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Brain Sciences
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Advances in Educational Neuroscience: Current Status and Future Directions
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Advances in Educational Neuroscience: Current Status and Future Directions

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Developmental Neuroscience".

Deadline for manuscript submissions: 17 October 2025 | Viewed by 18149

Special Issue Editors

Dr. Antonio Luque De La Rosa

E-Mail Website
Guest Editor
Departamento de Educación, Universidad de Almería, Carretera Sacramento s/n, 04120 Almeria, Spain
Interests: attention to diversity; social and educational inclusion; special educational needs
Special Issues, Collections and Topics in MDPI journals
Dr. Alejandro Vargas Serrano

E-Mail Website
Guest Editor
Departamento de Educación, Universidad de Almería, Carretera Sacramento s/n, 04120 Almeria, Spain
Interests: social inclusion; educational practices; music education; tutorial action; humanities and social sciences; teacher training
Prof. Dr. Celia Gallardo Herrerías

E-Mail Website
Guest Editor
Departamento de Educación, Universidad de Almería, Carretera Sacramento s/n 04120, Almeria, Spain
Interests: clinical characteristics of individuals with Autism Spectrum Disorder (ASD); social and educational performance; immediate socio-familial environment

Special Issue Information

Dear Colleagues,

This Special Issue titled “Advances in Educational Neuroscience: Current Status and Future Directions” focuses on the most recent developments in the field of educational neuroscience. Over the past years, neuroscience has provided new insights into how the brain learns and develops, significantly impacting educational practices. The aim of this Special Issue is to explore how these advances can be applied to enhance education, particularly in the context of student diversity.

We aim to present cutting-edge research that offers new perspectives on integrating neuroscientific discoveries into effective pedagogical strategies. We invite submissions that address both practical applications and innovative theories, providing both theoretical and empirical approaches on how neuroscience can enrich educational practice and foster an inclusive environment tailored to the needs of all students.

Dr. Antonio Luque De La Rosa
Dr. Alejandro Vargas Serrano
Prof. Dr. Celia Gallardo Herrerías
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • educational neuroscience
  • cognitive development
  • pedagogical strategies
  • student diversity
  • inclusive learning
  • neuroscientific applications
  • educational research
  • neuroscience-based interventions
  • innovative educational practices
  • theoretical and empirical approaches

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Published Papers (6 papers)

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Research

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18 pages, 293 KiB  
Article
The Impact of COVID-19 in Brazil Through an Educational Neuroscience Lens: A Preliminary Study
by Camila G. Fonseca, Camila L. L. Dias, Marcus L. L. Barbosa, Maria Julia Hermida, Luiz Renato R. Carreiro and Alessandra G. Seabra
Brain Sci. 2025, 15(6), 548; https://doi.org/10.3390/brainsci15060548 - 23 May 2025
Viewed by 631
Abstract
Background: Educational neuroscience has made important contributions to show how the COVID-19 pandemic impacted schooling. In countries like Brazil, with significant educational inequality, the suspension of in-person classes worsened these disparities, as low-income families faced difficulties accessing remote learning. Methods: This study evaluated [...] Read more.
Background: Educational neuroscience has made important contributions to show how the COVID-19 pandemic impacted schooling. In countries like Brazil, with significant educational inequality, the suspension of in-person classes worsened these disparities, as low-income families faced difficulties accessing remote learning. Methods: This study evaluated executive functions (EF) and academic skills in reading, writing, and maths for 178 public school students from the first to ninth grades in São Paulo, Brazil, comparing them with pre-pandemic norms to assess possible differences. EF were assessed using the Hayling Test, Digit Span Task, and Verbal Fluency, while academic skills were measured by the School Performance Test II. To analyse differences between the sample of this study and the pre-pandemic normative samples, one-sample t-tests were performed. Due to the small sample size, segmented by school grade and age, the bootstrapping resampling method was used, and the effect size was measured with Cohen’s d. Results: A one-sample t-test showed significant differences between times, with lower post-pandemic performance in verbal fluency (9–14 years old), working memory (10–14 years old), and inhibitory control across all age groups. Writing skills were lower from the fifth to eighth grades and reading from the fourth to eight grades. Maths skills were lower in the fourth, eighth, and ninth grades. Better post-pandemic performance was seen in working memory (6 and 7 years old). Conclusions: Students in the upper grades of elementary school during the pandemic were most impacted by the suspension of in-person teaching, highlighting the importance of schooling and the need for recovery efforts at these levels. Full article
(This article belongs to the Special Issue Advances in Educational Neuroscience: Current Status and Future Directions)
14 pages, 2079 KiB  
Article
The Problem with Time: Application of Partial Least Squares Analysis on Time-Frequency Plots to Account for Varying Time Intervals with Applied EEG Data
by Jessie M. H. Szostakiwskyj, Filomeno Cortese, Raneen Abdul-Rhaman, Sarah J. Anderson, Amy L. Warren, Rebecca Archer, Emma Read and Kent G. Hecker
Brain Sci. 2025, 15(2), 135; https://doi.org/10.3390/brainsci15020135 - 30 Jan 2025
Cited by 1 | Viewed by 862
Abstract
Background/Objectives: When attempting to study neurocognitive mechanisms with electroencephalography (EEG) in applied ecologically valid settings, responses to stimuli may differ in time, which presents challenges to traditional EEG averaging methods. In this proof-of-concept paper, we present a method to normalize time over unequal [...] Read more.
Background/Objectives: When attempting to study neurocognitive mechanisms with electroencephalography (EEG) in applied ecologically valid settings, responses to stimuli may differ in time, which presents challenges to traditional EEG averaging methods. In this proof-of-concept paper, we present a method to normalize time over unequal trial lengths while preserving frequency content. Methods: Epochs are converted to time-frequency space where they are resampled to contain an equal number of timepoints representing the proportion of trial complete rather than true time. To validate this method, we used EEG data recorded from 8 novices and 4 experts in veterinary medicine while completing decision-making tasks using two question types: multiple-choice and script concordance questions used in veterinary school exams. Results: The resulting resampled time-frequency data were analyzed with partial least squares (PLS), a multivariate technique that extracts patterns of data that support a contrast between conditions and groups while controlling for Type I error. We found a significant latent variable representing a difference between question types for experts only. Conclusions: Despite within and between subject differences in timing, we found consistent differences between question types in experts in gamma and beta bands that are consistent with changes resulting from increased information load and decision-making. This novel analysis method may be a viable path forward to preserve ecological validity in EEG studies. Full article
(This article belongs to the Special Issue Advances in Educational Neuroscience: Current Status and Future Directions)
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14 pages, 862 KiB  
Article
Neuroscience Literacy and Academic Outcomes: Insights from a University Student Population
by Abeer F. Almarzouki, Arzan I. Alqahtani, Jumana K. Baessa, Dhuha K. Badaood, Rwdyn R. Nujoom, Raneem W. Malibari, Elaf M. Aljared and Reema S. Alghamdi
Brain Sci. 2025, 15(1), 44; https://doi.org/10.3390/brainsci15010044 - 4 Jan 2025
Viewed by 1408
Abstract
Background/Objectives: There is growing interest in neuroscience-informed education, as well as neuroscience-derived strategies that maximise learning. Studies on neuroscience literacy and neuromyths, i.e., understandings or misconceptions about the brain, have primarily focused on their prevalence in educators, and few studies have examined their [...] Read more.
Background/Objectives: There is growing interest in neuroscience-informed education, as well as neuroscience-derived strategies that maximise learning. Studies on neuroscience literacy and neuromyths, i.e., understandings or misconceptions about the brain, have primarily focused on their prevalence in educators, and few studies have examined their impact on students’ study habits or academic performance. Methods: To address this gap, we surveyed 576 university students in different academic programmes to investigate the relationship between neuromyths and academic outcomes in university students. In this quantitative, cross-sectional study design, we used a validated neuroscience knowledge survey and the Revised Two-factor Study Process (R-SPQ-2F) Questionnaire. We also inquired about students’ interest in, exposure to, and awareness of neuroscience, as well as their academic grades. Results: Students showed significant awareness of and interest in neuroscience; this was highest among students in health science programmes and lowest among students in computer and engineering programmes. The most common sources of general neuroscience knowledge were internet articles. Higher neuroscience literacy was associated with higher interest in neuroscience and having taken more neuroscience courses. Neuromyth scores were also better among those with higher neuroscience literacy scores. Higher neuroscience literacy scores were significantly associated with higher grades, higher surface strategy scores, and lower surface motive study habits. Conclusions: Our study sheds light on the variations in foundational neuroscience literacy among students in different academic programmes. It also provides insight into how this foundation affects academic performance and study habits. This insight may help guide educational policymakers to adopt neuroscience-based strategies that may be beneficial for learning. Full article
(This article belongs to the Special Issue Advances in Educational Neuroscience: Current Status and Future Directions)
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Review

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16 pages, 493 KiB  
Review
Educational Discrimination and Challenges of Inclusion During the Pandemic: The Case of Students with Autism Spectrum Disorder (ASD) from an International Perspective
by José Jesús Sánchez Amate, Antonio Luque de la Rosa and Pedro Tadeu
Brain Sci. 2025, 15(8), 848; https://doi.org/10.3390/brainsci15080848 - 8 Aug 2025
Viewed by 275
Abstract
Background: The COVID-19 pandemic exposed the fragility of educational systems in ensuring inclusive schooling, especially for students with Autism Spectrum Disorder (ASD). Disruptions to daily routines, the shift to remote learning, and the suspension of specialized services intensified pre-existing inequalities and affected the [...] Read more.
Background: The COVID-19 pandemic exposed the fragility of educational systems in ensuring inclusive schooling, especially for students with Autism Spectrum Disorder (ASD). Disruptions to daily routines, the shift to remote learning, and the suspension of specialized services intensified pre-existing inequalities and affected the educational continuity and well-being of this group. Methods: This narrative review analyzes the educational discrimination experienced by students with ASD during the pandemic. A structured search was conducted across databases including Scopus, Web of Science, PubMed, ERIC, Dialnet, and Google Scholar. Sixteen empirical studies published between 2020 and 2024 were selected based on criteria such as open access, focus on compulsory education, and direct analysis of pandemic-related exclusion. Results: The findings reveal four key challenges: unequal access to digital resources, the interruption of support services, increased family burden, and limited institutional responses. These factors contributed to emotional distress, regression in skills, and reduced participation in educational and social settings. Conclusions: The review concludes that the pandemic acted as a magnifying glass for structural barriers already present in inclusive education. Moving forward, educational systems must develop flexible, sustainable, and equity-oriented frameworks to ensure that students with ASD are not left behind during future crises. Full article
(This article belongs to the Special Issue Advances in Educational Neuroscience: Current Status and Future Directions)
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27 pages, 965 KiB  
Review
The Effectiveness of Artificial Intelligence-Based Interventions for Students with Learning Disabilities: A Systematic Review
by Andrea Paglialunga and Sergio Melogno
Brain Sci. 2025, 15(8), 806; https://doi.org/10.3390/brainsci15080806 - 28 Jul 2025
Viewed by 472
Abstract
Background/Objectives: While artificial intelligence (AI) is rapidly transforming education, its specific effectiveness for students with learning disabilities (LD) requires rigorous evaluation. This systematic review aims to assess the efficacy of AI-based educational interventions for students with LD, with a specific focus on [...] Read more.
Background/Objectives: While artificial intelligence (AI) is rapidly transforming education, its specific effectiveness for students with learning disabilities (LD) requires rigorous evaluation. This systematic review aims to assess the efficacy of AI-based educational interventions for students with LD, with a specific focus on the methodological quality and risk of bias of the available evidence. Methods: A systematic search was conducted across seven major databases (Google Scholar, ScienceDirect, APA PsycInfo, ERIC, Scopus, PubMed) for experimental studies published between 2022 and 2025. This review followed PRISMA guidelines, using the PICOS framework for inclusion criteria. A formal risk of bias assessment was performed using the ROBINS-I and JBI critical appraisal tools. Results: Eleven studies (representing 10 independent experiments), encompassing 3033 participants, met the inclusion criteria. The most studied disabilities were dyslexia (six studies) and other specific learning disorders (three studies). Personalized/adaptive learning systems and game-based learning were the most common AI interventions. All 11 studies reported positive outcomes. However, the risk of bias assessment revealed significant methodological limitations: no studies were rated as having a low risk of bias, with most presenting a moderate (70%) to high/serious (30%) risk. Despite these limitations, quantitative results from the stronger studies showed large effect sizes, such as in arithmetic fluency (d = 1.63) and reading comprehension (d = −1.66). Conclusions: AI-based interventions demonstrate significant potential for supporting students with learning disabilities, with unanimously positive reported outcomes. However, this conclusion must be tempered by the considerable risk of bias and methodological weaknesses prevalent in the current literature. The limited and potentially biased evidence base warrants cautious interpretation. Future research must prioritize high-quality randomized controlled trials (RCTs) and longitudinal assessments to establish a definitive evidence base and investigate long-term effects, including the risk of cognitive offloading. Full article
(This article belongs to the Special Issue Advances in Educational Neuroscience: Current Status and Future Directions)
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Other

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101 pages, 7201 KiB  
Systematic Review
Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy
by Evgenia Gkintoni, Hera Antonopoulou, Andrew Sortwell and Constantinos Halkiopoulos
Brain Sci. 2025, 15(2), 203; https://doi.org/10.3390/brainsci15020203 - 15 Feb 2025
Cited by 19 | Viewed by 13364
Abstract
Background/Objectives: This systematic review integrates Cognitive Load Theory (CLT), Educational Neuroscience (EdNeuro), Artificial Intelligence (AI), and Machine Learning (ML) to examine their combined impact on optimizing learning environments. It explores how AI-driven adaptive learning systems, informed by neurophysiological insights, enhance personalized education for [...] Read more.
Background/Objectives: This systematic review integrates Cognitive Load Theory (CLT), Educational Neuroscience (EdNeuro), Artificial Intelligence (AI), and Machine Learning (ML) to examine their combined impact on optimizing learning environments. It explores how AI-driven adaptive learning systems, informed by neurophysiological insights, enhance personalized education for K-12 students and adult learners. This study emphasizes the role of Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS), and other neurophysiological tools in assessing cognitive states and guiding AI-powered interventions to refine instructional strategies dynamically. Methods: This study reviews n = 103 papers related to the integration of principles of CLT with AI and ML in educational settings. It evaluates the progress made in neuroadaptive learning technologies, especially the real-time management of cognitive load, personalized feedback systems, and the multimodal applications of AI. Besides that, this research examines key hurdles such as data privacy, ethical concerns, algorithmic bias, and scalability issues while pinpointing best practices for robust and effective implementation. Results: The results show that AI and ML significantly improve Learning Efficacy due to managing cognitive load automatically, providing personalized instruction, and adapting learning pathways dynamically based on real-time neurophysiological data. Deep Learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs) improve classification accuracy, making AI-powered adaptive learning systems more efficient and scalable. Multimodal approaches enhance system robustness by mitigating signal variability and noise-related limitations by combining EEG with fMRI, Electrocardiography (ECG), and Galvanic Skin Response (GSR). Despite these advances, practical implementation challenges remain, including ethical considerations, data security risks, and accessibility disparities across learner demographics. Conclusions: AI and ML are epitomes of redefinition potentials that solid ethical frameworks, inclusive design, and scalable methodologies must inform. Future studies will be necessary for refining pre-processing techniques, expanding the variety of datasets, and advancing multimodal neuroadaptive learning for developing high-accuracy, affordable, and ethically responsible AI-driven educational systems. The future of AI-enhanced education should be inclusive, equitable, and effective across various learning populations that would surmount technological limitations and ethical dilemmas. Full article
(This article belongs to the Special Issue Advances in Educational Neuroscience: Current Status and Future Directions)
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