A Data Science Approach to Measuring Cognitive Offloading and Short-Term Independent Problem Solving in AI-Assisted Tasks: A Two-Session Experimental Study in Kampala, Uganda
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Date
2026-09-05
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Uganda Christian University, Mukono
Abstract
This study investigates the impact of Artificial Intelligence (AI) assistance on human cognitive offloading and short-term independent problem-solving performance. Utilizing a two-session experimental study with a mixed-methods factorial design conducted in Kampala, Uganda, the research evaluates how cognitive reliance on LLMs and automated tools influences critical thinking, problem retention and subsequent unassisted execution. To systematically capture these behavioral dynamics, original data-driven metrics were established, including the Cognitive Offloading Index (COI) and the Human Engagement Score (HES). The findings provide empirical insights into balancing algorithmic support with independent skill retention, offering practical guidelines for human-computer interaction frameworks, educational policy and the deliberate design of AI system guardrails
Description
A Data Science Approach to Measuring Cognitive Offloading and Short-Term Independent Problem Solving in AI-Assisted Tasks: A Two-Session Experimental Study in Kampala, Uganda is a Master of Science thesis in Data Science and Analytics, completed in 2026 within the Department of Computing and Technology at Uganda Christian University. This research investigates human-AI interaction, empirical data analytics, and cognitive psychology. It systematically examines how the integration of generative Artificial Intelligence specifically Large Language Models (LLMs) and automated computational tools, alters human cognitive habits, problem-solving strategies and short-term knowledge retention. As artificial intelligence systems become increasingly embedded in academic, technical, and professional workflows, human operators frequently delegate complex cognitive tasks to automated assistants. This process known as cognitive offloading reduces immediate working memory load and accelerates execution. However, excessive reliance on automated tools introduces potential trade-offs, including reduced depth of engagement, diminished critical thinking, and reduced independent problem-solving ability when AI tools are removed. The primary objective of this thesis is to quantify these dynamics through a rigorous data science framework. By conducting a controlled experimental study in Kampala, Uganda, the research empirical measures the precise boundary where AI assistance shifts from a supportive scaffold to a mechanism for cognitive atrophy.
Keywords
Cognitive Offloading, Human-AI Interaction, Artificial Intelligence in Education, Problem Solving, Human Engagement, Data Analytics, Factorial Experimental Design, Kampala, Uganda
Citation
Wambede, R. (2026). A data science approach to measuring cognitive offloading and short-term independent problem solving in AI-assisted tasks: A two-session experimental study in Kampala, Uganda [Master's thesis, Uganda Christian University].