Aug 2026· Vifada Management and Social Sciences· 0 citations
TL;DR
The findings support generationally differentiated HR interventions for strengthening human–AI collaboration in higher education and contribute to SDGs 4, 8, and 9.
Abstract
Purpose: This study examines the simultaneous effects of digital skills, growth mindset, and organizational support on human–AI collaboration readiness among multigenerational academic workers, with generational cohort as a moderating variable.
Research Method: A sequential explanatory mixed-method design was employed. Quantitative data were collected from 280 academic workers at a public university in Indonesia using stratified random sampling and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The quantitative findings were further explored through semi-structured interviews with 12 key informants representing Generation X, Millennials, and Generation Z.
Results and Discussion: Organizational support emerged as the strongest and most consistent predictor of human–AI collaboration readiness across generations (β = 0.384; p < 0.001). Generational cohort significantly moderated the effects of digital skills (β = 0.110; p = 0.041) and growth mindset (β = 0.154; p = 0.004), with stronger effects among Generation Z workers. This pattern is identified as the “Generation Z Acceleration Effect.” The model explained 62.4% of the variance in collaboration readiness.
Implications: The findings support generationally differentiated HR interventions for strengthening human–AI collaboration in higher education and contribute to SDGs 4, 8, and 9.
Originality: This study integrates individual capabilities, psychological orientation, organizational support, and generational differences into a unified model of human–AI collaboration readiness and introduces the Generation Z Acceleration Effect.
It is argued that both AI and bullshitters are untrustworthy informants, and for similar reasons, it is natural to describe AI’s informational outputs as bullshit, as it signals their distinctive kind of epistemic deficiencies, which they share with bullshit.
In light of the pervasive methodological limitations identified, including high analytic risk of bias, absence of external validation, and lack of model interpretability, claims of ML superiority over CHA2DS2-VASc must be interpreted with caution.
Md. Mohaimenul Islam, Arinzechukwu Nkemdirim Okere· Int. J. Medical Informatics· 0 citations
This study presents three advanced machine learning models: the evolutionary Gaussian process inference model, the artificial satellite search algorithm–moment balance machine (ASSA-MBM), and the Operation Rain Forest (ORF), which are designed to predict the maximum reinforcement load in geosynthetic-reinforced soil structures. These models were developed to enhance both predictive accuracy and model interpretability by incorporating state-of-the-art optimization algorithms and explainable machine learning frameworks. A comprehensive evaluation was conducted using 10-fold cross-validation, and the proposed models were benchmarked against previously developed AI models from literature, as well as traditional and semiempirical approaches such as Rankine, Coulomb, and
K
-stiffness. Among the proposed models, ASSA-MBM consistently achieved the best performance, recording the lowest testing root mean squared error (0.617), the highest correlation coefficient (
R
=
0.918
), and the highest reference index (
RI
=
0.951
). Additionally, the ORF model offers transparency by generating mathematical regression equations, which are crucial in geotechnical engineering.
Min-Yuan Cheng, Akhmad F. K. Khitam, Jia-Wang Liou· Journal of computing in civi...· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.