Generative AI-Augmented Human Judgment: A Task-Technology Fit Perspective

This study examines how generative artificial intelligence can augment human judgment in assurance of learning assessments within business education, using the task-technology fit framework as a guiding lens.A case study in a college of business -where the Management Information Systems program served as a central unit in the assurance of learning cycle -compared generative artificial intelligence-driven evaluations of student writing with traditional faculty assessments.The results demonstrate that when mediated by human-based prompt engineering and moderated by human oversight, generative artificial intelligence markedly improves assessment efficiency and scoring consistency while providing more indepth feedback without compromising evaluation accuracy.These findings indicate that generative artificial intelligence is most effective as a complement to rather than a replacement for human evaluators.The study extends task-technology fit theory to generative artificial intelligence-driven educational assessment and introduces a human-integrated, generative artificial intelligence-augmented theoretical model for assurance of learning assessments.In this model, human expertise acts as an iterative mediator (via prompt engineering) to strengthen task-technology alignment, while human oversight serves as a moderator ensuring contextual fidelity and output quality.Beyond its theoretical contribution, the study highlights practical implications for information systems educators and curriculum designers.