Constraints and suggestions in utilisation of ICT and AIs tools among faculties in SAUs of Gujarat

Introduction

Teaching-learning procedures, research techniques and extension outreach have all been transformed by the incorporation of artificial intelligence (AI) and information and communication technology (ICT) in agricultural education. Personalized learning, data-driven research and effective information sharing are all made possible by these technologies. By producing qualified experts and developing pertinent technology, India's agricultural education system especially the State Agricultural Universities (SAUs) serves as the foundation for agricultural development. Four well-known SAUs, Junagadh Agricultural University, Anand Agricultural University, Navsari Agricultural University and Sardarkrushinagar Dantiwada Agricultural University are located in Gujarat, one of the states that leads in agricultural production and technological adoption. Technology integration in higher education, especially agriculture, is a key component of the National Education Policy (NEP) 2020. ICT resources like digital repositories, virtual labs and learning management systems are now crucial parts of contemporary agricultural curricula. Similar to this, academic members must be knowledgeable about these cutting-edge technologies for AI applications in agriculture, such as crop modelling, disease prediction, precision farming and decision support systems. However, there are a number of obstacles to faculty members' adoption of these tools, from talent gaps to infrastructure constraints. Meena et al. (2021) [7] claim that institutional support and faculty preparation play a major role in how well agricultural universities integrate technology. According to Singh and Patel (2022) [9] , although SAUs have made strides toward digitization, there are still a number of obstacles preventing the widespread use of sophisticated AI tools. Faculty members' technological prowess will be crucial to the future development of agricultural education. In light of this, the current study was conducted to determine the obstacles faculty members encounter while implementing ICT and AI tools and to collect recommendations for resolving these issues.

Objective

To identify the constraints in utilization of ICT and AI tools by faculty of SAUs of Gujarat and to obtain suggestions for overcoming them.

Materials and Methods

The four state agricultural universities in Gujarat are Sardarkrushinagar Dantiwada Agricultural University (SDAU), Anand Agricultural University (AAU), Navsari Agricultural University (NAU) and Junagadh Agricultural University (JAU). These universities are in charge of agricultural education, research and extension initiatives across the state's several areas. All four of Gujarat's SAUs participated in the study endeavor. Gujarat was specifically picked since it is one of the top states in terms of agricultural output and has a strong network of agricultural universities with different degrees of technological infrastructure. An ex post facto research design was used for the investigation. Because the independent variables' manifestations have already happened or are intrinsically unmanageable, the scientist has no direct control over them in this methodical empirical investigation. The study comprised faculty members from Agricultural Extension, Agronomy, Horticulture, Plant Pathology, Entomology, Agricultural Engineering and other related fields from each of the four SAUs. A total sample size of 240 respondents was obtained by selecting 60 faculty members from each university using a proportionate random selection procedure. To ensure representativeness, care was taken to include academic members with diverse years of experience and different designations (Assistant Professors, Associate Professors and Professors). Before being administered, questionnaires of constraints were created and pre-tested to gauge faculty members' replies about the challenges they faced when implementing ICT and AI tools. Each constraint and suggestion's mean score was determined for analysis and ranks were then allocated appropriately.

Results and Discussion

Constraints refer to a situation or circumstance which impedes, restricts or limits the activity or performance of an individual. In this study, it was operationalized as items of difficulties experienced by the faculty members in adopting ICT and AI tools for teaching, research and extension activities. Based on the responses from faculty members, the total score was calculated for each constraint, converted to mean score and accordingly, rank was given. The information regarding the constraints is depicted in Table 1. It is evident from the data presented in Table 1 that the foremost constraint reported by the respondents was "I am concerned about data safety and privacy while using AI tools" which secured the 1st rank with a mean score of 1.43. This indicates that faculty members are increasingly aware of the potential risks associated with AI platforms, including data breaches, unauthorized access and misuse of personal or institutional information. As AI tools often require sharing of data on cloud-based platforms, concerns regarding confidentiality and security have emerged as critical barriers to adoption. This finding assumes greater significance in the context of agricultural research data, which may include sensitive information related to ongoing experiments, unpublished findings and intellectual property. The second most critical constraint was "The cost of AI tools or software platforms was too high" (Mean=1.24) ranked 2nd. Many specialized AI tools, educational technology platforms and statistical software require expensive licenses or subscription fees that individual faculty members or even departments may find difficult to procure. This finding corroborates with the results of Sharma and Kumar (2021) [8] who documented similar financial constraints in technology adoption among agricultural university faculty. [2] and Subash et al.

(2013) [11] who reported that although faculty members recognize the importance of modern technologies, they often lack structured training opportunities to develop proficiency in these areas. The rapid evolution of ICT and AI tools requires continuous upskilling, which is currently not adequately addressed through faculty development programmes. These findings were in close conformity with the earlier research of Anurag et al. (2013) [2] , Subash et al. (2013) [11] , www.extensionjournal.com

Tanko et al. (2013) [12] , Sireesha et al. (2014) [10] , Agwu and Ogbonnah (2015) [1] , Mathews (2019) [6] , Dash (2021) [3] and Kumawat (2023) [5] , who also reported that lack of technical expertise, inadequate training, insufficient budget, erratic internet connectivity, poor maintenance, high equipment costs and health-related challenges acted as major obstacles in ICT adoption.

It is important to overcome constraints and seek suggestions from the respondents to improve the adoption of ICT and AI tools among faculty members. It gave value to the technical knowledge of the respondents and their understanding of technology integration in agricultural education. For this, suggestions were invited openly from the respondents. Most respondents gave similar suggestions, so the total score for each suggestion was calculated, converted into mean scores and rank was given accordingly. The information is presented in Table 2.

It is reflected from Table 2 that the most prominent suggestion ranked 1st with a mean score of 1.671 was "There should be upgraded classrooms with smart boards and modern digital infrastructure." This emphasizes the foundational need for physical infrastructure that supports technology-enhanced learning. Smart classrooms equipped with interactive boards, high-resolution projectors, audiovisual systems and reliable internet connectivity create an environment conducive to integrating ICT and AI tools in teaching. Faculty members are more likely to adopt new technologies when the necessary infrastructure is readily available and functional. This was closely followed by the 2nd rank given to "There should be a dedicated support unit for the maintenance and repair of ICT tools and software" with a mean score of 1.658. The presence of reliable technical support can significantly reduce faculty members' anxiety related to technology use. A dedicated support unit ensures timely resolution of hardware and software issues, regular maintenance of equipment and assistance during technology-integrated sessions, thereby encouraging faculty to incorporate these tools more frequently in their academic work. There should be allocation of time in the academic schedule for exploring and using new ICT and AI tools. 379 1.579 IX 10 There should be efforts to motivate and encourage faculties to adopt new technologies in their academic work. 378 1.575 X

The 3rd rank was assigned to "Periodical trainings should be given to professionals to update information related to ICT and AI tools" with a mean score of 1.654. This highlights the urgent need for structured, continuous faculty development programmes that focus not only on basic digital literacy but also on advanced applications of AI in agricultural education. Such programmes should be designed to cater to faculty members at different levels of technological proficiency and should be updated regularly to reflect the rapid advancements in technology. These findings were in conformity with the research of Subash et al. (2013) [11] , Sireesha et al. (2014) [10] , Agwu and Ogbonnah (2015) [1] , James (2017) [4] , Mathews (2019) [6] and Dash (2021) [3] , who similarly emphasized that regular training, infrastructure upgradation, sufficient budget allocation, better internet connectivity and supportive institutional policies were essential to increase ICT utilization in agricultural universities.

Conclusion

Acknowledgement

AcknowledgementThe authors acknowledge the support and cooperation extended by the four State Agricultural Universities of Gujarat, Junagadh Agricultural University (JAU), Anand www.extensionjournal.com Agricultural University (AAU), Sardarkrushinagar Dantiwada Agricultural University (SDAU) and Navsari Agricultural University (NAU) during the course of this work.

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