Attitudinal assessment on ai-driven tools in agricultural education: A student perspective

Introduction

The Fourth Industrial Revolution has brought Artificial Intelligence (AI) to the forefront of technological innovation across sectors, including agriculture and education. Globally, AI-driven tools are transforming conventional teaching-learning models by fostering personalized, adaptive, and immersive learning experiences. In agricultural education, these tools are increasingly employed to improve learning outcomes, support decision-making, and prepare students for agri-tech-driven ecosystems. According to the World Economic Forum (2023), AI in education is projected to grow at a compound annual growth rate (CAGR) of 45% between 2023 and 2030, with applications in automated content delivery, intelligent tutoring systems, performance analytics, and chatbotassisted learning. In the context of agricultural sciences, AI offers a paradigm shift not only in research and extension but also in pedagogy. Intelligent systems that simulate field scenarios, visualize data from precision farming, or analyse agronomic patterns are being integrated into classrooms and virtual platforms. A study by UNESCO (2022) reported that AIenhanced learning systems improved conceptual understanding and knowledge retention in agricultural and life sciences by over 30% when compared to traditional lecture-based teaching. Parma et al (2022), suggested that predictor variables namely extracurricular activities, information collection interpersonal communication and professional zeal have significant influence on attitude towards application of mobile technology in agricultural extension of the postgraduate scholars. The Indian higher education system, including agricultural education, is rapidly adapting to this transformation. The Indian Council of Agricultural Research (ICAR), through its National Agricultural Education Policy (NAEP 2020), emphasizes the integration of digital technologies including AI, machine learning, and data analytics to build the capacities of future agricultural professionals. As per ICAR's 2023 report, over 70% of agricultural universities in India have initiated steps toward digital learning platforms and smart classroom initiatives, many of which involve AIenabled components such as automated assessments, virtual practical's, and adaptive learning platforms. India's National Education Policy (NEP 2020) also underscores the use of emerging technologies, including AI, to improve quality, inclusivity, and student-centric learning. The Ministry of Education's AI for All campaign (2021) promotes AI literacy among students, including those from non-engineering backgrounds like agriculture. In a nationwide survey conducted by NITI Aayog and CBSE (2022), nearly 64% of students expressed willingness to engage with AI tools in their education if proper training www.extensionjournal.com and infrastructure were provided. However, the study also highlighted disparities in access and a general lack of awareness among rural students and those from nontechnical disciplines. Despite the institutional push and technological readiness, the success of AI integration in agricultural education ultimately depends on the attitudinal disposition of students their acceptance, adaptability, perceived usefulness, and concerns regarding these tools. Literature indicates that technology adoption in education is significantly influenced by user attitude and digital confidence (Davis, 1989; Venkatesh and Bala, 2008) [1,10] . In the Indian context, agricultural students often come from rural or semi-urban backgrounds, where digital exposure varies significantly, and their receptiveness to AI tools may be influenced by various socio-educational factors. Jaya et al (2021) [3] found that strongly agree to agree) of the students opined that "social media can be used effectively for learning" and at the same time students felt that social media may be misused to spread rumours Recent studies point to both enthusiasm and apprehension among agricultural students. For instance, Kumar and Bansal (2023) [6] found that while 58% of students from three agricultural universities in North India viewed AI tools as useful for understanding complex agronomic concepts, nearly 30% reported challenges in accessing or using such tools effectively. Similarly, Patil et al. (2022) noted that students' confidence in using AI tools increased with prior exposure to ICT-based learning environments, highlighting the role of digital readiness. The use of AI in agricultural education is still in a nascent stage in India, and there is limited empirical evidence on how students perceive and interact with these technologies. While most studies focus on teacher preparedness or institutional infrastructure, student-centric attitudinal assessments remain underexplored. Understanding students' perspectives is essential for designing need-based AI interventions, ensuring equitable access, and minimizing resistance to digital change. Therefore, this study was undertaken with following objective.

Objective

To measure Attitudes of students toward AI-driven tools in agricultural education

The study mainly focus on dimensions such as perceived ease of use, perceived usefulness, digital confidence, and concerns related to technological substitution or data privacy. The findings are expected to provide actionable insights for policymakers, curriculum developers, and educational technologists in designing AI-integrated learning environments that are pedagogically effective and contextually relevant to Indian agricultural students.

Material and Methods

The present study adopted a descriptive research design through a survey method to assess the attitudes of agricultural students toward the use of AI-driven tools in education. This design was suitable as it allowed the systematic collection of standardized data to analyze students' perceptions and opinions on technological innovations in learning.

Data Collection and Analysis

Data for the present study were collected through a structured questionnaire designed to assess students' attitudes toward the use of AI-driven tools in agricultural education. The questionnaire consisted of two sections: the first section captured demographic and background information of the respondents, while the second section comprised 20 attitude statements (both positive and negative) developed on a five-point Likert scale ranging from strongly agree to strongly disagree. The instrument was pre-tested for reliability and validity before final administration. Data were collected personally from the respondents using the Online survey method, ensuring uniformity and accuracy of responses.

Data Analysis

After collection, the data were carefully coded, tabulated, and entered into Microsoft Excel 2021 for initial organization and cleaning. Subsequently, statistical analyses were performed using SPSS (Statistical Package for the Social Sciences) Version 26.0. Descriptive statistics such as frequency, percentage, mean, and standard deviation were used to describe the respondents' profile and attitude levels. Furthermore, correlation analysis was employed to determine the relationship between students' attitude scores and selected independent variables. www.extensionjournal.com The narrow standard deviation (0.25) underscores the homogeneity of attitudes across the sample, indicating a shared baseline of acceptance that can be leveraged for department-wide AI integration strategies. Overall, the findings highlight a strong foundation for expanding AIenabled pedagogical practices in agricultural education, while emphasising the need to address the specific concerns of the minority holding less favourable views. Overall, the data demonstrate that AI integration in agricultural learning enhances student engagement, practical application skills, and self-directed learning, but must be complemented with strategies to maintain interpersonal interaction and reduce apprehension. This supports a blended learning model, where AI augments rather than replaces traditional teaching.

Result and Discussion

The Perceived Personality Development construct reveals a generally favourable orientation towards AI's role in enhancing students' personal and professional competencies, though with variability across specific skills. The highest score was recorded for Item 8 ("I feel more competent and capable when I use AI tools to support my learning and field work") with Mean = 3.73, Z = 0.62, indicating a solid positive perception. Item 1 ("AI tools have helped me become more confident in expressing my ideas", Mean = 3.62, Z = 0.41) also lies in the mild positive range, highlighting AI's contribution to confidence building. The above results are inline with Puja et al (2025) Moderate positive perceptions were evident in Items 3 (Mean = 3.49, Z = 0.16) and 4 (Mean = 3.50, Z = 0.17), suggesting AI supports creativity, problem-solving, and critical thinking. Item 5 ("Helps to gain knowledge on current trends in agriculture") scored slightly below the average line (Mean = 3.39, Z = -0.03), implying that while useful, AI is not perceived as the primary source for industry updates. Negative tendencies appeared in Items 2 (Mean = 3.33, Z = -0.15) and 7 (Mean = 3.32, Z = -0.17), reflecting relatively lower endorsement of AI's impact on communication skills and mental strength. The strongest negative Z was for Item 6 ("AI tools reduce creativity due to overuse"), with Mean = 3.05, Z = -0.69, highlighting apprehensions regarding overdependence on technology. These findings indicate that students perceive AI as a meaningful enhancer of self-efficacy, particularly in competence, confidence, and applied field learning. The high Z for competence (Item 8) aligns with literature emphasising AI's role in experiential and self-paced learning environments, where personalised recommendations and real-time analytics empower learners to perform tasks with higher autonomy and precision. The above is in line with Zade et. al (2024) [11] . The moderate gains in creativity, problem-solving, and critical thinking corroborate the growing evidence that AI can act as a catalyst for higher-order cognitive skills when integrated into project-based and inquiry-led pedagogies. However, the relatively low perceptions of AI's impact on communication skills (Item 2) suggest that digital platforms may lack sufficient interactive or collaborative features to foster interpersonal growth an area that requires targeted instructional design interventions.

The notable concern about creativity decline due to overuse (Item 6) mirrors global debates about the risk of "automation complacency," where excessive reliance on AI could lead to reduced original thought. This underlines the necessity of balanced AI adoption strategies embedding technology as a supportive tool while safeguarding the need for human-led brainstorming, debate, and reflective practice. Overall, the personality development outcomes suggest that AI in agricultural education holds promise for empowering learners' competence and problem-solving abilities, but strategic pedagogical frameworks are essential to ensure it enhances rather than erodes creative and interpersonal skillsets. This balanced approach will be critical for preparing graduates to navigate both technologically advanced and human-centred agricultural systems.

Conclusion

The overall findings indicate that students in agricultural education generally hold a favourable and constructive www.extensionjournal.com attitude towards the use of AI tools, with the majority falling in the medium to high attitude range and a relatively low variation in responses. Across the three dimensions Perceived Academics, Perceived Learning, and Perceived Personality Development AI was recognised for its ability to enhance examination preparation, practical skills, independent learning, critical thinking, and field-based competence.

High Z-scores in areas such as self-directed learning (Item 11), real-time advisory support (Item 16), and diagnostic/problem-solving functions reflect strong acceptance of AI's role in making learning more engaging, interactive, and applicable to real-world agricultural contexts. The positive perception of AI as a complementary academic tool highlights its potential to support blended learning models that combine technology-driven efficiency with traditional pedagogical strengths. However, the presence of low and negative Z-scores for certain items particularly those related to the risk of reduced human expertise (Item 4), preference for traditional methods (Item 6), feelings of impersonality (Item 8), and concerns about creativity decline (Item 22) signals the need for a balanced and human-centric AI integration strategy. These apprehensions emphasise that AI adoption must be accompanied by strong human mentorship, collaborative learning opportunities, and safeguards against overreliance. In conclusion, the data supports the notion that AI tools, when thoughtfully integrated into agricultural education, can significantly enrich academic performance, learning engagement, and professional skill development. The challenge and opportunity lie in designing strategic, contextappropriate AI adoption frameworks that amplify its benefits while mitigating its limitations ensuring that graduates emerge not only technologically proficient but also adaptable, creative, and grounded in the human dimensions of agriculture.

Recommendation/

Acknowledgement

AcknowledgementThe authors gratefully acknowledge the support and encouragement provided by the Vignan Institute of Agriculture and Technology, Vignan's Foundation for Science, Technology and Research (VFSTR) University, for facilitating the successful conduct of this study. Sincere appreciation is also extended to the B.Sc. (Hons.) Agriculture students who served as respondents and generously devoted their valuable time to participate in the survey. Their cooperation and insights were instrumental in the completion of this research.

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