Research background: The rapid development of generative artificial intelligence (AI) and ongoing digital transformation are reshaping higher education and increasing demand for more flexible and competence-oriented educational approaches. Recent studies indicate that students tend to associate educational innovation less with technological novelty itself and more with flexibility, practical learning outcomes, and career relevance.
Purpose of the article: This article aims to identify the key educational and digital factors that positively influence students’ perceptions of innovative educational approaches that support critical thinking and personal development in higher education.
Methods: The quantitative research was conducted using a sample of 925 students from a partner university participating in a V4 research project. Students’ attitudes were collected through an online questionnaire administered in October 2025. The research hypotheses were tested using correlation analysis and linear regression modelling. The data were analysed using IBM SPSS Statistics.
Findings & Value added: The findings indicate that students place greater value on educational approaches that support practical skills development, flexibility, and preparation for future careers than on technological novelty itself. The strongest positive effects were identified for approaches supporting the development of future career skills and flexible educational systems. By contrast, several variables related to AI and digital resources exhibited weaker or statistically non-significant effects. This may indicate that students increasingly perceive digital technologies as a standard component of higher education rather than as a distinct indicator of educational innovation. The study contributes to current debates on AI and educational innovation by demonstrating that students associate innovation more strongly with competence development and flexibility than with technological novelty alone.
Anikó Csepregi, Andrej Rajský· Journal of Business Sectors· 0 citations
The rapid advancement of artificial intelligence is fundamentally reshaping the architecture and logic of Knowledge Management Systems (KMS). Traditionally, KMS have been designed around repositories, taxonomies, and retrieval mechanisms for storing and redistributing explicit knowledge. However, in the AI era – particularly with the emergence of generative models – the role of KMS extends beyond storage and retrieval, towards active participation in knowledge processing and knowledge creation. This paper examines the evolution of KMS in the AI era through the lens of the SECI model (Socialisation, Externalisation, Combination, Internalization). It argues that AI tools can introduce a new operational dynamic within each SECI phase if properly addressed. In the externalisation process, AI systems can facilitate the conversion of tacit and loosely articulated insights into structured representations. In the combination phase, machine learning models may enable pattern discovery and synthesis across heterogeneous knowledge sources. During internalisation, AI-powered assistants can support experiential learning by contextualising and personalising information. Most importantly, socialisation can be augmented through collaborative AI-mediated environments that enhance collective intelligence, reshaping how shared meaning is constructed. The study critically explores how AI-enhanced KMS can transform from passive infrastructures to evolve into adaptive cognitive systems supporting KM. While AI may increase speed, scalability, and pattern recognition, it also introduces epistemological risks – such as bias propagation, over-automation, and erosion of human judgment. The paper discusses how organisations can mitigate these risks while developing resilient and adaptive KM practices. Adopting a conceptual and integrative approach, the research analyses current technological capabilities and conceptual KM frameworks. This research proposes an updated perspective on KMS as a hybrid socio-technical ecosystem. In such systems, AI tools do not replace human knowledge actors but extend their cognitive and organisational capacities. The paper concludes that the future of KMS lies not in more sophisticated repositories, but in intelligent systems capable of dynamic codification, contextual reasoning, and continuous organisational learning, redefining the balance between human and machine agency in organisational knowledge processes.
A. Antonova, Dilyan Georgiev, Anikó Csepregi· European Conference on Knowl...· 0 citations
