editors.

We look forward to your contributions to this timely and important special issue!

Special Issue on Self-Regulated Learning in the Age of Generative AI: Finding the Balance

Special Issue on Self-Regulated Learning in the Age of Generative AI: Finding the Balance

With the continuous advancement and growing accessibility of generative artificial intelligence (GenAI) technologies, their integration into every sphere of our lives is becoming increasingly pervasive. Within education, in particular, GenAI offers the potential to support learning processes by providing personalised guidance or even extend cognitive capabilities. However, it also brings forward concerns about cognitive offloading and the potential undermining of self-regulated learning processes (SRL), which remain critical for student success. As mixed views and findings regarding the impacts of genAI on SRL continue to emerge, the need for research evidence is critical to understanding how GenAI influences students’ SRL processes.

The accelerating pace and far-reaching impact of GenAI on SRL create a timely imperative for rigorous exploration and critique of GenAI's roles in relation to how students develop and exercise SRL capabilities. In response to this priority, Learning Letters announces a call for papers for our special issue, Self-Regulated Learning in the Age of Generative AI: Finding the Balance.

Aim of the Special Issue

We encourage contributions investigating the complex relationship between GenAI and SRL, offering valuable guidance for institutions and educators seeking to harness the potential of these technologies while preserving and enhancing students' SRL and learning processes.  Drawing on Lodge's (2025) metaphor of GenAI as "the e-bike of the mind", this special issue calls for papers that explore the interplay between GenAI and SRL, how to balance technological assistance with the necessary effort required by students for meaningful learning and the development of SRL capabilities. We invite researchers to submit their work to this special issue, contributing to the ongoing discourse and shaping educational approaches in an AI-enhanced learning landscape.

Suggested Topics of Interest

Authors may use the following suggested topic areas as guidance, but we welcome author-initiated topics that are relevant to the aim of the special issue.

  • Different models of human-AI interaction (cognitive offloading, extended mind, co-regulation, hybrid learning) and their implications for SRL development.
  • Threats to SRL posed by over-reliance on generative AI.
  • Opportunities presented by GenAI for enhancing SRL processes.
  • Pedagogical approaches for guiding students toward productive relationships with AI that foster rather than diminish SRL capabilities.
  • Assessment designs that encourage appropriate AI use while still developing essential SRL skills.
  • Emerging tensions between technological efficiency and the cognitive effort necessary for deep learning.
  • Ethical concerns surrounding students' use of GenAI and implications for developing SRL capabilities.
  • The challenges of balancing AI assistance with SRL development: What are the risks, and how can they be mitigated?

Educational Contexts

We welcome submissions set in diverse educational backgrounds, including:

  • K-12 education
  • Higher and further education
  • Faculty professional development
  • Vocational education and training
  • Teacher education and development
  • Educational policy, spanning institutional to national spectrums
  • Development and implementation of educational technology

Submission Guidelines

Types of articles accepted

  • Original and developing qualitative, quantitative and mixed-methods research
  • Rapid reviews
  • Theoretical/conceptual pieces

Format, length, style

Articles are limited to 2,500 words in order to present succinct research. Supplementary material can be lodged in electronic format, including publication of data, scripts, and additional methodological procedures. Larger datasets must be deposited in recognised public domain databases by the author.

The 2,500 word limit is for the manuscript body. The abstract and references are not included in the word count. ALL other text is included. The word count is strictly enforced. Please keep figures and tables to a minimum.

Full submission guidelines may be found here: https://learningletters.org/index.php/learn/about/submissions

Key dates & deadlines

  • 13 June 2025: Submission of title, keywords and 200-250-word abstract.
  • 24 June 2025: Notifications of abstract acceptance by this date.
  • 28 Aug 2025: Final versions of revised paper due.
  • Dec 2025: Publication of special issue.

Where and how to submit

For questions regarding the special issue, please contact the guest editors.

Copyright Notice

Published articles are licensed under CC BY-ND 4.0.

To view a copy of this license, visit http://creativecommons.org/licenses/by-nd/4.0/

Privacy Statement

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