Classrooms normally use teacher-led instructions as a teaching method despite the type of learner they have in class. As the world evolves, psychology has been used to assess or diagnose learners who have been known as low academical achievers as neurodevelopmental learners. Neurodevelopmental learners have their own way of thinking their own world as some normally say but that does not deny them the right to proper education that suits their needs. This research focuses on comparing the effectiveness of adaptive AI learning systems and traditional teaching methods in enhancing engagement among autistic learners, with a specific focus on Botswana's educational framework. Supported by the rapidly growing global debate on inclusive education, this research studies how adaptive AI learning systems can be used to foster continued learner engagement through personalisation to meet each learner's needs. Unlike traditional teaching methods that use teacher-led instructions, memorisation, repetitive drills etc., AI-driven learning systems have real-time feedback, content delivery and adaptive pacing tailored to each learner based on their needs. This research will use mixed methods design, where quantitative measures of learner engagement and qualitative insights from teachers, learners and parents or caregivers to procure statistic trends and individual encounters. The findings of this research will determine whether adaptive AI learning systems are a better solution for autistic learners if they promote engagement, interaction and zeal for education. On the other hand, the findings will identify loopholes in traditional teaching methods, for example, resource constraints, teaching skills and training for neurodevelopmental learner and cultural or ethical considerations in Botswana's context. In summary, this study aims to explore the potential of adaptive AI as a means for promoting equal learning opportunities in Botswana's educational context driven by the inclusive education development.