The growing diffusion of Artificial Intelligence (AI) across academia marks a profound shift in how universities engage with entrepreneurship and innovation. Within the emerging paradigm of academic entrepreneurship (AE), knowledge is no longer transferred linearly from research to market but co-created in dynamic ecosystems that blend human creativity with machine intelligence (Jarrahi, 2018; Krotov, 2025). The resulting human–AI synergy reframes the university from a traditional knowledge producer into an intelligent hub of entrepreneurial experimentation and collaborative value creation (Wilson & Daugherty, 2024). Recent literature identifies AI as a critical external enabler for entrepreneurial ecosystems, expanding access to data-driven insights and predictive modeling that enhance decision-making and opportunity recognition (Duan et al., 2019; Townsend & Hunt, 2019). Within university contexts, AI-driven analytics assist researchers in identifying commercialization pathways, mapping intellectual property landscapes, and forecasting market viability (Rajagopal et al., 2022; Rippa & Secundo, 2019). Moreover, Generative AI (GenAI) tools such as ChatGPT are emerging as catalysts for rapid ideation and prototyping, enabling academics to transform complex research findings into viable entrepreneurial concepts (Campbell et al., 2025; Sundberg & Holmström, 2024).

At the individual level, AI can enhance academic entrepreneurs’ cognitive capacities by augmenting creativity, reducing cognitive load, and supporting decision-making under uncertainty (Haefner et al., 2021; Lévesque et al., 2022). However, this empowerment depends on the quality of human input and critical reflection. At the organizational level, entrepreneurial universities are reconfiguring their structures to integrate AI in technology transfer offices, incubation programs, and entrepreneurship education (Etzkowitz, 2003; Secundo et al., 2020). At the ecosystem level, AI is transforming the classic Triple Helix relationship among university, industry, and government (Etzkowitz, 2017). AI-enabled platforms facilitate new forms of cross-sector collaboration, enhance transparency in research funding, and accelerate the co-creation of digital spin-offs and public–private innovation initiatives (Schaeffer et al., 2021; Guerrero & Urbano, 2019). This convergence creates fertile ground for Digital Academic Entrepreneurship – an emerging domain in which data-driven intelligence and human creativity jointly drive sustainable innovation (Rippa & Secundo, 2019; Secundo et al., 2020).

By highlighting the interplay between human and artificial intelligence, this track aims to advance a holistic understanding of how AI reshapes academic entrepreneurship ecosystems and promotes dialogue on balancing efficiency, creativity, and ethics in digital transformation. Therefore, we welcome empirical, methodological, or conceptual papers related (but not limited) to the following research questions:

• How does AI transform the dynamics of academic entrepreneurship ecosystems?
• In what ways can AI-driven tools augment opportunity recognition, venture creation, and knowledge transfer within universities?
• How can human-AI collaboration enhance creativity, ethical awareness, and strategic decision-making across different levels (individual, organizational, and ecosystem) of the academic entrepreneurship?
• What governance models, educational frameworks, and policy interventions are needed to ensure that AI integration fosters inclusivity, sustainability, and long-term adaptability in academic innovation ecosystems?
• How can AI support the development of entrepreneurial capabilities?
• How does AI reshape university-industry collaboration, influencing knowledge exchange, co-creation processes, and innovation outcomes?
• How can the impact of AI adoption on academic entrepreneurship performance be measured