Mentorship Interest Form
I created a mentorship interest form that captures prospective participants' interests, personality, communication styles, and the specific skills they would like to share or develop. Collecting this comprehensive data ensures that every match is intentional, leading to stronger, more supportive, and more productive partnerships. Organizations can make a copy of the form and then edit it to fit their needs and priorities.You can view the form by selecting the link below.
Automated Matching
I developed an AI-assisted tool to help program leaders create purposeful, participant-centered matches. Drawing on data from interest forms, it scores potential pairings on a 0–100 scale and generates the most efficient set of non-repeating matches. It can also analyze hand-selected pairs, highlighting likely strengths, challenges, and strategies to address potential issues—providing a clear, data-informed starting point. The program coordinator uses this output to identify a working list of pairings, which then moves forward for supervisor feedback and possible adjustment. Because each organization has unique priorities, I also included instructions for building a customized version of the assistant. You can try the tool using the link below; the information button leads to the customization guide.
Supervisor Input
Once the coordinator creates a working list of pairings, they share a brief rationale for each match with the participant’s supervisor (for example: “Alex and Priya are a strong match because Alex wants to strengthen facilitation skills, and Priya listed this as an area she can mentor in. Both prefer collaborative problem-solving and bring complementary departmental perspectives.”). Supervisors then provide insight about their own participant—highlighting strengths, accessibility or scheduling needs, and any fit considerations the survey may not capture. To support this step, I developed guidance with sample questions, strategies for mitigating bias, and options for gathering additional input if a supervisor cannot provide full context. This can be viewed at the link below. This ensures the process remains balanced, equitable, and well-informed.
Final Pairing Decisions
In the last step, the program coordinator—together with liaisons when possible—reviews all the gathered input to confirm the matches. This decision-making process combines AI-generated suggestions, supervisor insights, participant perspectives, and any additional context collected along the way. If feedback indicates that a proposed pairing may not be the best fit, the coordinator can revisit the AI tool to generate new suggestions. In some cases, this step may also surface that a participant is not currently well-positioned for the mentorship program, allowing coordinators to make thoughtful adjustments while keeping the process fair, supportive, and participant-centered.
Partner Matching System
Amy Houston
Created on August 16, 2025
Process for making final mentorship pair selections.
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Transcript
Mentorship Interest Form
I created a mentorship interest form that captures prospective participants' interests, personality, communication styles, and the specific skills they would like to share or develop. Collecting this comprehensive data ensures that every match is intentional, leading to stronger, more supportive, and more productive partnerships. Organizations can make a copy of the form and then edit it to fit their needs and priorities.You can view the form by selecting the link below.
Automated Matching
I developed an AI-assisted tool to help program leaders create purposeful, participant-centered matches. Drawing on data from interest forms, it scores potential pairings on a 0–100 scale and generates the most efficient set of non-repeating matches. It can also analyze hand-selected pairs, highlighting likely strengths, challenges, and strategies to address potential issues—providing a clear, data-informed starting point. The program coordinator uses this output to identify a working list of pairings, which then moves forward for supervisor feedback and possible adjustment. Because each organization has unique priorities, I also included instructions for building a customized version of the assistant. You can try the tool using the link below; the information button leads to the customization guide.
Supervisor Input
Once the coordinator creates a working list of pairings, they share a brief rationale for each match with the participant’s supervisor (for example: “Alex and Priya are a strong match because Alex wants to strengthen facilitation skills, and Priya listed this as an area she can mentor in. Both prefer collaborative problem-solving and bring complementary departmental perspectives.”). Supervisors then provide insight about their own participant—highlighting strengths, accessibility or scheduling needs, and any fit considerations the survey may not capture. To support this step, I developed guidance with sample questions, strategies for mitigating bias, and options for gathering additional input if a supervisor cannot provide full context. This can be viewed at the link below. This ensures the process remains balanced, equitable, and well-informed.
Final Pairing Decisions
In the last step, the program coordinator—together with liaisons when possible—reviews all the gathered input to confirm the matches. This decision-making process combines AI-generated suggestions, supervisor insights, participant perspectives, and any additional context collected along the way. If feedback indicates that a proposed pairing may not be the best fit, the coordinator can revisit the AI tool to generate new suggestions. In some cases, this step may also surface that a participant is not currently well-positioned for the mentorship program, allowing coordinators to make thoughtful adjustments while keeping the process fair, supportive, and participant-centered.