Publication Details
Abstract
The integration of Artificial Intelligence (AI) into educational systems has emerged as a powerful strategy to address disparities in access, inclusion, and learning outcomes, especially in regions experiencing socio-political crises. Inclusive education aims to provide equitable and quality learning opportunities for all students, including those with disabilities, internally displaced persons (IDPs), and children affected by trauma or poverty. In the North West Region of Cameroon one of the areas most affected by the Anglophone crisis, access to education has been significantly disrupted due to school closures, displacement, insecurity, and loss of infrastructure. Within this complex and volatile environment, AI-based tools such as adaptive learning systems, virtual classrooms, text-to-speech and speech-to-text software, and intelligent tutoring applications offer potential pathways for advancing inclusive education. However, there is limited empirical evidence on how AI technologies are being used to support inclusion in crisis-affected educational contexts in Cameroon. This study investigates the effects of Artificial Intelligence on inclusive education for learners in crisis-affected areas, using selected schools in the North West Region of Cameroon as a case study. The study is anchored on two theoretical frameworks: the Universal Design for Learning (UDL) developed by David H. Rose and Anne Meyer (1990), and the Technological Pedagogical Content Knowledge (TPACK) model proposed by Punya Mishra and Matthew J. Koehler (2006). UDL provides a framework for designing flexible learning environments that accommodate individual differences in learners by promoting multiple means of engagement, representation, and expression. TPACK, on the other hand, emphasizes the dynamic intersection of content, pedagogy, and technology, which is essential for teachers to effectively integrate AI tools into inclusive learning practices. These theories collectively guide the study's approach to exploring how AI technologies can promote equitable and accessible learning in fragile environments. The study employs a concurrent mixed-methods case study design, drawing on both quantitative and qualitative data collected from teachers, school administrators, inclusive education coordinators, and learners including those with special needs. Data will be gathered from ten schools across three divisions (Mezam, Bui, and Ngoketunjia) using questionnaires and interviews. The data will be analyzed using SPSS for descriptive and inferential statistics, while thematic analysis using NVivo will be applied to qualitative data. Qualitative findings suggest that while AI tools are available in a limited number of schools (primarily private or NGO-supported), their application significantly enhances differentiated learning, learner engagement, and accessibility for students with disabilities. Tools such as text-to-speech software helped visually impaired students to participate in class, while AI-enabled language translation apps facilitated multilingual instruction in linguistically diverse settings. Moreover, teachers reported that AI platforms enabled them to monitor learner progress and adapt instruction based on individual needs. However, major challenges remain: low digital literacy among teachers, insufficient training, unreliable internet connectivity, absence of electricity in some rural areas, and limited availability of culturally and linguistically relevant AI tools. Additionally, findings highlight ethical concerns regarding data privacy and the risk of reinforcing inequality through bias in AI algorithms. Quantitative data revealed that 68% of teachers who had access to AI tools perceived a positive impact on inclusive learning outcomes, while 77% of learners with special needs expressed increased participation and satisfaction with AI-enhanced lessons. However, only 32% of the schools surveyed had implemented any form of AI-based instruction, pointing to a critical digital divide within the region. The significance of this study lies in its contextual relevance and its contribution to local and global discourses on technology and inclusion in education. It offers policy recommendations for the Cameroonian government and stakeholders, including the integration of AI into national inclusive education policies, the development of teacher training programs on AI literacy, and the investment in local AI innovation tailored to the needs of learners in crisis-affected areas. Furthermore, it adds empirical evidence to the global conversation on how to leverage AI to enhance education in conflict zones, aligning with Sustainable Development Goal 4: "Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all." The study finally finds that AI has the potential to revolutionize inclusive education in fragile and crisis-affected environments if it is deployed strategically, ethically, and contextually. The results highlight the urgent need for a multi-stakeholder approach to overcome infrastructural, pedagogical, and policy-related barriers, and to build a resilient, inclusive, and future-oriented educational system in Cameroon and similar conflict-affected regions.