Task–Technology Fit Leads to Conflict: The Double-Edged-Sword Effect of Generative Artificial Intelligence on Scientific Creative Performance in Humanities and Social Sciences Research
Why this work is in the frame
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Bibliographic record
Abstract
This study examines the double-edged sword effect of task-technology fit (TTF) on scientific creative performance in the humanities and social sciences, utilizing a mixed-methods approach. Analyzing data from 405 Chinese HSS scholars through structural equation modeling and conducting thematic analysis with 12 in-depth interviews revealed that TTF generates a paradox by enhancing AI literacy while increasing AI dependence. AI literacy promotes augmentation interactions, boosting scientific creativity, whereas dependence leads to automation patterns that hinder it. Demographic variations show female and senior scholars exhibit higher levels of dependence, with AI usage frequency demonstrating a non-linear relationship with dependence. Qualitative insights highlight distinct methodological orientations toward GAI among subdisciplines, raising concerns about ethical implications, dependency, and academic inequality. This research challenges the assumption that technology-task fit always yields positive outcomes and introduces the “AI Empowerment-Inhibition Paradox” as a framework for understanding these dynamics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it