AI-Enhanced Trauma-Informed Differentiated Instruction for Neurodiverse Learners: Promoting Mental Health and Resilience in Schools
Article / Journal
Author(s) / editor(s):
Abiodun Peter Akande
,
Damilola Olamide Alomaja
,
Daniel Nwankwo Nwokwu
,
Emmanuel Kwakye Koduah
,
Igbanam Ogunte Iwowari
,
Julian Tagbo Ojiego
,
and Mohammed Mubarak Bello
Year: 2025
Language(s): English
Abstract:
Artificial intelligence (AI) is increasingly used to support neurodivergent learners (including those with autism, ADHD, and dyslexia, about 15–20% of students worldwide) through trauma‑informed, differentiated instruction. This review examines AI tools—such as adaptive learning systems and gamified platforms—that personalize content to sensory and cognitive needs, promoting more inclusive and equitable education.
Evidence from high‑income countries (e.g. Canada) shows AI can raise engagement by about 16% via real‑time content adaptation, while offline AI‑enabled apps in low‑resource settings (e.g. sub‑Saharan Africa) have increased participation by 18% despite infrastructure gaps. AI systems aligned with trauma‑informed principles can reduce stress by up to 20% and enhance self‑efficacy by 18%. In South Asia, AI using local languages has lowered dropout among neurodivergent learners by 12%.
However, major challenges remain, including algorithmic bias, connectivity deficits, and limited teacher training—particularly where only about 20% of teachers have inclusive‑practice competencies. Future priorities include low‑bandwidth AI solutions, culturally responsive design, and hybrid AI–human models. The article argues for policies that ensure ethical design and equitable access so AI can genuinely support neurodivergent students’ mental health, resilience, and learning globally.
https://doi.org/10.30574/wjarr.2025.28.3.4141
Post created by: Virginia Signorini