DEPARTMENT OF ADULT EDUCATION

Permanent URI for this communityhttps://repository.ui.edu.ng/handle/123456789/468

Browse

Subject: search.filters.subject.Machine Learning

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Integration of artificial intelligence in industrial education: a review of current trends and future directions
    (Stecab Publishing, 2025) Momoh, A. M.; Olajide, F. O.; Ogundipe, R. O.; Adesina, A. D.
    Digital revolution and the resultant emergence of Industry 4.0 has driven the incorporation of Artificial intelligence (AI) in Industrial education to enhance skills development in the industry. However, there is a lack of adequate empirical evidence on the integration of Artificial intelligence in industrial education. To fill this gap, this study reviewed previous studies on the adoption of AI in Industrial education and examined the frequency of occurrence of variables obtained in the studies reviewed. Relevant literature was screened and reviewed to find empirical evidence to support findings. A systematic review of 14 studies provided insights into the current applications, benefit and challenges of AI integration in industrial education. The study found that the most cited applications of AI is Adaptive and personalized learning systems, which customise workers/learners’ information based on their interaction with learning content. Other applications are augmented simulators for real-time feedback, virtual mentors, and intelligent tutoring systems which replicate real-life interaction with professionals among others. Majority of the studies found increased engagement and improved learning outcomes and skills development as benefit of AI integration in Industrial education. Other benefits are promotion of early identification of learning challenges and timely intervention and feedback, improvement in administrative efficiency and support, personalisation of learning. Notable challenges were skills and capacity gaps, lack of infrastructure and AI resources, curriculum issues and difficulty in integrating AI into current curriculum, ethical and privacy concerns among others. Based on the findings of the study, it was recommended that the skill gap should be filled with training in AI applications and use, investment in AI infrastructural development should be explored, industry collaboration and partnership in the area of needs should be considered, AI marketing and literacy should be adopted in industries, all AI intervention should be a continuing and lifelong process to ensure sustainability.
  • Thumbnail Image
    Item
    Leveraging artificial intelligence (AI) for Stress management in peak athletic performance: an integrative review
    (Stecab Publishing, 2025) Adebisi, E.; Balogun, T. N.; Oguntuase, S. B.; Olajide, F. O.
    Athletes often experience stressors that can impede their performance and well-being. Lack of coping resource or poorly regulated stress can undermine physical output, decision making and well-being. Effective stress management is crucial for achieving peak performance. This paper synthesize evidence on stress management strategies in sports psychology as well as AI powered stress management strategies using an integrative approach. The findings are presented narratively and organised into emerging themes. These strategies are categorized into cognitive-behavioural strategies, mindfulness-based strategies, relaxation strategies, biofeedback and technology-based and social support, and team-based strategies. AI powered stress management strategies were classified in the study as; AI powered wearable monitoring, virtual therapy and chatbot counseling, predictive analytics for stress forecasting, AI guided biofeedback training, AI enhanced injury and recovery support, AI supported cognitive behavior training, and integration of AI with human support systems. The following benefits are derived from the integration of AI in stress management for athletes; Improved performance, enhanced well-being, continuous and objective stress monitoring, all round support for athletes, Injury prevention, early detection and intervention, personalized training and recovery, confidentiality and stigma reduction. Advances in AI for stress management should focus on refining AI-powered injury prevention models, improving biometric sensing capabilities, advancing edge AI for realtime data processing, and integrating wearables sweat analysis to provide feedback, among others. This paper recommends that multimodal, specific interventions should be integrated into regular athletic training, warm-up and recovery process of athletes in order to attain peak performance and enhance overall well-being. Also that a hybrid approach adopted by sport psychologist integrating AI to support stress management among athletes will lead to a faster and positive outcome in stress management.