Artificial intelligence in academic publishing and the faculty tradeoff between productivity benefits and academic integrity concerns
Résumé
Abstract This study examines how university faculty perceive and navigate the tradeoff between AI-enabled research productivity and the maintenance of academic integrity, a central challenge for sustainable scholarly ecosystems. Using an explanatory sequential mixed-methods design, the study surveyed 478 faculty members across six universities in Upper Egypt and then used 118 individual follow-up interviews to contextualize the quantitative patterns. Descriptively, respondents reported moderate integrity concerns (M = 3.81 on a 1–5 scale) alongside neutral-to-slight endorsement of AI productivity benefits (M = 3.06), indicating that integrity concerns remain salient amid emerging instrumental use. MANOVA revealed small but statistically significant group differences by discipline (eta_p squared = 0.023) and academic rank (eta_p squared = 0.031): early-career and STEM faculty reported comparatively higher AI adoption and stronger productivity orientations, whereas senior and humanities/social-science faculty reported comparatively higher integrity concerns. Theory-guided hierarchical regression showed that institutional context was positively associated with perceived productivity benefits after accounting for discipline, career stage, and AI-use frequency, while AI-use frequency was negatively associated with integrity-risk perceptions in the corresponding associational model. Because AI Adoption & Usage and Productivity Perception were very highly correlated ( r = .914), the results are interpreted as construct-proximate co-variation rather than causal evidence. Harman’s single-factor check did not indicate a single dominant method factor at item level, but common-method inflation remains a limitation. Qualitative findings show that faculty managed this tradeoff through principled resistance, pressure-driven adoption, pragmatic task segmentation, innovation-oriented experimentation, active risk management, and discipline-specific boundary setting. The study supports explicit AI-literacy training, disclosure guidance, and verification frameworks for responsible AI integration in academic publishing.
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