Purpose of the study:
This study explores how human-centric assessments, improved by artificial intelligence (AI) and adaptive learning, can improve student engagement in the transition from Industry 4.0 to Industry 5.0. It examines how technological advancements are reshaping assessment methods to encourage personalized, student-centered learning experiences.

Design/methodology/approach:
A systematic literature review was conducted following the PRISMA framework, analysing studies published between 2010 and 2024 on AI-driven assessments and adaptive learning. Thematic analysis identified key trends, challenges, and opportunities in integrating human-centric assessments into higher education.

Findings:
Industry 4.0 technologies, such as AI, big data analytics, and intelligent tutoring systems, have improved personalised learning and real-time feedback. However, Industry 5.0 shifts the focus toward human-centric approaches, integrating emotional intelligence, collaborative learning, and teacher-student interaction into assessments. While AI-driven assessments improve efficiency and adaptability, challenges include ethical concerns, data privacy, and digital accessibility gaps.

Recommendations/value:
A balanced approach that combines AI-driven automation with pedagogical strategies is essential to maximize student engagement. Institutions should invest in faculty training, ethical AI frameworks, and policies ensuring equitable access to technology-driven assessments.

Managerial implications:
Policymakers must prioritize infrastructure improvements, data security measures, and professional development programs to support the effective implementation of AI-improved, human-centric assessments.

Authors:
Alexander Samuels, North West University, South Africa

About the Presenter(s)
Dr. Alexander Samuels, Lecturer at North-West University, specializes in supply chain management, technology integration, and education innovation. Currently pursuing a second PhD, focusing on robotics, AI, and student-centricity in education from In

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