CALL FOR PAPERS FOR RIED – Vol. 29(1)
In the contemporary landscape of educational research, artificial intelligence (AI) stands at the forefront of transformative innovation. AI-driven systems are redefining the ways in which students engage with educational content, offering unprecedented opportunities for personalized and adaptive learning experiences. This issue of RIED invites scholarly contributions that rigorously examine the intersection of AI and education, with a particular emphasis on how AI can tailor learning environments to address the diverse needs of students.
The RIED Editorial Board, overseeing a well-established Q1 journal indexed in Scopus-CiteScore and Web of Science-JCR, welcomes high-quality original research that pushes the boundaries of current knowledge within the scope of the journal's objectives. We seek contributions that provide critical insights, robust empirical data, and well-founded, innovative theoretical frameworks. Submissions should delve into the role of AI in optimizing learning outcomes through adaptive technologies, personalized learning environments, data-driven feedback systems, intelligent tutoring systems, and other related topics.
Articles must present original research applying artificial intelligence (AI) across the various contexts outlined below. Priority will be given to studies that clearly demonstrate learning outcomes and evidence, based on rigorous research that meets RIED's comprehensive standards.
TOPICS OF INTEREST for authors in this Call for Papers:
- AI-Driven Adaptive Learning Systems: Application of models and algorithms to personalize educational content.
- Impact of AI on Educational Equity: Empirical evidence on how adaptive learning can narrow (or widen) gaps in access and performance.
- Adaptive Learning in Hybrid and Distance Environments: Research on how AI can optimize online teaching and learning experiences.
- AI in Inclusive Education: Outcomes demonstrating how AI addresses the needs of students with special educational requirements.
- Personalized Learning vs. Collaborative Learning. Strategies for integrating individualized and collective approaches with the support of AI.
- AI and Student Motivation: Studies contrasting the use of adaptive systems to enhance student motivation and engagement.
- Intelligent Tutoring Systems: Design, development, and evaluation of systems providing real-time personalized guidance and feedback, including:
- Using AI to identify student needs.
- Adapting content based on progress and performance.
- Delivering closer and more effective support compared to traditional methods.
- AI-Based Adaptive Assessment: Tools and approaches for formative and summative assessment, exploring:
- Implementation of AI models and algorithms for evaluation.
- Continuous, personalized formative assessments with AI.
- AI-driven measurement of complex competencies and skills.
- Predictive techniques for academic performance using AI.
- Evidence of assessment time optimization through AI.
- Immediate, tailored feedback delivered by AI.
- The impact of AI-driven assessment on student motivation and engagement.
- Ethical and Privacy Concerns: Implications of collecting and analyzing large volumes of personal data for assessment personalization, addressing issues such as privacy and security challenges, and ethical considerations in AI applications.
- Future Trends in AI and Adaptive Learning: Emerging technologies, novel research, and perspectives on the future of AI in education.
On the other hand, excellent articles that delve deeply into topics related to RIED's core objectives could always have a place in the journal.
We encourage submissions from scholars and researchers at the forefront of these topics. Authors should avoid submitting manuscripts that address or conclude with the obvious or reiterate what has already been published. At RIED, we have significantly limited the publication of systematic reviews. Exceptions may be made for reviews that go beyond mere description, offering a truly critical analysis of the selected material and reaching conclusions that contribute genuinely novel and substantive insights.
Each issue of RIED (published twice annually) includes between 15 and 17 articles. Due to the increasing attention the journal receives, we receive a high volume of submissions for each call, necessitating a rigorous prioritization process to select those that best meet the publication's criteria and the specific requirements of this call.
SUBMISSION GUIDELINES
Prior to submitting an article, authors must carefully review this document and all linked materials. Failure to comply with any of the stated criteria, requirements, or guidelines may result in the rejection of the manuscript. Many articles are rejected after an initial review of the abstract alone.
Authors should also pay close attention to the Authors' Responsibilities guidelines.
By adhering to these standards, authors can ensure that their submissions are considered seriously and align with the high expectations of RIED.
KEY DATES:
- Article submission: during May 2025 (deadline 01/06/2025). Avoid sending articles before May 2025.
- Official publication: This issue (Vol. 29(1)) corresponds to 01/01/2026.
- OnlineFirst publication: Before the official publication date, articles will be published in OnlineFirst format (ready to read and cite) as they pass the different evaluation phases.
IMPORTANT:
- Please, avoid sending articles before May 2025.
- Do not submit any work to RIED unless you are convinced that all the parameters required in this document and the referenced links are met.
- All submissions will be directed to the "Studies and Research" section.
- All articles not considered for publication in Vol. 29(1) will be rejected.