GenAI in Entrepreneurship: a systematic review of generative artificial intelligence in entrepreneurship research: current issues and future directions
Project Overview
The document presents a systematic literature review examining the influence of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) on education, emphasizing their transformative role in enhancing learning systems and educational experiences. It identifies five thematic clusters: Digital Transformation & Behavioral Models, GenAI-Enhanced Education & Learning Systems, Sustainable Innovation & Strategic AI Impact, Business Models & Market Trends, and Data-Driven Technological Trends in Entrepreneurship. Within these clusters, the review elucidates how GenAI facilitates personalized learning, improves engagement, and supports adaptive educational methodologies, ultimately reshaping pedagogical approaches. Key applications include the development of intelligent tutoring systems, automated content generation, and data analytics for student performance tracking. The findings reveal that while GenAI offers significant opportunities to enhance educational outcomes and accessibility, it also brings forth ethical considerations and the necessity for robust regulatory frameworks to address potential biases and misuse. Overall, the document underscores the dual nature of GenAI in education, highlighting both its potential to revolutionize learning environments and the challenges that must be navigated to ensure responsible implementation.
Key Applications
GenAI-Enhanced Education & Innovation Systems
Context: Integration of GenAI tools like ChatGPT in educational settings to enhance entrepreneurial education and sustainable innovation. This includes fostering creativity among student entrepreneurs and improving decision-making efficiency through AI-driven insights.
Implementation: Institutional support for the integration of GenAI in curricula, focusing on personalized learning, creativity enhancement, and aligning AI tools with sustainability frameworks and strategic entrepreneurship objectives.
Outcomes: Improved entrepreneurial skills, enhanced creativity, iterative learning, and better problem-solving capabilities that facilitate greener business practices. However, concerns arise around overreliance on AI tools and ethical considerations related to data use and efficiency gains.
Challenges: Need for critical engagement with AI outputs, ensuring responsible use of GenAI tools, alignment with sustainability goals, and addressing limitations in semantic reasoning.
Implementation Barriers
Ethical
Concerns about data privacy, intellectual property, and algorithmic bias in AI outputs.
Proposed Solutions: Development of regulatory frameworks and ethical guidelines to ensure responsible use of GenAI in entrepreneurship.
Technical
Challenges related to the limitations of GenAI in tasks requiring semantic reasoning and domain-specific knowledge.
Proposed Solutions: Hybrid instructional strategies combining GenAI capabilities with educator-guided mentorship.
Project Team
Anna Kusetogullari
Researcher
Huseyin Kusetogullari
Researcher
Martin Andersson
Researcher
Tony Gorschek
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Anna Kusetogullari, Huseyin Kusetogullari, Martin Andersson, Tony Gorschek
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai