This study investigates public perceptions in Texas regarding two common calf disbudding methods used in dairy farming: caustic paste and hot iron. With growing consumer interest in humane animal treatment, understanding the preferences for these methods provides insights into public expectations of welfare in agricultural practices. The study utilized an online survey distributed to Texas residents via the Centiment research platform, assessing preferences, demographic influences, and the impact of scientific information on perceptions. Results, from 511 participants, indicated a stronger preference for caustic paste, especially among women and participants with higher education levels, while hot iron was favored more by men. Dietary habits, such as seafood and cheese consumption, also influenced preferences, with higher consumption linked to a preference for caustic paste. Viewing images of the disbudding techniques affected the willingness to purchase, consume, and serve beef, with hot iron prompting a greater negative response. The findings suggest that public knowledge and awareness can significantly impact perceptions surrounding animal welfare practices and that education could play a critical role in aligning consumer preferences with humane agricultural practices. These insights are valuable for the dairy industry, informing practices that are more aligned with public expectations and supporting the promotion of welfare-oriented disbudding methods.

This proof-of-concept study explores the innovative application of Large Language Models (LLMs) for qualitative analysis of feedback from an Extension program, addressing the challenge of efficiently analyzing qualitative data. The study juxtaposes traditional human-led qualitative analysis with Artificial Intelligence (AI)-driven techniques, revealing the complementary strengths of human insights and AI efficiency. It underscores the potential of LLMs to enhance qualitative analysis while recognizing the need for human oversight to ensure depth and context accuracy. This research contributes to the fields of program evaluation and data analysis, offering a new paradigm for integrating advanced AI tools in qualitative research.

This study was undertaken to observe the effects of Thankuni and grapes on hematological and biochemical parameters against arsenicosis in Long Evans rats. Sixty male rats were selected to perform the study. The experimental rats were randomly divided into five different groups, with n = 12 in each group. Animals in group T0 were given normal feed and water and kept as control. Rats of group T1 were given arsenic trioxide @ 100 mg/L of drinking water orally. Rats of group T2 were given arsenic trioxide @ 100 mg/L drinking water and Thankuni @ 1gm/kg feed. Group T3 was given arsenic trioxide @ 100 mg/L drinking water with grapes @ 10 mg /kg body weight thrown DW. Group T4 was given arsenic trioxide, Thankuni, and grapes with the same dose for up to 45 days, respectively. Four randomly selected rats from each group (T0, T1, T2, T3, and T4) were sacrificed at 15-day intervals to determine body weight, hematological and biochemical parameters. At the end of the experiment, the result showed that the minimum weight gain group was T1 whereas the maximum weight gain was found in the T3 and T4 groups. The weight gain of the T2 group is better than T1. Reduction of TEC and Hb values were significant (P < 0.01) in the T1 group. Whereas in the rest of the groups, the reduction of TEC and Hb was less than in arsenic-treated groups. In conclusion, Thankuni and grapes have a significant effect on body weight and hematological and biochemical parameters.

This study introduces an artificial intelligence–human-in-the-loop (AI-HITL) process to create an instrument for evaluating the quality of learning outcomes (LOs) in a veterinary curriculum. Although clear LOs are essential for competence-based education, their quality often varies, and traditional manual review is inconsistent and time-consuming. We used a large language model (ChatGPT-4o) to generate the initial 12-item Learning Outcomes Evaluation Instrument (LOEI) intended to measure clarity, measurability, and alignment. This instrument was then used by human experts to rate 415 LOs. Psychometric analyses supported a strong, unidimensional structure for the reduced to four-item LOEI, with high inter-item correlations ( r = 0.53 to 0.97), excellent model fit (CFI = 0.998, TLI = 0.995), and strong reliability (ordinal α = 0.92, ω = 0.93). These findings indicate that the AI-HITL–developed instrument demonstrates internal coherence and reliability, though evidence of item redundancy motivates further instrument refinement. We highlight the promise and limitations of using AI for instrument development and confirm that human expertise remains essential for psychometric validity and contextual relevance to improve curriculum quality and alignment with competence-based veterinary education.