International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Artificial Intelligence and Machine Learning Based Approach to Motivate and Assist Primary School ADHD Children

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder among primary school children that significantly impacts their academic performance and social interactions. Traditional interventions for ADHD children often lack personalization and struggle to engage and motivate them effectively. This research paper proposes an innovative approach that leverages Artificial Intelligence (AI) and Machine Learning (ML) techniques to develop a personalized intervention system for motivating and assisting primary school children with ADHD. This study includes designing and developing an AI and ML-based intervention system tailored to the unique characteristics and preferences of ADHD children. The system will incorporate adaptive features to customize the intervention content, difficulty level, and feedback mechanism based on individual needs. Furthermore, the effectiveness of the intervention will be evaluated through standardized ADHD assessments, focusing on improvements in attention, impulsivity, and hyperactivity levels. Ethical considerations regarding privacy, data security, and potential risks associated with AI and ML interventions will be addressed to ensure the responsible and ethical deployment of these technologies. Practical recommendations will be provided for parents, and professionals to facilitate the implementation of AI and ML interventions in primary school settings.

Country : Sri Lanka

1 Balage Diniru Sandipa2 Dulakshi Madhuhansani Mitratne3 Madapathage Dona Ashani Isurika4 Dulanga Semini Kandanaarachchi5 Samadhi Chathuranga Rathnayaka6 Wishalya Wanshanee Tissera

  1. Department of Computer science and Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  2. Department of Computer science and Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  3. Department of Computer science and Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  4. Department of Computer science and Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  5. Department of Computer science and Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  6. Department of Computer science and Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

IRJIET, Volume 7, Issue 9, September 2023 pp. 89-96

doi.org/10.47001/IRJIET/2023.709010

Full Paper
Download

References

  1. K. Holland, “The History of ADHD: A Timeline,” Healthline, Oct. 28, 2021. https://www.healthline.com/health/adhd/history
  2. R. A. Barkley and H. Peters, “The Earliest Reference to ADHD in the Medical Literature? Melchior Adam Weikard’s Description in 1775 of ‘Attention Deficit’ (Mangel der Aufmerksamkeit, Attentio Volubilis),” Journal of Attention Disorders, vol. 16, no. 8, pp. 623–630, Feb. 2012, doi: 10.1177/1087054711432309.
  3. C. C. M. Professional, “DSM-5,” Cleveland Clinic. https://my.clevelandclinic.org/health/articles/24291-diagnostic-and-statistical-manual-dsm-5#:~:text=In%202022%2C%20the%20APA%20published,accurate%20version%20of%20this%20resource.
  4. R. Drechsler, S. Brem, D. Brandeis, E. Grünblatt, G. Berger, and S. Walitza, “ADHD: Current Concepts and Treatments in Children and Adolescents,” Neuropediatrics, vol. 51, no. 05, pp. 315–335, Jun. 2020, doi: 10.1055/s-0040-1701658
  5. “Whaam - Web Health Application for Adhd Monitoring.” https://app.whaamproject.eu/en/index.php
  6. J. M. Vasko, “Psychosocial Interventions for College Students with ADHD: Current Status and Future Directions | The ADHD Report,” The ADHD Report. https://guilfordjournals.com/doi/abs/10.1521/adhd.2020.28.4.5
  7. R. Periyasamy, V. Vibashan, G. Varghese, and Mohd. A. Aleem, “Machine Learning Techniques for the Diagnosis of Attention-Deficit/Hyperactivity Disorder from Magnetic Resonance Imaging: A Concise Review,” Neurology India, vol. 69, no. 6, p. 1518, Jan. 2021, doi: 10.4103/0028-3886.333520.
  8. Md. Maniruzzaman, J. Shin, and Md. A. M. Hasan, “Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis,” Applied Sciences, vol. 12, no. 5, p. 2737, Mar. 2022, doi: 10.3390/app12052737.
  9. M. Adamou, T. Fullen, and S. Jones, “EEG for Diagnosis of Adult ADHD: A Systematic Review with Narrative Analysis,” Frontiers in Psychiatry, vol. 11, Aug. 2020, doi: 10.3389/fpsyt.2020.00871.
  10. P. D. Barua et al., “Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review,” International Journal of Environmental Research and Public Health, vol. 19, no. 3, p. 1192, Jan. 2022, doi: 10.3390/ijerph19031192.
  11. T. Chen, I. Tachmazidis, S. Batsakis, M. Adamou, E. Papadakis, and G. Antoniou, “Diagnosing attention-deficit hyperactivity disorder (ADHD) using artificial intelligence: a clinical study in the UK,” Frontiers in Psychiatry, vol. 14, Jun. 2023, doi: 10.3389/fpsyt.2023.1164433.
  12. R. Drechsler, S. Brem, D. Brandeis, E. Grünblatt, G. Berger, and S. Walitza, “ADHD: Current Concepts and Treatments in Children and Adolescents,” Neuropediatrics, vol. 51, no. 05, pp. 315–335, Jun. 2020, doi: 10.1055/s-0040-1701658.
  13. K. Sayal, V. Prasad, D. Daley, T. Ford, and D. Coghill, “ADHD in children and young people: prevalence, care pathways, and service provision,” The Lancet Psychiatry, vol. 5, no. 2, pp. 175–186, Feb. 2018, doi: 10.1016/s2215-0366(17)30167-0.
  14. A.Caye, J. M. Swanson, D. Coghill, and L. A. Rohde, “Treatment strategies for ADHD: an evidence-based guide to select optimal treatment,” Molecular Psychiatry, vol. 24, no. 3, pp. 390–408, Jun. 2018, doi: 10.1038/s41380-018-0116-3.
  15. P. H. H. Lopez et al., “Cognitive-behavioural interventions for attention deficit hyperactivity disorder (ADHD) in adults,” The Cochrane Library, vol. 2018, no. 3, Mar. 2018, doi: 10.1002/14651858.cd010840.pub2.
  16. “Artificial intelligence in education,” UNESCO, Jun.                 2023, [Online]. Available: https://www.unesco.org/en/digitaleducation/artific al-intelligence
  17. K. Zhang and A. B. Aslan, “AI technologies for education: Recent research & future directions,” Computers & Education: Artificial Intelligence, Jan. 2021, doi: 10.1016/j.caeai.2021.100025.
  18. S. Akgun and C. Greenhow, “Artificial intelligence in education: Addressing ethical challenges in K-12 settings,” AI And Ethics, vol. 2, no. 3, pp. 431– 440, Sep. 2021, doi: 10.1007/s43681-021-00096-7.
  19. Johnson, A. B., et al. (2018). "Primary data source selection for research: A survey." Journal of Research Methods, 21(4), 256-269.
  20. Pedregosa, F., et al. (2011). "Scikit-learn: Machine learning in Python." Journal of Machine Learning Research, 12, 2825-2830.
  21. Conners, C. K. (2014). Conners 3rd Edition: Manual. Multi-Health Systems.
  22. Lundberg, S. M., & Lee, S. I. (2017). "A unified approach to interpreting model predictions." In Guyon, I., et al. (Eds.), Advances in Neural Information Processing Systems (pp. 4765-4774). Curran Associates, Inc.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies