Future Generation’s Paths to Success in Research, Innovation, and Entrepreneurship: Framework Analysis of An International Student Workshop using Large Language Models
Year: 2026
Type: article
Abstract: Entrepreneurial universities are expected not only to generate knowledge but also to translate it into innovation and new ventures.Students, through their research, innovation, and entrepreneurial activities, are key contributors to this model.Using a large language model (LLM)-assisted qualitative approach, this study explored Central and Eastern European students' perspectives on research, innovation, and entrepreneurship, and evaluated the performance of LLMs in framework analysis.Fifteen interactive workshops were organised with 75 students from Slovakia (n = 42), Romania (n = 23), Hungary (n = 9), and Brazil (n = 1).Guided by Social Cognitive Theory, 483 responses were coded across 36 themes, yielding 17,388 data points analysed by three LLMs (GPT-4o-mini, Claude Sonnet 3.7, and DeepSeek v3.1) and validated by two independent human raters.The LLMs achieved 85.6% agreement, but only 58.8% of LLM-identified themes were jointly accepted by human reviewers.Summaries of accepted codes were produced using ChatGPT 5.1.Students expressed strong intrinsic motivation, altruistic values, and positive attitudes toward innovation, entrepreneurship, and research.Resilience, critical thinking, communication, networking, and time management emerged as common enablers, whereas low confidence, limited experience, and anxiety were key internal barriers.Time pressure, scarce funding, institutional rigidity, and complex regulations were the most prominent external challenges.Tailored institutional support is needed to foster student-driven innovation, entrepreneurship and research.
Source: Acta Polytechnica Hungarica
Language: English
Subfield: Management of Technology and Innovation
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7204998652