Shaping the Future Workforce: Students' Perceptions on AI and Human-Centric Technologies in Industry 5.0

Ferhataj Anxhela (), Biçoku Jonida () and Memaj Fatmir ()
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Ferhataj Anxhela: Faculty of Engineering, Informatics and Architecture, European University of Tirana, Tirana, Albania
Biçoku Jonida: Faculty of Business and Law, "Aleksander Xhuvani" University, Elbasan, Albania
Memaj Fatmir: Faculty of Economy, University of Tirana, Tirana, Albania

Economics and Culture, 2025, vol. 22, issue 1, 136-147

Abstract: Research purpose. This paper aims to analyse how university students perceive the role of Artificial Intelligence and human-centric technologies in shaping the future workforce within the context of Industry 5.0. By focusing on students as future professionals, the study explores their expectations, ethical concerns, and readiness for AI-driven transformation in a developing country context. Design / Methodology / Approach. A total of 344 university students from the disciplines of Information Technology, Computer Engineering, and Business Management participated in the study through an online questionnaire. The survey assessed students’ knowledge of AI technologies, their perceptions of AI’s role in workforce transformation, and their ethical concerns related to AI integration. Data were collected from Bachelor’s and Master’s students enrolled at public and private higher education institutions in Albania. The study complied with institutional research ethics regulations. As it involved an anonymous, voluntary survey without the collection of sensitive or personally identifiable data, formal ethics committee approval was not required. Spearman’s Rank Correlation and descriptive statistics were used to analyse the relationships between AI knowledge, workforce perceptions, and ethical concerns. Findings. The findings reveal that students generally recognize AI’s transformative potential in reshaping industries and workforce dynamics, particularly among Information Technology and Computer Engineering students. However, they express scepticism regarding AI’s ability to enhance human-centric traits such as creativity and emotional intelligence. A statistically significant positive correlation was found between AI knowledge and perceptions of industrial transformation and skill augmentation, indicating that higher digital literacy is associated with greater optimism about AI’s role. Conversely, students who voiced stronger ethical concerns—especially regarding algorithmic bias, privacy, and job displacement—also placed greater emphasis on the need for transparency and explainability in AI systems. These patterns reflect the relevance of both the Technology Readiness and Acceptance Model (TRAM) and Trust in Automation Theory, underscoring the importance of integrating ethical reasoning with technical training to prepare students for responsible engagement with AI in the context of Industry 5.0. Originality / Value / Practical implications. While student perceptions of AI have been widely examined in global contexts, this study offers a distinct contribution by focusing on Albania—a developing country underrepresented in the AI and Industry 5.0 literature. Its originality lies in the integration of two behavioural frameworks—the Technology Readiness and Acceptance Model (TRAM) and Trust in Automation Theory—to explain students’ attitudes toward AI within a human-centric industrial paradigm. The research draws on insights from students in Information Technology, Computer Engineering, and Business Management, integrating both ethical and educational dimensions. These findings provide practical implications for designing inclusive, ethically grounded, and future-ready strategies in education and workforce development.

Keywords: AI integration; industry 5.0; workforce evolution; human-centric technologies; ethical AI (search for similar items in EconPapers)
JEL-codes: I23 L86 O33 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:ecocul:v:22:y:2025:i:1:p:136-147:n:1011

DOI: 10.2478/jec-2025-0011

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