Deconstructing the Veneer of Simplicity: Co-Designing Introductory Generative AI Workshops with Local Entrepreneurs

Document Title

Deconstructing the Veneer of Simplicity: Co-Designing Introductory Generative AI Workshops with Local Entrepreneurs

Document Information

• Subject Area: Human-Computer Interaction (HCI) and the application of generative AI in community entrepreneurship
• Keywords: Generative AI, entrepreneurship, community-based participatory research, human-computer interaction, social support

Research Background and Problems

• What issues or challenges did the authors identify?
  This study explores the potential and existing challenges of using generative AI in entrepreneurship. Although tools like ChatGPT and DALL-E are designed to be "simple and easy to use," these technologies may exacerbate the digital divide and further disadvantage entrepreneurs from marginalized communities. The use of generative AI often requires technical expertise or higher educational levels, while many local entrepreneurs face limited access to technical resources, systemic inequalities in education, and a lack of awareness about generative AI technologies. This has made technological inclusivity an increasingly important focus for policy and research.

• Why is this issue important?
  The study focuses on resource-limited communities, particularly entrepreneurial groups in areas affected by poverty and racial inequality. These groups often engage in entrepreneurship out of economic necessity, but their unfamiliarity with and distrust of technological tools exacerbate their challenges in sustaining and growing their businesses. Generative AI tools like ChatGPT and DALL-E can significantly enhance efficiency and creativity, but intervention models that ensure equity, inclusivity, and support within communities are crucial for bridging the technological divide.

• Research Motivation and Related Work
  This study is inspired by low-tech support models, such as long-standing practices of community mentoring and technical assistance. Previous research on local entrepreneurs has shown that building informal, non-hierarchical support networks can significantly improve technology adoption rates and trust. For example, one-on-one technical support or shared experiences have proven effective. However, how to construct support models for these users in the rapidly evolving field of generative AI remains an unresolved issue. This study aims to address this gap.

Solutions

• What methods or solutions did the authors propose?
  The authors proposed co-designing a series of introductory generative AI workshops to help local entrepreneurs overcome the misleading perception of technology as "simple and easy to use" while addressing their technological anxieties. The research team collaborated with the community organization Community Forge to host four interactive workshops over five months, emphasizing community participation, hands-on experiences, and long-term technical support.

• What is innovative about this solution?
  The innovation lies in the workshops not only focusing on the use of technology itself but also on building trust networks within the community, supporting non-use patterns, and highlighting practical business applications and ethical considerations. The workshop format was centered on community needs, using iterative design to explore the practical applicability of generative AI in local businesses.

• What are the implementation steps, and what key technologies were used?

  1. Initial Design: The research team collaborated with community leaders to set goals and identify application themes for generative AI, such as marketing.
  2. Workshop Structure: Each workshop included community gatherings, live demonstrations of devices and tools, group collaboration, and hands-on practice. Tools included ChatGPT, DALL-E2, Canva, and Midjourney.
  3. Feedback and Iteration: Feedback was collected from entrepreneurs and technology providers after each workshop through surveys, questionnaires, and one-on-one interviews to continuously refine the design.
  4. Long-term Support: A weekly Tech Help Desk was established to provide one-on-one assistance to entrepreneurs for specific technical issues while maintaining the community support network.

Research Outcomes

• What specific outcomes were achieved?

  1. Entrepreneurs gained an initial understanding of practical applications of generative AI tools, such as website copywriting, marketing image generation, and improving product descriptions.
  2. The study identified the foundational skills required for entrepreneurs to successfully use these tools, including browser navigation, file storage and conversion, graphic design, password management, and building self-efficacy.
  3. Key concerns among entrepreneurs regarding generative AI were highlighted, including intellectual property issues, racial and gender biases, authenticity, and risks of technological dependency.

• What advantages does this solution have compared to existing approaches?
  This study provides a community-centered technological intervention approach, emphasizing support for entrepreneurs' choices between "use and non-use" while focusing on building their technical capabilities and fostering community capital. Unlike traditional technology promotion methods, it addresses the psychological and emotional aspects of entrepreneurship while designing a coherent collaboration and support framework.

• What were the experimental or evaluation results?
  Through interviews and workshops, the study revealed that beyond the surface-level "simplicity" of generative AI, entrepreneurs require complex skill training and support systems for technology adoption. Additionally, entrepreneurs' intuitive skepticism toward technology highlighted the limitations of tool design and underscored the necessity of leveraging community resources to bridge the digital divide.

• Limitations and Future Directions

  1. Limitations: Participants recruited for the study tended to actively engage in workshops, which may skew data toward positive feedback. Furthermore, the evaluation of generative AI usage did not cover long-term impacts.
  2. Future Directions: Enhance the systemization of courses and effectiveness of knowledge transfer; design research methods to support "non-use"; explore the impact of technology ethics education and rapidly changing policies on entrepreneurs; develop broader community support systems to address policy updates and technological challenges.