Investigating AI-driven assessment tools in engineering education: enhancing personalized learning for Industry 4.0 competencies.

Abolle-Okoyeagu, Chika Judith; Mahon, Ruissein; Okpu, Ambrose; Fernando, Wattala

Abstract

With Industry 4.0 constantly improving the industrial landscape, engineering education is subsequently fraught with the immediate challenge of ensuring synchrony between the ever-changing needs of automation, smart manufacturing as well as digital transformation. AI-based tools provide evolving, instantaneous assessment capabilities that allow learning facilitators to structure educational plans to individual learning styles and abilities. With the aid of machine learning algorithms, the tools can effectively analyze and record the progress of students; highlighting their various strengths, areas for improvement and above all, provide personalized feedback that will ultimately ensure skills acquisition. This paper investigates how such personalized assessments can enhance Mechanical Engineering students' proficiency in critical Industry 4.0 competencies, including but not limited to data analytics, robotics, additive manufacturing and the Internet of Things. The research explores how AI-driven assessment tools can be integrated into Mechanical Engineering curricula to better prepare students for Industry 4.0. The study would evaluate how AI can tailor educational experiences based on students' unique learning styles, abilities, and progress, specifically in core Mechanical Engineering disciplines and assess how AI-based tools can help bridge the gap between traditional mechanical engineering education and the evolving needs of Industry 4.0. Finally, the research proposes that AI-driven assessment tools enhance personalized learning experiences and further support a curriculum that is adaptive to technological advancements in Industry 4.0. By encouraging a profound awareness of AI's contribution to education, this study contributes to the current discussion on modernizing engineering curricula for future industry requirements.

Citation

ABOLLE-OKOYEAGU, C.J., MAHON, R., OKPU, A. and FERNANDO, W. 2024. Investigating AI-driven assessment tools in engineering education: enhancing personalized learning for Industry 4.0 competencies. In Abstract book of the proceedings of the 6th Global conference on education and teaching 2024 (GLOBALET 2024), 25-27 October 2024, Nice, France. Vilnius, Lithuania: Diamond Scientific Publishing [online], abstract number 30-4124. Available from: https://www.dpublication.com/wp-content/uploads/2024/10/30-4124.pdf

• Presentation Conference Type: Presentation / Talk
• Conference Name: 6th Global conference on education and teaching 2024 (GLOBALET 2024)
• Start Date: Oct 25, 2024
• End Date: Oct 27, 2024
• Acceptance Date: Sep 23, 2024
• Online Publication Date: Oct 25, 2024
• Publication Date: Oct 25, 2024
• Deposit Date: Oct 31, 2024
• Publicly Available Date: Nov 1, 2024
• Publisher: Mokslines Leidybos Deimantas (Diamond Scientific Publishing)
• Peer Reviewed: Peer Reviewed
• Article Number: 30-4124
• Book Title: Abstract book of the proceedings of the 6th Global conferences in education and teaching 2024 (GLOBALET 2024)
• Keywords: Industry 4.0; Industrial revolution; Digital technologies; Artificial Intelligence (AI); Internet of Things (IoT); Robotics; Manufacturing processes; Engineering education
• Public URL: https://rgu-repository.worktribe.com/output/2548723
• Publisher URL: https://www.dpublication.com/proceeding/6th-globalet