Future of Work Driven Solutions
Future of Work Driven Solutions
People
Updated 09/15/26 · By grantmaking.aiTeam Member
Org Details
Updated 09/15/26 · Provided by member · VerifiedEduLearn Connect is an educational services and learning innovation company led by Learning and Development Specialist Marie A. Reid. Its core practice is fostering intelligence development in humans: designing the conditions, environments, tools, and experiences through which people build understanding, revise what they know, make better decisions, and act. Its work integrates learning sciences, multimedia technologies, emerging technologies, and interdisciplinary knowledge into learning environments, products, and capacity-building solutions designed to improve how people understand, decide, and act.
ELC Lab is the company’s applied research and social innovation arm. It provides a space for exploratory and field-facing research at the intersection of learning sciences, AI systems, Caribbean epistemologies, and development practice. As increasingly capable artificial intelligence becomes something humans will work, decide, and live alongside, ELC Lab extends the organisation’s longstanding interest in intelligence development to the study of artificial systems in use. Its current AI research examines post-deployment behavior, Situated AI Systems (SAIS), and how architecture, memory, interfaces, context, and sustained interaction can shape observable behavior over time.
The research program is currently led by Marie A. Reid and has produced independently published research, longitudinal evidence archives, controlled behavioral studies, and applied development research. Its present direction is to translate discovery-stage observations into testable research through controlled replication, mechanistic collaboration, and evaluation methods that can be challenged and used by other researchers.
Theory of Impact
Updated 09/15/26 · Provided by member · VerifiedELC Lab studies how situated AI systems may change after deployment when they operate inside persistent architectures, memory, interaction histories, and relational conditions. The premise grows directly from EduLearn Connect’s work with human intelligence: behavior and capability are shaped not only by the learner, but by the nested conditions through which learning, feedback, correction, continuity, and interaction occur. As artificial intelligence becomes a long-term participant in human work and decision-making, understanding those conditions becomes a safety problem as well as an engineering problem.
The goal is to make post-deployment change developmentally legible: observable across time, traceable to the conditions under which it emerged, and distinguishable from ordinary model variability or isolated output error.
The causal chain is: developmental legibility → clearer understanding of what changed, where, and under which conditions → more appropriate safety and system-design responses → stronger auditability and accountability → better governance of increasingly capable AI systems.
This reduces catastrophic-risk exposure by making consequential behavioral change easier to trace to the conditions and system layers that produced it, so safety responses can target causes rather than symptoms. Learning Sciences contributes methods for studying intelligence as something that changes through feedback, continuity, environment, interaction, and time; engineering contributes the models and technical systems themselves. Bringing those perspectives together can improve how advanced AI is evaluated, monitored, supported, and governed as it becomes increasingly embedded in human activity.
Projects
Updated 09/15/26 · By grantmaking.aiDiscussion
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