Custodes Futurifor the advancement of humanity

Technological factor, barrier 4 of 7

Technology without supporting systems

Providing devices does not by itself improve learning, while technology designed around how children learn can work well.

Evidence

  • One Laptop per Child, Peru. A program in 318 rural schools raised computers per student from 0.12 to 1.18 but produced no evidence of effects on math or language test scores after 15 months (Cristia et al., 2017).
  • Long-term follow-up. A later study found no significant effects on academic performance, school completion, or university enrollment. Computer skills improved, but teachers' digital skills did not, and classroom computer use was low (Cueto et al., 2025).
  • Mindspark, India. Personalized, technology-aided after-school instruction raised scores by 0.37 standard deviations in math and 0.23 in Hindi after 4.5 months. It adjusted to each student's actual level, like Teaching at the Right Level, but is a single trial in one city (rated C for general claims) (Muralidharan, Singh, and Ganimian, 2019).

Why it matters: Technology helps when it adapts to learners and is supported by teachers. Hardware alone rarely changes outcomes.

Proposed and experimental methods

Methods that are proposed, under trial, approved in some places, or tried and then failed. Each shows a stage label and an evidence rating. A stage label shows how far a method has progressed, not whether it works. The stage labels are explained on the technological factor page.

  • Adaptive learning software with instructor support (Early trial, B). This builds on the AI tutor entries in "Fixed beliefs about ability" in the psychological factors. In Delhi, middle-school students who won a lottery for an after-school center combining personalized software (Mindspark) with group instruction gained 0.37 standard deviations in math and 0.23 in Hindi in about 4.5 months, with the largest relative gains for weaker students (Muralidharan, Singh, and Ganimian, 2019). A review of randomized and regression discontinuity studies concluded that programs expanding access to computers raised computer use and skills with "more mixed" results for achievement, while computer-assisted learning showed "enormous promise," especially in mathematics (Escueta et al., 2017).
  • Not shown to help: adding e-readers or tablets to a structured reading program (Large trial, B). In a randomized trial within Kenya's Primary Math and Reading program, e-readers for students and tablets for teachers did "not improve literacy outcomes significantly more than the base non-ICT instructional program," which used tablets only for instructional coaches, and the authors concluded that "cost considerations should be paramount" (Piper et al., 2016).
  • Failed: improved cookstoves without follow-up support (Failed or reversed, B). A lab-validated stove was tested in a large randomized trial in India with four years of follow-up. Smoke inhalation fell at first but the effect disappeared by year two, and there were no changes in health or greenhouse gas emissions, because households used the stoves irregularly, did not maintain them, and used them less over time (Hanna, Duflo, and Greenstone, 2016).
  • Failed: One Laptop per Child (Failed or reversed, B). The program gave each child a low-cost laptop. In a randomized evaluation in 318 rural Peruvian primary schools, computers per student rose from 0.12 to 1.18 and children used computers much more, but after 15 months there was no effect on math or language scores and only inconclusive evidence of gains in general cognitive skills (Cristia et al., 2017). In a separate trial in Lima with about 1,000 laptops for home use, children became more proficient with the laptops, teachers reported lower academic effort, and there was no measurable effect on achievement or cognitive skills (Beuermann et al., 2015).

Sources cited on this page

  1. Cristia, J., Ibarrarán, P., Cueto, S., Santiago, A., & Severín, E. (2017). Technology and child development: evidence from the One Laptop per Child program. American Economic Journal: Applied Economics, 9(3), 295-320. AEA A Strong
  2. Cueto, S., Beuermann, D. W., Cristia, J., Malamud, O., & Pardo, F. (2025). Laptops in the long run: evidence from the One Laptop per Child program in rural Peru. Journal of Public Economics, 252, 105538. DOI A Strong
  3. Muralidharan, K., Singh, A., & Ganimian, A. J. (2019). Disrupting education? Experimental evidence on technology-aided instruction in India. American Economic Review, 109(4), 1426-1460. AEA C Limited: B for the students studied
  4. Beuermann, D. W., Cristia, J., Cueto, S., Malamud, O., & Cruz-Aguayo, Y. (2015). One Laptop per Child at home: Short-term impacts from a randomized experiment in Peru. American Economic Journal: Applied Economics, 7(2), 53-80. link B Moderate
  5. Escueta, M., Quan, V., Nickow, A. J., & Oreopoulos, P. (2017). Education technology: An evidence-based review (NBER Working Paper No. 23744). National Bureau of Economic Research. link B Moderate
  6. Hanna, R., Duflo, E., & Greenstone, M. (2016). Up in smoke: The influence of household behavior on the long-run impact of improved cooking stoves. American Economic Journal: Economic Policy, 8(1), 80-114. link B Moderate
  7. Piper, B., Zuilkowski, S. S., Kwayumba, D., & Strigel, C. (2016). Does technology improve reading outcomes? Comparing the effectiveness and cost-effectiveness of ICT interventions for early grade reading in Kenya. International Journal of Educational Development, 49, 204-214. link B Moderate

Every source for this factor is listed on the technological factor page.