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Engineer Inclusion

Transform Apprenticeship Programs with an AI-driven DEIA Coach

Discover how our AI-powered Feedback Generator acts as your personal DEIA coach, providing tailored, actionable insights to elevate your registered apprenticeship programs. Instantly receive expert-like evaluations and recommendations that empower you to build more inclusive and effective educational environments.

Strengthening Apprenticeship Programs with Advanced AI

Apprenticeship programs are fundamental in preparing tomorrow’s workforce with necessary skills. To enhance the effectiveness and inclusivity of these programs, we are thrilled to introduce our new AI-powered Feedback Generator. This tool is designed to enrich program delivery by providing targeted insights and actionable recommendations, aligning closely with diversity, equity, inclusion, and accessibility (DEIA) principles.

Drive Apprenticeship Success: Your No-Cost AI Coach, Anytime, Anywhere.

Purpose Behind the Feedback Generator

In partnership with Jobs for the Future (JFF), we’ve developed this feedback tool to apply the JFF DEIA Framework in a practical, user-friendly format. It acts like a first-pass conversation with a subject matter expert on DEIA principles, providing a structured approach to enhancing apprenticeship programs. By aligning program designs more closely with these principles, we ensure that all participants benefit from a richer, more inclusive learning experience.

Jobs for the Future's Program Design Framework for Diversity, Equity, Inclusion, and Accessibility in Registered Apprenticeship

Key Features of the Feedback Generator

This tool combines detailed AI analysis with an intuitive interface, embodying the sophistication of a DEIA expert and the accessibility of a friendly advisor:

How to Use the Feedback Generator

Answer an Online Survey

In the survey, you'll answer 11 questions about your program design (see which questions below). It could take you 5 to 15 minutes, depending on how thorough your answers are. 

Complete Survey1

Check Your Email

In less than 5 minutes, you'll receive an email with an AI-generated analysis based on JFF's DEIA Framework. The email will include customized feedback to help improve your program based on DEIA principles, plus a summary of what you submitted. 

2

Implement Feedback

Make some positive changes and then regularly use the tool to monitor adjustments and continuously refine your approach to maximize educational outcomes.

3
Survey Questions

Prepare to submit your information by gathering your responses in as much detail as follows to as many of the following questions as possible. The more enriching your responses are, the better the feedback will be. 

Q1: Does your program have clear goals to include people from different backgrounds? What are they? How do you share these goals with everyone involved?

Q2: What steps have you taken to make sure everyone feels welcome and supported in your apprenticeship program?

Q3: Do your program leaders and staff come from a variety of backgrounds? How does this help your program?

Q4: How do you choose who gets to be a mentor or leader in your program? Do you look for different experiences and backgrounds?

Q5: How do you make sure the pay is fair for everyone in your apprenticeship? Do you adjust wages as apprentices learn more?

Q6: In what ways do apprentices get to share their thoughts or feedback about the program? How is this feedback used?

Q7: How do you tell people about your apprenticeship program? Do you use different ways to reach out to all kinds of young people?

Q8: Are there special supports in place to help everyone succeed in your program, no matter their background or needs?

Q9: Do your training materials reflect people from different cultures and backgrounds? How do you choose these materials?

Q10: How do you match apprentices with mentors? What makes a good mentor in your program?

Q11: How do you keep track of who joins and completes your apprenticeship? How do you use this information to make your program better?

Sample Data Sets

Here are three sets of sample data for the survey questions. These examples represent a range of scenarios, from highly effective and inclusive programs to those needing improvement. 

Sample Data Set 1: High Performance

  1. Clear Goals for Diversity
    • “Our program has clearly defined goals to increase diversity by recruiting 40% of our apprentices from underrepresented backgrounds this year. We communicate these goals through our monthly newsletters, team meetings, and on our internal website.”
  2. Culture of Belonging
    • “We’ve implemented a comprehensive onboarding process that includes diversity training and mentoring from diverse team leaders. We host quarterly workshops that focus on cultural competence and sensitivity.”
  3. Leadership and Staff Diversity
    • “Our leadership team includes individuals from various racial, ethnic, and gender backgrounds, which provides a wide range of perspectives that enhance our decision-making and program development.”
  4. Mentor and Leader Selection
    • “Mentors and leaders are chosen based on their experience, commitment to equity, and ability to foster an inclusive environment. We specifically look for individuals who have demonstrated an understanding of diverse cultural backgrounds.”
  5. Wage Equity
    • “We regularly review and adjust wages to ensure fairness based on skill level, industry standards, and cost of living. Pay increases are tied directly to skills assessments and performance reviews.”
  6. Apprentice Feedback
    • “Apprentices can provide feedback through bi-monthly surveys, suggestion boxes, and town hall meetings. This feedback directly influences program adjustments and improvements.”
  7. Recruitment Strategies
    • “We use a variety of recruitment methods, including social media campaigns, community outreach, and collaborations with local schools. We ensure our materials are accessible and resonate with diverse audiences.”
  8. Supports for Success
    • “Our program offers language support, flexible scheduling, and personalized learning plans to accommodate diverse needs and backgrounds, ensuring all apprentices can succeed.”
  9. Training Material Diversity
    • “We select training materials that include examples and case studies from various cultures. Materials are vetted by our diversity committee to ensure they are inclusive and representative.”
  10. Matching Mentors with Apprentices
    • “Mentors and apprentices are matched based on shared interests, career goals, and, when possible, cultural backgrounds. This ensures meaningful and supportive relationships.”
  11. Tracking Participation and Completion
    • “We use a digital tracking system to monitor who joins and completes our program. This data is analyzed to identify trends and areas for improvement.”

Sample Data Set 2: Average Performance

  1. Clear Goals for Diversity
    • “We aim to recruit from diverse backgrounds, but we haven’t set specific targets. We mention our diversity goals in recruitment brochures.”
  2. Culture of Belonging
    • “We organize an annual diversity day and have a non-discrimination policy, but we lack a structured program for ongoing support and inclusion.”
  3. Leadership and Staff Diversity
    • “Our staff somewhat reflects diversity, mainly in lower-level positions. We recognize the need to improve diversity in leadership roles.”
  4. Mentor and Leader Selection
    • “Leaders are usually chosen for their tenure and expertise, without specific consideration for diversity.”
  5. Wage Equity
    • “Wages are set at hiring and reviewed annually without a structured assessment of equity across different groups.”
  6. Apprentice Feedback
    • “Feedback is collected at the end of the apprenticeship through exit interviews, which sometimes inform policy changes.”
  7. Recruitment Strategies
    • “Our recruitment is primarily through job fairs and online postings. We haven’t utilized targeted strategies to reach diverse populations.”
  8. Supports for Success
    • “Support services are available but not widely advertised or tailored to individual needs.”
  9. Training Material Diversity
    • “Our materials are standard for the industry but do not specifically address cultural diversity.”
  10. Matching Mentors with Apprentices
    • “Mentorship pairings are based on availability rather than a strategic match, which could be more effective.”
  11. Tracking Participation and Completion
    • “We keep basic records of apprenticeship completions but do not analyze the data for diversity insights.”

Sample Data Set 3: Needs Improvement

  1. Clear Goals for Diversity
    • “We lack formal goals related to diversity and do not communicate consistently about the importance of diversity in our program.”
  2. Culture of Belonging
    • “There is minimal effort to address or promote a culture of belonging; most initiatives are ad hoc and not integrated into the program structure.”
  3. Leadership and Staff Diversity
    • “Our team lacks diversity, and this has been a limitation in understanding and addressing the needs of diverse apprentices.”
  4. Mentor and Leader Selection
    • “Selection is often ad hoc, without consideration for diversity or inclusiveness.”
  5. Wage Equity
    • “There is no formal process for assessing or ensuring wage equity. Pay reviews are infrequent and not standardized.”
  6. Apprentice Feedback
    • “We do not have a formal mechanism for collecting or acting on apprentice feedback.”
  7. Recruitment Strategies
    • “Recruitment efforts are sporadic and rely on traditional methods that do not effectively reach diverse groups.”
  8. Supports for Success
    • “There are no special supports in place; apprentices must navigate the program based on their own resources.”
  9. Training Material Diversity
    • “Training materials have not been updated to reflect diversity and are not reviewed for cultural sensitivity.”
  10. Matching Mentors with Apprentices
    • “There is no system in place for matching; it is typically based on who is available rather than who is most appropriate.”
  11. Tracking Participation and Completion
    • “We do not track who joins or completes the apprenticeship, limiting our ability to make data-driven improvements.”

Benefits of Using the Feedback Generator

Utilizing the Feedback Generator provides significant advantages:

We encourage all program designers, educators, and administrators involved in apprenticeship education to explore the capabilities of this innovative tool. 

Conclusion

Our AI-powered Feedback Generator is a pioneering tool designed to transform how apprenticeship programs integrate DEIA principles. By offering expert-like feedback instantly, this tool helps pave the way for more effective and inclusive educational practices. We look forward to seeing how it positively impacts your programs.

Feedback

We value your feedback! If you’d like to help us improve future iterations of this tool, please share your feedback here.

Acknowledgments

This project has been funded, wholly or partially, with Federal funds from the Department of Labor, Employment & Training Administration under contract number GS10F0094X to Jobs for the Future and subcontracted to and in partnership with Engineer Inclusion. The contents of the Feedback Generator Tool and the AI-generated response do not necessarily reflect the views or policies of the Department of Labor, Jobs for the Future, or Engineer Inclusion, nor does mention of trade names, commercial products, or organizations imply endorsement of the same by the U.S. Government. 

I want to acknowledge the contributions of Jobs for the Future’s Ginger Avila and Monique Sheen on this project, as well as all of the authors of JFF’s DEIA Framework

Behind the Creation of the AI-Powered Feedback Generator

Engaging deeply in this project allowed me to reconnect with my computer science roots, crafting a sophisticated tool that intersects technology with educational efficacy. Utilizing OpenAI’s developer tools, I programmed an AI assistant tailored specifically to JFF’s DEIA Framework for Registered Apprenticeships. This tool is designed to function like an expert DEIA coach, providing asset-based feedback and guidance without requiring direct human consultation.

To ensure accessibility and ease of use:

  • I set up an intuitive input process using Typeform, enabling straightforward data entry.
  • Using Zapier, I integrated various functionalities to ensure seamless operation from start to finish.
  • The feedback is instantly generated and beautifully formatted—thanks to HTML coding—offering a personalized touch to each piece of advice sent directly to users’ emails.

This AI coach simplifies the complex task of applying DEIA principles into daily program operations, making it an invaluable asset to the JFF team and potentially any team focused on improving educational outcomes through technology.

Explore the AI Tool Yourself

If you’re curious about the AI feedback generator and how it can enhance your apprenticeship program, I invite you to try the tool firsthand. You’ll find sample data sets above to give it a try!

Interested in Custom AI Solutions?

🟢 If you’re looking to build a custom AI-feedback generator or need a specialized tool that aligns with your operational framework, let’s connect! 🟢 I’m eager to help organizations save time and enhance their services through tailored AI solutions. Contact us to discuss how we can bring your ideas to life with cutting-edge technology.

Headshot of Dr. Meagan Pollock

Meagan Pollock, PhD

Dr. Meagan Pollock envisions a world where personal and social circumstances are not obstacles to achieving potential, and where kindness, inclusivity, and conservation prevail.

An international speaker, teacher, engineer, and equity leader, her mission is to provide services, tools, and resources that inspire awareness and initiate action.

As an engineer turned educator, Meagan Pollock is focused on engineering equity into education and the workforce.

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