This study investigates the use of AI-driven macro expression analysis to enhance the engagement of hyperactive students in English language learning. By utilizing Convolutional Neural Networks (CNN) and K-Nearest Neighbors (K-NN), this research aims to detect and analyze students' macro facial expressions, as well as their correlation with engagement levels. Data was obtained from 24 learning videos, consisting of 13,263 frames, analyzed to identify expressions of boredom, sadness, and happiness. The analysis results show that boredom and sadness dominate, while happiness is recorded at a lower frequency, indicating the need for a more varied and responsive teaching approach. This study also finds that AI-driven emotion detection can provide more adaptive feedback for hyperactive students, allowing teachers to adjust teaching methods in real-time according to the students' emotional responses. The findings contribute new insights into the field of inclusive education by integrating AI technology to monitor and tailor learning for students with special needs. Theoretically, this research enriches the understanding of the role of macro expressions in student engagement, particularly in the context of ADHD. Practically, the results offer technology-based solutions to support more adaptive and responsive teaching that aligns with students' emotional changes. This research contributes to the development of more holistic and interactive learning methods, which can improve learning outcomes for students with special needs, especially in English language education.

The accelerating pace of industrial transformation necessitates a strategic reconfiguration of higher education curriculum to ensure alignment with dynamic labour market demands. This study introduces a hybrid decision-making framework that integrates Machine Learning with Multi-Criteria Decision Making techniques to evaluate and classify the readiness and relevance of academic programs. The methodological core includes the Step-wise Weight Assessment Ratio Analysis, Linguistic q-Rung Orthopair Fuzzy Numbers, and the Multi-Attributive Border Approximation Area Comparison method for criteria weighting, coupled with a classification model based on Support Vector Machine optimized using the Salp Swarm Optimization algorithm. The results demonstrate the framework's efficacy in identifying curriculum gaps and recommending adaptive enhancements, especially for programs categorized as “Needs Improvement” Beyond classification, the system facilitates strategic curriculum planning, fosters pedagogical innovation, and promotes industry-responsive learning pathways. This study highlights the transformative potential of machine learning in higher education, equipping students with the skills required to navigate an increasingly dynamic professional landscape, while offering actionable insights into instructional redesign, competency-based delivery, and industry-informed pedagogy. Future research will explore longitudinal impact assessment and broader stakeholder integration to enhance the framework’s scalability and contextual adaptability.