Generative AI-Augmented Human Judgment: A Task-Technology Fit Perspective
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| 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 |
| 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. |
|---|---|
| ISSN: | 1055-3096 2574-3872 |
| DOI: | 10.62273/STSP3767 |
