Ready to Learn

Teacher and students at a table working on an educational art project

The Ready to Learn program (RTL) offered through the Center for Urban Education is an after-school and summer math mentoring initiative that blends tutors and technology to create an engaging learning experience that connects math to the real-world. RTL is part of a broader research practice partnership with Carnegie Mellon University called Personalized Learning Squared (PL²) which seeks to address the opportunity gap for urbanized students through personal mentoring and tutoring with artificial intelligence learning software. The program is funded by the Chan Zuckerberg Initiative and the Heinz Endowments.

RTL is offered to Pittsburgh Public School students attending Pittsburgh Milliones University Preparatory, Westinghouse Academy, and Pittsburgh Science and Technology Academy middle schools. During the 2019-20 school year, more than 80 middle school students and 20 mentors participated in RTL.

Middle School Mentees

RTL is more than just math tutoring. Students who participate in RTL also benefit from the development of meaningful relationships with mentors, who are Pitt and CMU students, through the completion of interdisciplinary social justice projects that connect math to the real-world. 

Students who participate in RTL will: 

  • Gain confidence in their ability to approach and solve complex problems. 
  • Strengthen relationships with peers by working in small groups 
  • Develop research and digital literacy skills through the completion and presentation of projects 
  • Critically examine social justice problems impacting their community 

>> Register to participate in the program

Pitt Mentors

To effectively tutor and mentor middle school students, RTL mentors must complete seminar-training sessions to build five program competencies: 1) urban context, 2) teaching, tutoring and pedagogy, 3) mentoring and social support, 4) Research (action research), and 5) technology. 

Upon completion of this program, mentors will be able to: 

  • Demonstrate foundational knowledge of urban contexts. 
  • Critically examine the social and cultural contexts of urban education, including race and socio-economic class. 
  • Demonstrate knowledge and display behaviors of the professional aspects of tutoring and mentoring students including dispositions, reflective practice, and classroom observation. 
  • Demonstrate knowledge of how to provide pedagogical supports in urban schools with and culturally diverse students.

Why Focus on Math

High levels of mathematical competence are an increasingly central requirement for high-paying careers in the 21st century (Rose & Betts, 2004). Racial and economic learning gaps are preventing millions of American students from realizing their potential, and this perpetuates inequalities of income and opportunity across generations (Autor, 2014). While these are longstanding problems, researchers have struggled to identify effective solutions.  Fortunately, advances in computer-aided learning may provide a method of substantially lowering the cost of personalized tutoring, while maintaining the magnitude of the learning gains. Research on AI-driven computer-based tutoring has shown that computer tutors can substantially accelerate student learning, especially in mathematics (Koedinger et al, 1997). In one recent large-scale randomized control trial, this technology was shown to double the rate of math learning (Pane et al., 2014).  However, implementation matters, which is why Ready To Learn blends human-mediated and computer-mediated tutoring along with mentorship to offer students with access to interdisciplinary math enrichment from near peer Pitt students. Although building math literacy is the main aim of the current program, students engage with other content areas, such as civics, English and computer science.

>> For more information on the Ready to Learn program, contact


Autor, D. (2014).  Skills, education, and the rise of earnings inequality among the “other 99 percent.” Science, 344(6186), 843-851.
Guryan, J., Christenson, S., Claessens, A., Engel, M., Lai, I., Ludwig, J., & Turner, M. C. (2017). The effect of mentoring on school attendance and academic outcomes: A randomized evaluation of the Check & Connect Program. Institute for Policy Research Working Paper Series, WP-16-18. Evanston, IL: Northwestern University. 
Hurd, N. M., Varner, F. A., & Rowley, S. J. (2013). Involved-vigilant parenting and socio-emotional well-being among Black youth: The moderating influence of natural
mentoring relationships. Journal of Youth & Adolescence, 42, 1583-1595. doi:10.1007/s10964-012-9819-y
Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (1995). Intelligent tutoring goes to school in the big city. In Proceedings of the 7th World Conference on Artificial Intelligence in Education (pp. 421-428). Charlottesville, VA: Association for the Advancement of Computing in Education.
Pane, J. F., Griffin, B., McCaffrey, D.F. & Karam, R. (2014).  Effectiveness of Cognitive Tutor Algebra I at Scale. Educational Evaluation and Policy Analysis, 36 (2), 127 - 144.
Rose, H., & Betts, J. (2004).  The effect of high school courses on earnings.  Review of Economics & Statistics 86(2), 497-513.