It is widely accepted that cognitive load plays a crucial role in online learning. However, despite neurodevelopmental conditions being the largest category of qualifying disabilities in education, and the rise of online learning, there is little understanding of the factors impacting cognitive load in online learning for neurodivergent students and how these factors differ from those affecting neurotypical students. This study used qualitative comparison groups with neurotypical and neurodivergent students to examine their experiences of cognitive load in online learning. A sample of 26 university students (14 neurotypical and 12 neurodivergent) participated in focus group discussions. While neurodivergent students reported many similar experiences of cognitive load in online learning compared to their neurotypical peers—such as confusion in navigating the content and technical issues—some difficulties were more present for neurodivergent students—such as transcripts including mistakes and inaccessible content presentation—creating additional barriers in effectively engaging with the educational content. The results suggest that neurotypical and neurodivergent students experience similar challenges, albeit to differing degrees of intensity, and that more research is needed to explore the relationship between neurodiversity and cognitive load in online learning.

This theoretical paper presents the development and analysis of an inclusive educational framework designed to manage cognitive load for neurodivergent students in online learning environments. Drawing from cognitive load theory and neurodiversity studies, the framework is based on existing literature, empirical work conducted by the authors, and iterative feedback from a participatory research advisory board. Taking a neurodiversity-informed perspective that focuses on interventions addressing challenges common across a range of conditions, it identifies six critical areas that might impact cognitive load in online learning for neurodivergent students: format, environment, delivery, instruction, support, and research (FEDIS+R). To assess the external factors influencing the potential implementation of the framework and its place within the broader landscape of inclusive education, a PESTEL (Political, Economic, Social, Technological, Environmental, and Legal) analysis was conducted. The analysis highlights challenges such as resource disparities, institutional commitment to inclusion, and legal requirements for accessibility, which may affect the adoption of the framework. Given the evolving nature of both cognitive load theory and neurodiversity studies, future research directions are suggested to evaluate its effectiveness across diverse educational contexts. This paper contributes to the growing body of knowledge on neurodiversity in education and offers practical recommendations for educators and policymakers seeking to create inclusive online learning environments.

Inclusive research practice characterized by the involvement of neurodivergent people in meaningful roles has been described as a “requirement of excellence” in neurodiversity research. This report describes a co-design process in partnership with a Community Advisory Board undertaken to develop a research study involving neurotypical and neurodivergent students as participants. From the formation of the partnership with seven Community Advisory Board members to the outcomes of the co-design process, this report details the methods associated with each step, as well as the benefits and challenges collated in collaboration with members of the Community Advisory Board. Opportunities for improvement in co-design for participatory neurodiversity research are suggested.

Working memory is integral to a range of critical cognitive functions such as reasoning and decision‐making. Although alterations in working memory have been observed in neurodivergent populations, there has been no review mapping how cognitive load is measured in common neurodevelopmental conditions such as attention‐deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and dyslexia. This scoping review explores the neurophysiological measures used to study cognitive load in these specific populations. Our findings highlight that electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are the most frequently used methods, with a limited number of studies employing functional near‐infrared spectroscopy (fNIRs), magnetoencephalography (MEG) or eye‐tracking. Notably, eye‐related measures are less commonly used, despite their prominence in cognitive load research among neurotypical individuals. The review also highlights potential correlates of cognitive load, such as neural oscillations in the theta and alpha ranges for EEG studies, blood oxygenation level‐dependent (BOLD) responses in lateral and medial frontal brain regions for fMRI and fNIRS studies and eye‐related measures such as pupil dilation and blink rate. Finally, critical issues for future studies are discussed, including the technical challenges associated with multimodal approaches, the possible impact of atypical features on cognitive load measures and balancing data richness with participant well‐being. These insights contribute to a more nuanced understanding of cognitive load measurement in neurodivergent populations and point to important methodological considerations for future neuroscientific research in this area.

Working memory impairments are common in neurodevelopmental conditions, potentially impacting how neurodivergent students experience cognitive load during learning. We conducted a survey with 231 participants focused on students with attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and dyslexia. Parametric tests and a regression analysis were used to investigate the relationship between neurodiversity and perceived cognitive load in online learning. Neurodivergent students reported significantly higher extraneous cognitive load (ECL) in online learning compared to their neurotypical peers. However, no significant differences in perceived intrinsic and germane cognitive load were found between the two groups. Neurodiversity, and specifically ADHD, positively predicted perceived ECL in online learning. This study provides novel insights into the association between neurodiversity and cognitive load in online learning, suggesting a need for targeted support to help neurodivergent students reduce ECL in online learning environments and highlighting the importance of promoting inclusive educational practices that meet the needs of all students.