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

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Bibliographic Details
Title: Generative AI-Augmented Human Judgment: A Task-Technology Fit Perspective
Language: English
Authors: John Xavier Volker, Al Tilooby
Source: Journal of Information Systems Education. 2026 37(1):151-166.
Availability: Journal of Information Systems Education. e-mail: editor@jise.org; Web site: http://www.jise.org
Peer Reviewed: Y
Page Count: 16
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Business Schools, Student Evaluation, Undergraduate Students, Graduate Students, Technology Uses in Education, Evaluation Methods, Business Education, College Faculty, Writing Evaluation
DOI: 10.62273/STSP3767
ISSN: 1055-3096
2574-3872
Abstract: 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 in-depth 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.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1507570
Database: ERIC
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  Data: 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 in-depth 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.
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