Leveraging Generative AI for Data Analysis in Farm Management

Generative AI (GAI) marks significant advancements in technology and machine learning models. It has achieved a newer and higher level of creativity and innovation through the AI system. With such rapid growth and boom in GAI, gaps exist in the current literature about the organizations and individual levels of applying GAI. The researchers conducted a mixed-methods study to explore business professionals’ experiences and perceptions of using GAI. This current study examined the purpose of using GAI and the statistically significant differences in productivity before using GAI versus after using GAI. The impact of gender, age, and educational background on work productivity while using GAI was also investigated. Furthermore, this study researched the most prominent GAI tools these business professionals use. The advantages and disadvantages of using GAI were analyzed through detailed content analyses of the qualitative data using NVIVO and SQL. This study highlights the vital impact of GAI in improving efficiency, increasing productivity, and fostering innovation. It also calls for strategic planning to maximize the GAI benefits in organizational implementations while addressing overreliance, ethics, security, hallucination, and user experience concerns.

Generative AI (GAI) can be leveraged to good effect in engineering, technology, and computing (ETC) classrooms but with pre-conditions; among these include teacher preparedness. One dimension of teacher preparedness is knowing how GAI is used in classrooms like their own. This paper responds to that need. The paper surveys the literature to answer three questions: in formal instructional interventions, what were the tasks for which GAI was used; what methods did scholars use to evaluate the GAI instructional set; and what were some of the identified challenges and opportunities regarding the use of GAI in ETC classrooms? Taking account of quality and recency of scholarship, and the need to focus on ETC practices, a rapid review of IEEE FIE conference proceedings for 2023 and 2024 was undertaken. Of the 1,181 papers published in both conference proceedings, and after a two-stage screening process, 20 papers were selected for synthesis. Of the 20 papers examined in this review, we found seven patterns of research design. The most common was the learning and teaching intervention followed by gathering student feedback. While we expect that a bigger corpus will yield additional research designs, these seven provide a start for defining a typology of research design investigating student use of GAI. The review revealed eight distinct categories of use ranging from software development, the most common, with over 26 distinct tasks, to data classification and data analysis, each with two distinct tasks. GAI was found to be used for many low-cognitive tasks like generating bibliographies to cognitivelydemanding uses like design and modeling. The studies reported recurring concerns about using GAI, like perpetuating bias, and hallucinations. One striking empirical finding is that previous ways of accomplishing ETC tasks, like using MATLAB and hand analysis, may be quicker and easier than using GAI. The scholarship advocates for broad and deep GAI literacy programs, addressing GAI abilities, limitations, ethics of use, and prompt design. Moreover, instructors are encouraged to be GAI literate themselves. This know-how is central to designing meaningful GAI-based tasks which seek to prepare students for an increasingly AI-infused world and workplace.

Generative artificial intelligence (GAI) created a whirlwind in late 2022 and emerged as a transformative technology with the potential to revolutionize various industries, including design. Its feasibility and applicability have been extensively explored and studied by scholars. Previous research has investigated the potential of AI in different domains, such as aiding data collection and analysis or serving as a source of creative inspiration. Many design practitioners have also begun utilizing GAI as a design tool, stimulating creativity, integrating data more rapidly, and facilitating iterative design processes. However, excessive reliance on GAI in design may lead to losing the uniqueness emphasized in the field and raise concerns regarding ethical implications, biased information, user acceptance, and the preservation of human-centered approaches. Therefore, this study employs the double-diamond design process model as a framework to examine the impact of GAI on the design process. The double diamond model comprises four distinct stages: discover, define, develop, and deliver, highlighting the crucial interplay between divergence, convergence, and iteration. This research focuses on GAI applications' integration, timing, and challenges within these stages.A qualitative approach is adopted in this study to comprehensively explore the potential functionalities and limitations of generative AI at each stage of the design process. Firstly, we conducted an extensive literature review of recent advancements and technological innovations in generative artificial intelligence. Subsequently, we participated in lectures and workshops and invited experts from various domains to gather insights into the functionality and impact of generative AI. Later, we interviewed design professionals experienced in utilizing generative AI in their workflow. Data triangulation and complementary methods like focus groups are employed for data analysis to ensure robustness and reliability in the findings related to the functionality of generative AI.The findings of this study demonstrate that generative AI holds significant potential for optimizing the design process. In the discovery stage, generative AI can assist designers in generating diverse ideas and concepts. During the definition stage, generative AI aids in data analysis and user research, providing valuable insights to designers, such as engaging in ideation by addressing "How might we" questions, thereby enhancing decision-making quality. In the development stage, generative AI enables designers to rapidly explore and refine design solutions. Lastly, in the delivery stage, generative AI can help generate design concept documentation and produce rendered design visuals, enhancing the efficiency of iterative processes.This study contributes to a better understanding of how generative artificial intelligence can reshape the existing framework of the design process and provides practical insights and recommendations for contemporary and future designers.

Despite the potential and challenges of generative AI (GenAI) in higher education, there remains a significant gap in empirical research to guide strategic decision-making. Current studies are often conceptual or small-scale explorations of staff and student experiences, ranging from analyses of the potential advantages of GenAI, through to expressions of serious concerns about risks for higher education (e.g. Chan & Hu, 2023). Large-scale and comprehensive, data-driven investigations remain rare, however. In particular, there is a need to explore the experiences of students, to better understand what they know, how they are using GenAI, why they are using it, and their attitudes about the use of AI in their studies, by their educators and in their future lives (e.g. Lodge et al., 2023; O’Dea, 2024). This Pecha Kucha reports on the second phase of a multi-institutional collaborative project. The project’s overall research question is ‘How do university students make meaning about GenAI in relation to themselves as learners?’ The first phase involved focus groups at each university, followed by a large-scale online survey. The analysis of the focus group data informed the design of a cross-sectional survey to explore students’ perspectives on GenAI on topics such as knowledge and access, use and usefulness, and attitudes. The survey development was an iterative and significant undertaking. Current literature was unable to provide a comprehensive picture of student use and attitudes. Given the rapidly changing GenAI landscape, we were also aware that student experience and attitudes are contextually dependent and may also evolve quickly. Whilst valuable, existing studies have not connected the varied dimensions of the student experience we seek to investigate. While keeping these studies in mind, this project used a grounded thematic approach to build its own survey instrument, beginning with 4 focus groups of 11 scholars to identify the key issues which needed to be explored within the Australian university context. This stage was followed by iterative development of the survey, in which over 200 suggested edits were provided by 17 scholars as well as a reference group of Deputy/Pro-Vice Chancellors. When completed, the survey was piloted by undergraduate students. The anonymous online survey was conducted in the second half of 2024 at four Australian Universities located in Victoria, New South Wales, and Queensland. Human Research Ethics Committee approval was granted at each University prior to conducting this study. The data is currently being collected. To date, the survey has been completed by 8,340 students. The Pecha Kucha will present key descriptive data on selected components of the survey including student knowledge and access, GenAI literacies and skills, perceptions of use and usefulness, and motivations and barriers for when they choose to (not) use GenAI. We will present findings of the whole sample, as well specific equity groups who may have experienced disadvantage or under-representation. For example, preliminary analysis already indicates significant differences in the ways in which international students use and trust generative AI. The findings will provide an evidence base to inform policies and practices that leverage GenAI's benefits while mitigating its risks.

This research explores the innovative application of Generative AI (GenAI) in the context of Mars missions, drawing key insights from Mars analogue missions. Given the unique challenges of Mars exploration, including extreme environmental conditions, resource constraints, and the need for autonomous decision-making, GenAI emerges as a pivotal technology. We begin by introducing GenAI's capabilities, followed by an analysis of Mars analogue missions to understand the parallels and challenges relevant to actual Martian expeditions. We then delve into a variety of potential GenAI applications, ranging from data analysis, habitat design, and crew health monitoring, to psychological support and communication enhancements. Key case studies from analogue missions are presented to illustrate how GenAI could effectively address specific challenges faced during these simulations. Technical and ethical considerations are discussed to provide a comprehensive view of the implementation requirements and potential concerns. The paper also highlights how feedback from analogue missions can be instrumental in refining GenAI for future Mars missions. The concluding section of the paper speculates on the future trajectory of GenAI in space exploration and its role in advancing Mars colonization efforts. This research aims to bridge the gap between theoretical AI potential and practical application in space exploration, offering insightful recommendations for integrating GenAI into future Mars missions.

The rapid development of generative AI (GenAI) technologies in recent years has enabled new opportunities as well as new challenges in higher education. While many studies in computer science have focused on GenAI in programming education, fewer have examined its possibilities and challenges in requirements engineering (RE). This study aims to explore the impact of GenAI on the pedagogical aspects of RE in higher education, focusing on the student perspective, to analyse how GenAI might influence learning experiences, knowledge acquisition, and skill development. The main research question to answer was: "What are the students’ perspectives of the integration of GenAI in the educational practices of requirements engineering?" An Action research strategy was employed, with one of the authors also serving as teacher in the investigated course. A mixed-methods approach was used to collect both qualitative and quantitative data from workshops and surveys. During the workshops, students used ChatGPT to generate and evaluate software requirements and compared these to manually crafted requirements. Thematic analysis of the qualitative data captured students’ perspectives, while survey data identified trends and preferences. Findings show that while students generally had a positive experience with GenAI, valuing its efficiency and the quality of generated requirements, they also recognized the need for human oversight to maintain accuracy. The study highlights both opportunities and challenges of using GenAI in RE education. While GenAI increased learning engagement and helped with brainstorming, students faced difficulties in creating effective prompts and found it time-consuming to refine AI-generated requirements. A hybrid approach, combining AI-generated and manually created requirements, proved most effective by balancing AI's advantages with human insights. Further research is needed on how GenAI could be effectively integrated into computer science education.

The education sector has witnessed major changes in the era of generative artificial intelligence (GenAI), since the role of the teacher is not only limited to transferring or presenting information but has also expanded to include multiple roles that are dependent on AI. The framework for teachers’ primary operations has changed and become more focused on operations based on GenAI variables. As GenAI is a promising field, it is necessary to identify the areas of the framework for intelligent operations (FiOps) for the teacher, where FiOps relates to the main areas of the teacher’s work, including performance operations based on GenAI tools. Therefore, this study aimed to explore the FiOps for teachers in the era of GenAI. The study adopted a phenomenological approach to perform an in-depth analysis of the FiOps domains according to the views of expert teachers on using GenAI. Semi-structured individual interviews were conducted with eight teachers. A focus group was also conducted with the entire group to reach a deep understanding of the expert teachers’ thoughts on FiOps. The results show that the main FiOps domains are positioned around four areas: data analysis, program design, guidance and counseling, and discovery and enhancement of capabilities. It has become important to train teachers in the FiOps domains so that they can be more effective and more suitable for work in the era of GenAI.

This research aims to understand undergraduate business students’ Generative AI (GenAI) usage and perception by analyzing survey data collected in August 2024 and January 2025. The study intends to identify whether any gap exists between the extent of GenAI usage by college students and how businesses are planning to use GenAI. Key findings revealed that business students often use GenAI for information search, brainstorming ideas, and writing-related tasks; however, their use of GenAI for data analysis and multimedia contents are extremely limited. The study also finds that students’ GenAI usage is increasing, but they are still not confident about their GenAI proficiency. This research stresses the need for higher education institutions and instructors to not only teach how to use GenAI in more depth, especially prompt engineering, but also incorporate GenAI tools in a variety of business use case settings to teach the full potential of GenAI and its limitations to meet industry needs.

The rapid evolution of large-language models such as ChatGPT, Claude, and Gemini is reshaping the methodological landscape of obstetrics and gynecology (OBGYN) research. This narrative review provides a comprehensive account of generative AI capabilities, key use cases, and recommended safeguards for investigators. First, generative AI expedites hypothesis generation, enabling researchers to interrogate vast corpora and surface plausible, overlooked questions. Second, it streamlines systematic reviews by composing optimized search strings, screening titles and abstracts, and identifying full-text discrepancies. Third, AI assistants can draft reproducible analytic code, perform preliminary descriptive or inferential analyses, and create publication-ready tables and figures. Fourth, the models support scholarly writing by suggesting journal-specific headings, refining prose, harmonizing references, and translating technical content for multidisciplinary audiences. Fifth, they augment peer-review and editorial workflows by delivering evidence-focused critiques. In educational settings, these models can create adaptive curricula and interactive simulations for trainees, fostering digital literacy and evidence-based practice early in professional development among clinicians. Integration into clinical decision-support pipelines is also foreseeable, warranting proactive governance. Notwithstanding these opportunities, responsible use demands vigilant oversight. Large-language models occasionally fabricate citations or misinterpret domain-specific data ("hallucinations"), potentially propagating misinformation. Outputs are highly prompt-dependent, creating a reliance on informed prompt engineering that may disadvantage less technical clinicians. Moreover, uploading protected health information or copyrighted text raises privacy, security, and intellectual property concerns. We outline best-practice recommendations: maintain human verification of all AI-generated content; cross-validate references with primary databases; employ privacy-preserving, on-premises deployments for sensitive data; document prompts for reproducibility; and disclose AI involvement transparently. In summary, generative AI offers a powerful adjunct for OBGYN scientists by accelerating topic formulation, evidence synthesis, data analysis, manuscript preparation, and peer review. When coupled with rigorous oversight and ethical safeguards, these tools can enhance productivity without compromising scientific integrity. Future studies should quantify accuracy, bias, and downstream patient impact. · Generative AI supports various research stages in OBGYN, such as hypothesis generation, systematic review assistance, data analysis, and scientific writing, demonstrating its potential to streamline research workflows and improve research efficiency.. · Generative AI has notable limitations, including the risk of generating inaccurate references ("hallucinations") and the need for careful supervision.. · Effective usage requires skill in prompt engineering, posing a challenge for those without technical expertise.. · Utilizing generative AI in sensitive fields like OBGYN raises privacy, security, and ethical concerns..

Purpose The present study aims to examine how organisations adopt Generative AI and what are the key capabilities that influence the adoption of this technology. The study also investigates how Generative AI adoption influence market performance and sustainability performance. It additionally examines the moderating effect of regulatory support and organizational culture in influencing the association between Generative AI and firm performance. Design/methodology/approach The study uses a mixed-methods design involving qualitative and quantitative data collection and analysis. The first stage begins with qualitative interviews followed by thematic analysis to establish leading capabilities behind Gen AI adoption, in the second stage, the data were collected from 385 respondents from different organizations which was then analysed using PLS-SEM structural equation modelling. Findings The results observed that a firm’s digital transformation, innovation and marketing capabilities (MC) significantly enhance its Generative AI Adoption, which further influences firm performance. In addition, regulatory support emerges as a key moderator in driving DTC. Research limitations/implications The findings emphasize that the firm should enhancing digital transformation capabilities, innovation capabilities and MC which can further strengthen the adoption of Generative AI and affect the firm market and sustainability performance. Whereas strengthening regulatory support can enhance the positive impact of DTC and Gen AI on firm sustainability performance. Originality/value The study contributes to the literature on Generative AI by shaping an understanding of how the adoption of GenAI relates with the capability of firms in impacting sustainability performance. The research reports a critical gap in the literature by moving beyond the GenAI enthusiasm and assesses whether the advantages of adopting GenAI are durable as the technology diffuses across industries. The study contributes to literature by highlighting the role of regulatory support and determines that how environmental and firm-level factors jointly shape the effective and responsible capitalization of Generative AI for value creation over time.

• Generative AI tools are highly adopted in academia, with significant differences across fields, genders, and countries. • PhD students and early-career academics are the highest adopters for research purposes. • AI applications are widely used for translation, proofreading, and literature review, but less for data analysis in research activities. • Content creation, learning support, and assignment design are key reasons for using AI in teaching, with different patterns based on academic positions and countries. • Inaccurate information, plagiarism, and reduced critical thinking skills are the top concerns of AI use in academia. Although generative AI is transforming academic research and education, little is known about the role, gender, international, and disciplinary variations in uptake and use. This 20-country survey of publishing academics shows the widespread awareness and adoption of generative AI tools in academia, but with substantial international and disciplinary differences, and some role and gender differences. In particular, females were 10 % less likely to use Gen AI frequently (daily or weekly) for research, which may exacerbate gender inequalities. Perhaps surprisingly, the highest adoption rates occurred in some non-Western nations, possibly because of a greater need for translation services. The highest awareness is in the social sciences, perhaps because of the greater need for text analysis. Across all groups, these tools were mainly used for academic writing rather than data analysis and support for critical thinking. Despite this, personalized instruction and problem-solving are among generative AI's most generally claimed benefits. However, participants in all groups were skeptical about the creativity, accuracy, and consistency of AI-generated content in academic contexts. The most significant concerns about using generative AI in academia were inaccuracy, plagiarism, discouraging critical thinking, a lack of transparency and explainability, intellectual property rights violations, and data privacy risks. For policymakers, the findings point to fields and countries that may need action to prevent falling behind, as well as the ongoing need to investigate and monitor the impacts of generative AI on research practices.

The infusion of generative AI (GenAI) is already disrupting established services. This technology’s generative and agentic nature challenges the design and management of service routines, which have been previously handled primarily by frontline service employees. Guided by organizational routines theory, our longitudinal study (2020–2024) examines how the infusion of GenAI changes routines in customer support services. We gathered interview data from 41 employees, managers, and AI experts in two phases, pre- and post-GenAI . Based on the analysis of the qualitative data, we revealed seven recurring micro-level augmentation patterns, illustrating how GenAI-infused service routines function. The results show that GenAI is primarily embedded in the backstage of knowledge-intensive services, from which it then permeates the frontstage. We contribute to the literature on hybrid human–AI service delivery by identifying augmentation patterns and conceptualizing service permeation via two mechanisms: (1) simultaneous service permeation , which unfolds as employees leverage GenAI in real-time and integrate GenAI’s responses, recommendations, and adaptations into the frontstage; (2) sequential service permeation , which emerges as employees perform new routines of documentation and AI feeding to facilitate GenAI’s adaptability in frontstage and backstage operations. The MAPs and service permeation mechanisms guide practitioners in integrating GenAI into service routines and managing novel employee-GenAI collaborations.

Generative AI (GenAI) is experiencing rapid growth, particularly in its application as a tool for qualitative text analysis—a key element of Human-Computer Interaction (HCI) research. This study examines the potential of GenAI, specifically ChatGPT, to assist in the analysis of qualitative research data. Four qualitative HCI studies, previously conducted and analyzed by our research group, were selected for this investigation. ChatGPT was employed to perform AI-assisted analyses on the raw data from these studies, and the AI-generated insights were then compared with the human-led analyses already completed. The results reveal significant alignment between the human and AI-assisted analyses, indicating that GenAI can serve as an effective support tool in qualitative research. However, while GenAI offers considerable advantages in enhancing research efficiency, human oversight remains crucial to ensuring accurate interpretation and contextual alignment. This study also provides practical recommendations for researchers interested in incorporating GenAI into their qualitative analysis processes.

Future research recommendations for transforming higher education with generative AI

The incorporation of generative artificial intelligence in governance, political knowledge, electoral, and democratic processes is essential as the world transitions to a digital paradigm. Numerous nations have adopted Generative AI (GenAI), a disruptive technology that compels electoral bodies to advocate for the integration of such tools into governance, electoral, and democratic processes. Nevertheless, these technologies do not ensure effortless integration or efficient usage owing to intricate socio-cultural and human dynamics. Certain African jurisdictions are ill-prepared for the adoption of these technologies due to factors including underdevelopment, insufficient electrical supply, lack of technology literacy, reluctance to change, and the goals of governing parties. This study examines generative artificial intelligence as a disruptive technology for enhancing governance, political knowledge, electoral processes, and democracy. A mixed-method approach was employed, incorporating surveys and in-person interviews. The analysis of data, debates, and interpretation of findings were grounded in postdigital theory and theme analysis employing an abductive reasoning technique, in alignment with the tenets of critical realism. The study demonstrated that GENAI can influence political knowledge, election processes, and enhance efficiency in government and democracy. Moreover, GENAI, including ChatGPT, can either exacerbate or mitigate societal tendencies that contribute to human division, facilitate the dissemination of misinformation, perpetuate echo chambers, and undermine social and political trust, as well as polarise disparate groups or sets of viewpoints or beliefs. AI offers substantial opportunities but also poses many obstacles, including technical constraints, ethical dilemmas, and social ramifications. The swift progression of AI may disrupt labour markets by automating tasks conventionally executed by people, resulting in job displacement. Implementing AI necessitates significant upskilling and proficiency with digital tools; therefore, governments and organisations must adequately train their personnel to reconcile the disparity between AI's capabilities and users' comprehension. Additionally, there is a requisite for governmental oversight, regulation, and monitoring of AI adoption and utilisation across all facets of its implementation.