Discipline-specific AI adoption in higher education: insights for advancing quality education and sustainability (SDG 4)
Discipline-specific AI adoption in higher education: insights for advancing quality education and sustainability (SDG 4)
StatusVoR
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Authors
Berniak-Woźny, Justyna
Kowalska-Jarnot, Katarzyna
Stach, Przemysław
Plebańska, Marlena
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Date
2026-05
Publisher
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Zeszyty Naukowe Politechniki Śląskiej. Seria Organizacja i Zarządzanie
Issue
Volume
246
Pages
Pages
23-44
ISSN
1641-3466
2720-751X
2720-751X
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Access date
2026-05
Abstract PL
Abstract EN
Purpose: This study examines the usage patterns of artificial intelligence (AI) tools among 859 university students in Poland, representing three major academic fields. The research aims to identify discipline-specific differences in AI adoption, usage motivations, perceived benefits, and challenges, contributing to evidence-based strategies for advancing Sustainable Development Goal 4 (Quality Education). Design/methodology/approach: An exploratory quantitative approach was employed using a cross-sectional online survey conducted from February to May 2025. Data from 859 students across humanities and social sciences, natural sciences and technology, and arts and communication were analysed using descriptive statistics and nonparametric methods (KruskalWallis and DSCF post-hoc tests). Findings: Significant inter-group differences were identified in AI adoption patterns. Humanities students primarily use AI for knowledge expansion (54%) and translation (48%). Natural sciences students emphasise problem-solving (51%) and knowledge acquisition (62%). Arts students focus on translation (63%) and creative content generation (46%). Despite widespread adoption, students across all disciplines express concerns about data privacy, ethics, and employment impacts. Research limitations/implications: Convenience sampling and unequal group sizes limit generalizability. The cross-sectional design captures a single point during rapid technological change. Future longitudinal and comparative studies are recommended. Practical implications: Universities should develop discipline-specific AI integration strategies, targeted professional development for faculty, and comprehensive AI literacy curricula. Preparing graduates for the evolving business landscape requires aligning AI literacy, critical thinking, and ethical competencies with industry expectations. Social implications: The findings underscore the need for inclusive and equitable AI integration in higher education to prevent new forms of digital inequality, contributing to sustainable economic growth, digital transformation, and responsible workforce development. Originality/value: This study provides empirical evidence on discipline-specific AI adoption patterns in the underexplored Central and Eastern European context, documenting usage during the critical transition from experimental to systematic AI integration in higher education.
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Keywords PL
Keywords EN
artificial intelligence
higher education
AI literacy
cross-disciplinary differences
sustainable education
higher education
AI literacy
cross-disciplinary differences
sustainable education
Keywords other
Sustainable Development Goals
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