CASE STUDIES
STEM Education for Girls in East Africa
This case study examines an AI-driven approach to enhancing STEM education for girls in Kenya and Rwanda, utilizing the ESAM® innovation platform. This advanced tool integrates Augmented Intelligence and Generative AI, such as ChatGPT. Aligning with United Nations Sustainable Development Goals (SDGs) 4 and 10, the project aims to foster equal educational opportunities.
In 2021, a team led by internationally recognized innovation expert Bob King, three East African educators, and ESAM® specialist Ben Babaii spent 50 hours designing solutions. AI tools reduced this by at least 25 hours, accelerating idea generation.
Comparing Human vs. AI-Driven Innovation
A previous study, ‘Ideation AI Application for Complexity Management’ (ICSI 2023), demonstrated how Generative AI could streamline complex problem-solving. This project evaluated the effectiveness of ESAM® compared to a traditional café-style human collaboration format for STEM education improvement.
Using ESAM®, the team mapped system complexity, formulated innovation directions, and generated ideas. In the café setting, 50 hours were spent on structured brainstorming, whereas ESAM® reduced this to just 11 hours, with concept generation using ChatGPT taking under one hour. The AI model identified contradictions within the system to build resilient solutions faster.
Results & Conclusion
This study examines whether Generative AI can replace some or all human experts, thereby dramatically accelerating innovation. While traditional team-based design requires deliberation, consensus, and arbitration, AI eliminates much of this process. Seed ideas emerged in minutes, achieving an estimated 5x acceleration with innovation direction alone, and 10x acceleration when combined with refinements.
By integrating AI into STEM education development, this project demonstrates the power of human-machine collaboration in solving complex social challenges.
COVID-19 Problem-Solving with ESAM®
This case study, done in December 2020, showcases the effectiveness of the ESAM® innovation platform, an AI-powered problem-solving tool. The rapid spread of COVID-19 created confusion among experts, with contradictory recommendations emerging globally. Using an earlier version of ESAM® in a café-style expert format, we worked to unify conflicting health directives. The result: 13 recommendations in three key groups aimed at slowing transmission and easing hospital burden, communicated to health authorities in the US and Canada.
Project Overview
COVID-19 exemplifies a complex, ambiguous system where expert disagreements hinder transparent decision-making. The Cynefin Framework (Snowden, HBR 1999) classifies such challenges, distinguishing between ordered and disordered situations. Our approach identified contradictions within long-term care facilities, utilizing system modeling to visualize the interactions between internal and external factors.
Recommendations
The analysis led to 13 strategic directions categorized into three groups:
- Educating the Young – Addressing reckless transmission among youth ignoring public health directives.
- Building Public Trust – Ensuring reliable messaging from health officials to counter misinformation.
- Making Best Practices Common Practice – Systematic strategies for personal and community protection.
By applying disruptive innovation within a structured framework, this study demonstrates how AI can accelerate crisis response, enhance clarity in complex systems, and improve decision-making outcomes.
Finding Important Emails in the Inbox
The ESAM® innovation platform is designed to solve complex problems, particularly contradictions, where solving one issue creates another. Using insights from two million inventive patents, ESAM® applies structured problem-solving to highlight contradictions and generate solutions.
Applying ESAM® to Email Management
A common challenge is email overload, where essential messages are lost among junk and non-priority emails. ESAM® can help by analyzing the problem structure, mapping contradictions, and guiding users to effective filtering strategies.
Primary Objective
- Eliminate or reduce unwanted emails to prioritize essential messages, improving efficiency.
Challenges Identified
- Difficulty distinguishing between essential and non-essential emails.
- Uncertainty about blocking unwanted emails effectively.
Proposed Solution Using ESAM®
- Prioritize emails based on relevance and importance.
- Develop filtering techniques that reduce junk emails while retaining valuable ones.
Additional Strategies to Reduce Email Overload
Blocking Junk Emails
- Use spam filters provided by your email provider.
- Unsubscribe from unwanted mailing lists.
- Create a separate email for purchases and subscriptions.
- Set up email filtering rules based on keywords or sender.
- Report spam emails to improve automated filtering.
- Use third-party spam blockers for additional protection.
Reducing Incoming Emails
- Regularly unsubscribe from unnecessary newsletters.
- Use email management tools like Unroll.me or SaneBox.
- Set up automatic sorting rules to prioritize messages.
- Encourage alternative communication for non-urgent matters.
- Limit email checking frequency to reduce distractions.
By integrating ESAM® and AI-driven solutions, this approach streamlines inbox management, reduces clutter, and ensures important messages are easily accessible.