Course Overview

This course begins with an introductory module covering foundational concepts about a developmental evolutionary view and ends with a specialized intervention model. Participants will learn a structured, step-by-step research methodology to transform laboratory or small non-random sample findings into scalable, field-based proposals aimed at initial implementation in low- and middle-income countries (LMICs). The transition from controlled lab studies to diverse field environments presents challenges, which the course frames as opportunities to enhance research relevance and effectiveness. Participants will apply a five-step process to develop and refine developmental interventions, considering the political and logistical constraints specific to LMICs. They will also explore strategies to adapt and scale their research interests within these contexts. The final section of the course features the Integrative Social Intervention (INSI) Framework, a model that starts with family-centered goals, linking institutional activities (such as children’s centers) with primary caregiver-child interactions at home. This framework emphasizes the importance of addressing both proximal processes and surrounding systems to drive meaningful change. Affordability and scalability are core elements in integrative social interventions, illustrated in the course through specific tools, such as the CARE Booklet. This tool supports consistent application by assessing caregiver agreement and reliability and includes direct screening methods. The course compares the CARE Booklet Intervention (CBI) with a proven Dialogical Book-Sharing (DBS) intervention, with each component designed to keep intervention goals adaptable and maximize effectiveness. Finally, the course concludes with a project elaboration and review focused on integrating artificial intelligence tools (e.g., web scraping, pipelines, chatbots), guided by two key principles: (1) expanding from community samples to larger populations, and (2) prioritizing early intervention for optimal developmental outcomes.

Learning Outcomes

By the end of this course students should be able to:
(1) Select and compare previous well-defined Lab or empirical research result(s) in a specific topic with manifest variables to consider for intervention in LMIC.
(2) Apply a transformation framework using the Top-Down and Bottom-Up models in a wide-ranging replicable cycle for interventions in LMIC.
(3) Create a plan with an ideal structure and timeline with specific issues rated in LMIC considerations.
(4) Design a preliminary research-intervention plan including AI support with content, budget and funding perspective prepared.

Course Content

1. Evolutive perspective for development: from normative to individual development.

2. Why are interventions in LMIC different? Identifying well-defined Lab or empirical research of interest.
3. Top-Down and Bottom-Up models in a wide-ranging replicable cycle for interventions framework: The integrative social intervention model.
4. First plan: structure and timeline with specific issues in LMIC interventions.
5. Re-Plan: a preliminary research intervention.
6. AI supported Re-plan intervention: tools and incorporated efforts.

Instructional Method

Lecture; PowerPoint slides; Video Excerpts; Participants Q&A; short-search exercise; writing exercises.

Required Course Materials

 - Participants will need a desktop/laptop with licensed Microsoft Excel.
- Readings (*: optional; **: mandatory):
* Burger, K. (2010). How Does Early Childhood Care and Education Affect Cognitive Development? An International Review of the Effects of Early Interventions for Children from Different Social Backgrounds. Early Childhood Research Quarterly, 25(2), 140-165.
doi.org/10.1016/j.ecresq.2009.11.001
* Diener, E., Northcott, R., Zyphur, M. & West, S. (2022). Beyond experiments. Perspectives on Psychological Science, 17 (4), 1101-1119.
* Eckel, C., Priday, B. & Wilson, R. (2018). Charity begins at home: A lab-in-the-field experiment on charitable giving. Games, 9, 95-117.
** Cavallera, V., Tomlinson, M., Radner, J., Coetzee, B., Daelmans, B., Hughes, R., Dua, T. (2019). Scaling early child development: what are the barriers and enablers? Archives of disease in childhood, 104(Suppl 1), S43S50. doi:10.1136/archdischild-2018-315425
** Giraldo-Huertas, J. & Schafer, G. (2021). Agreement and Reliability of Parental Reports and Direct Screening of Developmental Outcomes in Toddlers at Risk. Frontiers in Psychology, 12:725146. https:// doi.org/10.3389/fpsyg.2021.725146
** Giraldo-Huertas, J. (2023). Parental developmental screening with CARE: A pilot remote assessment and intervention with vulnerable families in Colombia. Plos One, 18(6): e0287186. https://doi.org/10.1371/journal.pone.0287186
** Jeong, J., McCoy, D. C., & Fink, G. (2017). Pathways between paternal and maternal education, caregivers support for learning, and early child development in 44 low- and middle-income countries. Early Childhood Research Quarterly, 41, 136-148. doi.10.1016/j.ecresq.2017.07.001

Assessment

Students will work on group projects that will be assessed in conference-like presentations. The conceptual assessment is individual. Students will receive verbal feedback from the professor and the other participants in the course. Distribution weights for principal assessments are:

40% Pre-writing Project

20% Conceptual assessment exam

40% Final pre-submission of an AI supported intervention project