Journal of Scientific Research and Reports

Veterinary Science educational institutes across the globe have witnessed a technological revolution in the last half-decade, giving a retouch to the traditional teaching methods to facilitate holistic learning and minimize animal usage in practical demonstrations. Computer-aided learning resources, Virtual worlds, Mixed reality, augmented reality, Immersive Virtual Reality, and Artificial intelligence-enabled reality are the technologies where veterinary science teaching is finding its scope to minimize animal usage. Virtual reality (VR) is one of the potential technologies that can be incredibly valuable in veterinary science teaching and learning environments. Several Western Veterinary Institutions have explored this and are implementing implemented in regular classroom teaching. The Govt of India, under the National Agricultural Higher Educational Project (NAHEP) component-II, also introduced VR in Veterinary and Agricultural Institutions.

This research study was conducted to assess the effectiveness of Virtual Reality as an educational tool in Veterinary Science, specifically in teaching animal slaughter techniques. Customized VR-based learning modules were developed and deployed to 120 third-year undergraduate veterinary students from four geographically diverse colleges across India, two from the northern region and two from the southern region. Students experienced the VR modules and subsequently provided structured feedback on their learning experience. Majority of the students found VR-based learning engaging, ethical, and educationally superior to conventional audiovisual (AV) methods. 81% of the students reported an improved conceptual understanding and better retention through VR immersion compared to standard teaching aids.  A survey questionnaire was administered to collect detailed feedback, with an emphasis on open-ended responses to yield wide insights. Natural language processing (NLP) toolkit was employed to analyze the open-ended responses, comparative analysis was done using artificial intelligence (AI), large language models (LLM), qualitative data analytical software, Atlas.ti, and manual processing techniques. Further results were summarized through Exploratory Data Analysis (EDA) wherever applicable.