Many different engineering fields play a role in assisting and treating those with learning disabilities. One innovation of biomedical engineering is the fMRI, shown in the image above by The Transmitter.

Learning disabilities, often stemming from neurodevelopmental disorders, impact millions worldwide. From Dyslexia to Down Syndrome, these issues detrimentally impact education, work, and social integration. However, engineering has made great progress in directly treating or indirectly assisting those with these issues through creative solutions, and will continue to do so in the future.

Biomedical Engineering

Biomedical engineering has provided the most direct treatment approaches to learning disabilities. Through functional magnetic resonance imaging (fMRI), we can better examine brain activity in people with disorders like attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder, commonly referred to as ASD (Sen & Greiner, 2018). This imaging technology allows researchers to better identify abnormal patterns of brain activity, improving diagnoses, and provide more targeted medicines.

Furthermore, neural scaffolds, made from hydrogels, have been used to promote tissue regeneration, often axonal regeneration. These scaffolds provide structural support and guide growth of neurons, and are often used after injuries (Madhusudanan et. al., 2020). However, this regenerative property allows them to serve well for treating neurodevelopmental disorders, so long as they are incorporated with proper extracellular matrix and have proper mechanical properties. As this method and other related cell or tissue engineering methods improve, we’ll be able to provide more accurate and effective treatments that may resolve these disorders even before birth.

Software Engineering

Software engineering has made strides in helping with the learning process. While apps like Grammarly give people with written expression problems real-time feedback on grammar and syntax, text-to-speech (TTS) programs like Kurzweil 3000 allow people with dyslexia to access written content audibly (Young & MacCormack, 2021).

In order to monitor behavioral development and provide customized teaching methods for kids with ASD, personalized learning platforms incorporate machine learning algorithms. Augmentative and alternative communication (AAC) devices, many of which use sophisticated software to enable successful communication for non-verbal people, help to overcome social struggles.

Mechanical and Electrical Engineering

Through a combination of mechanical and electrical engineering, advancements in hardware have also helped to address learning impairments. For instance, people with cerebral palsy can operate computers using neurological signals thanks to brain-computer interfaces, or BCIs (Chandler et. al., 2022). Because it allows individuals to engage with digital tools, BCIs not only help with communication but also learning.

Adaptive robotics has also become popular. Through interactive play and educational activities, tools such as socially assistive robots (SARs) aid in the social and communication development of kids with ASD (Spitzer, 2024). These robots provide a customized educational experience by using sensors and machine learning to adjust to a child's emotional state and learning style.

Future Avenues

Integration and customization are key to the future of engineering for learning difficulties. Before conducting clinical trials, researchers may be able to mimic the effects of different treatments thanks to the development of comprehensive brain models made possible by advances in neuroinformatics . Furthermore, wearable neurotechnology, including portable EEG devices, may make it easier to monitor and help people with ADHD in real time, keeping them focused in class.

Bottom Line

Even with the advancements that have been made in treatment for learning disabilities, there is still more to come. As engineers continue creating solutions to combat the various aspects of learning disabilities, they help more people achieve their potential and promote an inclusive society.

Bibliography

Sen B., Borle and Brown R. Greiner MRG (2018, April 17). “A general prediction model for the detection of ADHD and Autism using structural and functional MRI.” PLoS ONE 13(4): e0194856. https://doi.org/10.1371/journal.pone.0194856.
Madhusudanan, P, Raju G., Shankarappa S. (2020, Jan 17). “Hydrogel systems and their role in neural tissue engineering” . J R Soc Interface. (162):20190505. doi: 10.1098/rsif.2019.0505. PMID: 31910776; PMCID: PMC7014813.
Young, G. and Jeffrey MacCormack (2014, Jun 10). “Assistive Technology for Students with Learning Disabilities”. LD@School, https://www.ldatschool.ca/assistive-technology/
Chandler, J. et. al. (2022). “Brain Computer Interfaces and Communication Disabilities: Ethical, Legal, and Social Aspects of Decoding Speech From the Brain”. Frontiers, https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2022.841035/full
Associated Press. “AI is a game changer for students with disabilities. Schools are still learning to harness it.” AP News, https://apnews.com/article/ff1f51379b3861978efb0c1334a2a953
Spitzer, H. (2024, Nov. 18). “How can we make the world a more sensory inclusive place?” Vox, https://www.vox.com/ad/389129/how-can-we-make-the-world-a-more-sensory-inclusive-place
Akst, J. (2023, Jan. 30). “Head motion mars most fMRI results, even after correction.” The Transmitter, https://www.thetransmitter.org/brain-imaging/head-motion-mars-most-fmri-results-even-after-correction/

Engineering Applications: Learning Disabilities