This study explored artificial intelligence (AI)–human intelligence integration within knowledge ecosystems by analyzing how generative AI (GenAI) content aligned with, or differed from, human perceptions. A mixed methods approach was adopted, combining computational sentiment analysis with human evaluation. Three prominent large language models were prompted with identical questions across topics regarding politics, gender, and climate. The computational analysis quantified stances, sentiments, and emotions using natural language processing techniques. Simultaneously, human participants rated the AI outputs on perceived stance and emotion through a structured questionnaire. The findings confirmed a positivity bias with AI models. Human evaluations corroborated these trends. The study emphasized that AI-human integration was shown to be essential for building trustworthy, fair, and inclusive systems. The study concluded that computational tools were able to rapidly generate and assess content; however, they overlooked cultural cues, implicit biases, or emotional subtleties to which human evaluators were more sensitive.

Digital agriculture is a growing field where complex systems are utilized for computer vision applications to aid decision-making. This work provides a comprehensive comparison of the applications of Convolutional Neural Networks and Vision Transformers, in the agricultural sector, specifically focusing on image classification. An extensive evaluation and comparison of both modeling frameworks is conducted with 17 different deep learning models. Using five distinct agricultural datasets comprising various image classification tasks, this work highlights the strengths and limitations of each architectural approach. Specifically, transformer variants like the Swin Transformer Version 2 excel in accuracy and the ability to capture complex patterns due to their attention-based mechanisms. In contrast, models Residual Networks offer a balance between computational efficiency and performance, making them suitable for scenarios with limited computational resources. The findings highlight the importance of selecting an appropriate deep learning model based on specific agricultural tasks.

Predicting academic performance is vital in education, yet early identification of at-risk students and timely interventions remain challenging. The authors present a decision support system using a Bayesian network to dynamically estimate course failure risk and integrate predictive analytics with institutional knowledge management. The model, built with the Bayesian Information Criterion and domain expertise, handles incomplete data and enables continuous, near-real-time monitoring. Applied to data from 8,897 undergraduates at a Southeastern U.S. university, it achieves over 85% accuracy at semester start and over 90% by midterm. The system enables prompt, individualized interventions and enhances institutional knowledge flows, supporting informed decisions and organizational learning. This scalable framework strengthens student retention and academic outcomes, bridging predictive modeling and knowledge-based strategies in higher education.

Emotion detection in social media remains challenging, particularly in polarized public debates where emotional expression is often imbalanced across categories. Addressing this challenge, this study compares the performance of multiple computational approaches using a gold-standard dataset of tweets about an ongoing geopolitical conflict. The dataset reflects the authentic, skewed distribution of emotions observed in real-world online discourse. We evaluated lexicon-based methods, classical machine-learning classifiers, deep-learning architectures, transformer models in both fine-tuned and zero-shot configurations, and a zero-shot large language model to assess their effectiveness in capturing both frequent and less frequently expressed emotions. Across approaches, transformer models, especially those fine-tuned for contextual emotion recognition, demonstrated the strongest overall performance, with emotion-specific fine-tuning offering a particular advantage for detecting rare emotion categories. These findings emphasize the importance of evaluating emotion detection methods under realistic class imbalance and highlight both the comparative strengths and limitations of widely used modeling strategies in applied social media research. This study advances emotion analysis and computational social science by offering practical guidance for selecting appropriate emotion detection methods in complex, imbalanced social media contexts.

Large language models are increasingly used to read résumés and judge who advances in hiring, a task once reserved for people and now handed to systems whose reasoning is hard to inspect. Whether these models carry the demographic biases that have long shaped human hiring is therefore an urgent question, and the published evidence so far is mixed and difficult to interpret, partly because studies tend to test a single condition and rarely confirm that their measurement instrument can detect bias at all. This paper audits demographic bias in résumé evaluation across three current models, one of them open-weight, and it treats robustness as a central concern rather than seeking a single verdict. Each résumé is scored through a reference-anchored comparison task in which the model rates the candidate against a fixed neutral reference for the same occupation. Effect sizes are estimated as standardised mean differences under false-discovery control, the sensitivity of the instrument is tested with an embedded seniority control, and the null findings are corroborated by formal equivalence tests against a justified smallest effect size of interest and by mixed-effects models that account for the clustered structure of repeated evaluations. The audit pairs a positive control that confirms the models read genuine differences in candidate quality with a deliberate attempt to provoke bias by weakening candidates, relaxing the prompt, and adding culture-fit language of the kind used in real hiring. Across more than thirty thousand evaluations, gender and race effects prove negligible and remain so under every one of these conditions. The one systematic preference that emerges favours candidates who appear more experienced, and closer inspection shows that most of it is an artefact of how the résumés were built rather than a bias against age, leaving only a modest effect that surfaces when the prompt is casual. A separate and quieter pattern appears in the open-weight model, which reacts to a few explicit signals of minority status. The broader lesson is that fairness measured on a clean benchmark does not by itself guarantee fairness in deployment, because how a model is prompted can decide whether bias appears.