The Science Behind: MentorPRO Academy
Jun 06, 2026
A Randomized Trial of A Course That Teaches Students How to Build the Relationships That Open Doors
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The Science Behind: Check-In
Jun 06, 2026
Check-Ins: A Simple Weekly Question That Turns Into Timely Support
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The Science Behind: Flash Mentoring
Jun 06, 2026
Flash Mentoring: A New Model for Reaching Mentors Who Can Open Doors
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The Science Behind: MatchPRO
Jun 06, 2026
Making the Right Match in MentorPRO Makes a Real Difference and Leads to Relationships That Last 3X Longer
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The Science Behind: Goal Setting
Jun 06, 2026
Setting Goals With MentorPRO Pays Off in Grades and Well-Being
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The Science Behind: MentorPRO
Jun 06, 2026
Research shows that students Who Use MentorPRO Earn Higher Grades and Feel More Connected
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The Science Behind: Messaging
Jun 06, 2026
The act of sending a message to your peer mentor may seem small, but research shows it matters.
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What the World Can Teach Us About Mentoring in Schools
Jun 02, 2026
A new comparative study in Inovasi Kurikulum, an Indonesian peer-reviewed open-access journal, offers one of the most comprehensive looks to date at how ten high-performing countries structure student mentoring in secondary schools.
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MentorPRO Academy: Evidence-Based Mentoring Training and Workforce Certifications
May 15, 2026
Most mentoring programs want to do the right thing. They recruit caring adults, make thoughtful matches, and genuinely believe that a positive relationship will make a difference. And sometimes it will. But three decades of research tell a more complicated story.
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Mentoring Program Surveys: Collect, Customize, and Report With Confidence
May 11, 2026
Think about the last time you rated an Uber driver, confirmed an Instacart delivery, or gave feedback on an online order. You did not receive a separate email three days later asking you to fill out a survey.
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Why MentorPRO Chose Human-at-the-Helm AI — and Why It Matters for Students
May 07, 2026
There is a version of AI-enhanced student support that is spreading rapidly across higher education right now. It looks like this: a student types a question into a chat window, a large language model generates a response, and a human reviewer spot-checks a small fraction of those exchanges after the fact.
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