Can GPT-4 Think Computationally about Digital Archival Tasks? – Part 2

This study examines the computational problem-solving capabilities of GPT-4, focusing on its knowledge of machine learning, email categorization, and computational problem solving, alongside its proficiency in Python programming, computational abstraction, and program debugging. The aim of these investigations is to evaluate whether the capabilities of Large Language Models (LLMs), as demonstrated by GPT-4, can support Master of Library and Information Science (MLIS), graduate students in developing computational thinking skills relevant to digital archival tasks.