Research and innovation grants

People Who Help Other People Learn (PWHOPL)

The Stanford Accelerator for Learning has funded 11 research projects that foster new understandings of how to use technology for rapid capability development to support PWHOPL.

Two people in professional dress high five, sitting at a table full of papers and a laptop.
Photo: Unsplash

Overview

PWHOPL (pronounced PWAH-puhl) is an acronym for People Who Help Other People Learn, and includes, but is not limited to, mentors, managers, section leaders, attending physicians, community health workers, tutors, early childhood education workforce, coaches, classroom paraprofessionals, and more – anyone who teaches but has not had the benefit of the thousands of hours of training that professional teachers receive.

The Stanford Accelerator for Learning has funded research that will lead to new understandings of how to optimize rapid capability development and foster ways to use technology to support PWHOPL to be more effective at helping other people learn. As part of the Accelerator’s Adult and Workforce Learning initiative, we funded proposals for projects and learning technologies that support adult learners who have teaching responsibilities but often receive minimal formal training, if any.

In most cases, PWHOPL do not have the opportunity or time to learn how to teach well, and the costs of developing their teaching capability can be prohibitive. At the same time, PWHOPL are often steeped in the contexts and cultures where learning takes place, so they bring unique strengths. If we can find ways to support PWHOPL, there could be a tremendous multiplier effect in benefits to learners of all ages.

Selected proposals were sensitive to the highly contextualized nature of PWHOPL as well as the constraints on the time available for learning. Innovative uses of technology received priority as did proposals that included data collection and the eventual demonstration of impact.

Application

Applications are currently closed

2025 Awardees (Faculty)

Parents as PWHOPLs: Expanding Visions of Learning at Home Through Parent-to-Parent Narratives

Parents are critical learning partners for their children, but they are often unsupported in leveraging technology for meaningful home-based learning experiences. This project examines how parent-to-parent storytelling can position caregivers to advance enjoyable learning for and with their children. Researchers will curate a set of parent-submitted narratives about home learning moments. These stories will then be presented to caregiver participants to reflect upon and interact with, alongside one of two frameworks — a goals frame (what caregivers want to achieve) or a roles frame (what role the caregivers plays in the moment). This project will advance both conceptual frameworks and practical solutions by creating and testing a model of “parent-to-parent learning stories” in the service of equity.

Principal Investigator: Brigid Barron; Project Team: Sophie Chen, Caitlin Martin

Equipping School Staff to Address Youth Substance Use: Community-Based Participatory Research and Evaluation of Online Training

Substance use in adolescents is at an alarmingly high rate. School is one setting where at-risk adolescents regularly interact with adults who have the potential to offer support and mitigate harm, but schools are often ill-equipped to train staff to meet these needs. Stanford’s REACH lab, which creates evidence-based drug prevention programs, will collaborate with the Stanford Center for Health Education to develop an online training course as a resource to train the school staff, including teachers, who are responsible for educating students about the risks of substance abuse. This course, which will be based on community-centered participatory research with school staff, will be freely available on YouTube’s new Courses platform. This approach leverages the tremendous scaling potential of free online platforms to amplify evidence-based substance use prevention resources.

Principal Investigators: Charles Prober, Bonnie Halpern-Felsher, Jamie Johnston

Clinical Mind AI: Supporting Medical Instructors in Teaching Clinical Reasoning

Clinical reasoning is the most important skill for physicians, yet teaching this fundamental medical skill effectively and equitably remains challenging. The researchers have developed Clinical Mind AI, a platform that generates AI-simulated patients tailored to specific contexts and local learning outcomes. To refine the current beta version, the team aims to develop an AI-based dashboard providing precise insights about student performance to inform instructors’ decisions. This work addresses equity in medical education by enabling effective clinical reasoning instruction through scalable virtual solutions, particularly benefiting resource-limited settings where access to traditional medical simulations is challenging.

Principal Investigators: Thomas Caruso, Shima Salehi; Project Team: Marcos Rojas, Edward Chang, Henrik Markland, Asheen Rama, Chinat Yu

Parents and Children Together (PACT-US): Enhancing Parents’ Ability to Support Oral Language Skills in Children with Down Syndrome

Parents are their children’s first teachers, yet few evidence-based interventions exist to support parents of young children with Down syndrome (DS), who experience delayed oral language skills. This study evaluates how the Parents and Children Together (PACT) intervention enhances parents’ abilities to support expressive and receptive vocabulary, listening comprehension, grammar, and narrative language in children aged 3-6 with DS. Parents will first learn and practice skills in the context of PACT-US; once learned, further training will help parents generalize the strategies into other daily contexts (e.g., bath time). The learner-centered approach allows parents to individualize programming to their child’s strengths and needs.

Principal Investigators: Chris Lemons, Craig Heller; Project Team: Lakshmi Balasubramanian

Enhancing Math Tutoring Through Whiteboard Interaction Data

This project explores how digital whiteboard interactions can illuminate and improve the quality of math tutoring, particularly when delivered by novice instructors. Aligned with the goals of PWHOPL, the work investigates whether whiteboard data—beyond verbal language—offers unique insights into instructional effectiveness and student learning.

Principal Investigators: Judith Fan, Dora Demszky

Using Generative AI to Support University Section Leader Coaching, Rehearsal, and Community

Professional development (PD) for instructors is a key method for advancing equitable and effective education. However, many university instructors do not receive the formal training that a teacher preparation program provides. To address this challenge, the research team will collaborate with members of the STEM teaching community to co-design AI-supported PD. This project will focus in particular on university computer science section leaders to explore their instructional needs and how they can engage effectively with AI tools through community and coaching. Informed by instructor surveys and interviews, the researchers will gain a sense of instructors’ PD needs, preferences, and constraints before building prototypes of AI agents that simulate instructional coaches, students, other teachers, and facilitators. In examining how section leaders engage with these agents in simulated PD, the project will identify needs, challenges, and opportunities that arise for university instructors in AI agent-based PD.

Principal Investigators: John Mitchell, Jennifer Osuna

2025 Awardees (Students/Postdocs/Staff)

Nail Salon as a Learning Space: Improving Community Awareness of Melanoma by Equipping Nail Technicians with Teaching Skills

Nail salons are a site of economic and cultural activity for the largely Vietnamese immigrant and refugee female workforce. These spaces can also be a site of learning and community health promotion. Through community-engaged research, the team will partner with racial and ethnic minority nail technicians in the San Francisco Bay Area to co-design a free, open-access educational tool that equips technicians with pedagogical skills to effectively teach clients about melanoma prevention and detection. By equipping nail technicians with the relevant teaching skills, this project can amplify the impact of existing efforts so that melanoma awareness doesn’t stop with the individual nail technicians but is passed on to the millions of clients they work with.

Project Lead: Charbel Khalil; Advisors: Susan Swetter, Patricia Rodriguez Espinosa, Rachel Mesia

Generative AI for Teaching Neurodivergent Students in Bangladesh

This project explores how Computer-Supported Collaborative Learning augmented by generative AI can improve teaching strategies for trainers working with neurodiverse students in Bangladesh. The researchers will implement the study at InclusionX “Joy of Computing,” a computer training program in Dhaka serving students aged 6-30 with autism, Down syndrome, or cerebral palsy, many of whom are non-verbal or have limited literacy skills. By integrating no-code generative AI tools for image, video, text, and AR filter generation, the research team aims to empower college and high school student trainers to create personalized content that brings students’ ideas to life through joint media engagement.

Project Lead: Labib Rahman; Advisor: Roy Pea

Enhancing Inclusive Instruction by Offering Special Education Workers Training and Access to Real-Time Student Insights

15-20% of K-12 students have special needs, but 98% of school districts report shortages in special education-related service personnel. TeachAssist supports special education workers in K-12 U.S. school districts by providing access to valuable insights on student progress and needs. These PWHOPL will be able to see real-time progress monitoring of each IEP goal, information on student strengths and areas of focus, AI-generated worksheets and assessments aligned with Common Core State Standards, and instructional guidance based on student performance and inclusive pedagogical frameworks. This comprehensive approach ensures special education workers have up-to-date understanding of their students’ needs and IEP progress, while also receiving training and access to personalized, compliant materials.

Project Lead: Mara Steiu; Project Team: Sagar Manchanda, Kayleigh Miller, Anil Yildiz; Advisor: Jonathan Levav

Misinformation on the Frontline: Empowering Librarians to Teach Patrons Effectively About Misinformation

Concerns about misinformation have been tied to several pressing issues facing the United States. As central community and information centers, libraries are uniquely positioned to address this issue by teaching patrons digital literacy, a set of core skills to discern misinformation from true news. The researchers’ pilot study revealed that the core challenge library workers face in teaching digital literacy lies in navigating difficult conversations with resistant patrons. This proposed project integrates theories from interpersonal communication, political communication, and digital literacy and develops two conversational-based toolkits to support library workers in navigating difficult conversations with patrons effectively. The first one focuses on empathetic dialogue, while the second leverages AI chatbots to facilitate conversational dialogues.

Project Lead: Fangjing Tu, Sunny Liu; Project Team: Aysu Sarigul; Advisor: Ilana Shumsky

MentorMate: Elevate Hackathon Feedback

Hackathons are intensive events where participants develop projects within 24-72 hours. Existing platforms focus on showcasing projects without providing substantial feedback, leaving participants without valuable insights for improvement. This project aims to build a platform that helps judges give better feedback during hackathons by providing scaffolding using generative AI and delivering it to participants in a constructive and actionable format. This initiative addresses a significant gap in the hackathon experience, improving learning outcomes and supporting participants’ growth. The platform will facilitate the submission of project materials, including videos, demo links, and descriptions, and enable judges to log in and provide real-time comments and scores. Generative AI will help refine the feedback, ensuring it is clear, supportive, and socially and emotionally considerate.

Project Lead: Chinat Yu; Advisor: Christopher Piech