AI fuels creative breakthroughs in entrepreneurship education

Entrepreneurship education is undergoing a parallel revolution, one powered by artificial intelligence. The growing integration of large language models (LLMs) into educational contexts is reshaping how students develop core competencies like creativity, adaptability, and problem-solving. With AI's increasing influence on pedagogy, the strategic use of generative technologies in entrepreneurial instruction is fast becoming a hallmark of forward-looking education.

A new systematic review published in the journal AI, titled “AI and Creativity in Entrepreneurship Education: A Systematic Review of LLM Applications” by Jeong-Hyun Park, Seon-Joo Kim, and Sung-Tae Lee, presents the most comprehensive analysis to date on the impact of LLMs in fostering creativity within entrepreneurship education. Conducted using PRISMA methodology, the review evaluates 36 peer-reviewed studies from 2019 to 2024, focusing on LLMs’ role in enhancing learner engagement, creativity, and pedagogical innovation.

How are LLMs changing educational outcomes in entrepreneurial learning?

The study identifies LLMs as transformative tools that optimize learning environments, enhance learner motivation, and improve educational outcomes across a wide range of entrepreneurship education scenarios. Personalized feedback, real-time interaction, and adaptive learning pathways were found to significantly improve students’ self-efficacy and cognitive engagement. By providing context-aware support, LLMs help students structure complex ideas, test hypotheses, and explore business scenarios more efficiently.

In practical terms, LLMs streamline content comprehension and clarify abstract concepts, reducing cognitive load while increasing retention. Studies cited in the review demonstrate that AI-generated content often matches or exceeds human instruction in terms of learner satisfaction and engagement. These improvements are especially pronounced in areas like business strategy development and opportunity analysis, where the iterative generation of ideas is key.

LLMs also enhance learner motivation by addressing emotional and affective needs, contributing to sustained engagement. However, the study cautions that the quality of these outcomes hinges on effective prompt design and ethical use. Poorly structured tasks or excessive reliance on LLM-generated outputs risk diminishing critical thinking and promoting cognitive passivity. The authors stress the importance of integrating LLMs within well-scaffolded instructional frameworks that promote active learning and evaluation.

In what ways are LLMs being applied in entrepreneurship education?

The review reveals that LLMs are already widely used to simulate entrepreneurial scenarios, assist with business model development, support market analysis, and facilitate multicultural communication. Real-world case studies included in the review highlight how students use LLMs as digital collaborators to refine strategic plans, rehearse investor pitches, and navigate culturally diverse business environments.

LLMs enhance entrepreneurial education in four core areas:

1. Learning support functions: LLMs deliver real-time feedback and facilitate complex problem-solving.

2. Theoretical grounding: They integrate classical entrepreneurship theories, such as Schumpeter’s innovation and Drucker’s systematic entrepreneurship, into interactive learning experiences.

3. Creative development: By encouraging experimentation, LLMs act as generative thinking partners.

4. Ethical frameworks: Challenges such as algorithmic bias, data security, and over-dependence on AI are increasingly factored into curriculum design.

Examples include ChatGPT-based discovery learning that supports project-based innovation, and AI-generated simulations that teach resource optimization in circular economy models. These AI-driven environments foster a hands-on, reflective mindset that mirrors real-world entrepreneurial dynamics. Nevertheless, the researchers note ongoing ethical challenges, such as risks of homogenized thinking and loss of independent judgment, particularly when AI systems are used without critical oversight.

How do LLMs foster creativity in entrepreneurial learners?

The heart of the review is its novel framework analyzing how LLMs contribute to creativity using a five-component model: Actor, Process, Outcome, Domain, and Space. According to the authors, LLMs serve not only as content generators but also as co-creative agents that engage students in ideation, refinement, and collaborative innovation.

• Actor: LLMs enable students to explore and execute novel ideas, reinforcing their creative self-efficacy.

• Process: By supporting divergent and convergent thinking, LLMs help structure creative problem-solving.

• Outcome: They assist in producing original, actionable ideas tailored to specific business contexts.

• Domain: LLMs generate domain-relevant knowledge, enhancing cross-disciplinary creativity.

• Space: In digital learning environments, they act as facilitators of collaborative ideation and innovation.

The review also points out that creativity supported by LLMs is not just a result but an iterative process cultivated through dynamic interactions between learners and digital tools. In multicultural learning settings, LLMs bolster cross-cultural collaboration, preparing students for global entrepreneurship. However, the review urges educators to avoid over-automation, as excessive reliance on AI may lead to metacognitive laziness and suppressed originality.