Vamsi Dasi

https://doi.org/10.64823/ijter.2608003

Chatbots powered by artificial intelligence are becoming a part of higher education, supplementing teaching and learning as well as student services. Nevertheless, there is only limited evidence regarding the factors influencing students’ intention to use these technologies in the Indian higher education context. The influence of performance expectancy, effort expectancy, social influence and perceived trust on students’ intention to adopt artificial intelligence-based chatbot. This study adopted a quantitative research design using a self-administered, structured questionnaire administered to students of Pondicherry University. For data analysis, 477 valid responses were analyzed through IBM SPSS Statistics and SmartPLS after screening. The results demonstrate that performance expectancy was the strongest predictor of chatbot adoption intention, suggesting students are likely to adopt chatbots when they see obvious academic and learning gains. Social influence, perceived trust, and perceived intelligence also significantly and positively impact adoption intention, whereas effort expectancy does not significantly affect students' behavioral intention, implying that ease of use may be less important for digitally literate learners. This study recommends that educational institutions should focus on developing chatbot system programs with high efficiency, elegance and academic aptness while promoting their usage through institutional initiatives alongside peer influence. Combining the factors of the Technology Acceptance Model with artificial intelligence-specific features provides this study with a holistic view and practical advice to increase student acceptance and intent to engage.

https://doi.org/10.64823/ijter.2607013

The insurance industry is increasingly leveraging artificial intelligence (AI) to enhance operational efficiency, customer experience, and decision-making capabilities. While existing studies have predominantly focused on AI adoption determinants and specific application areas, limited attention has been given to understanding how insurers evolve from digitalization to AI-enabled business models over time. Addressing this gap, the present study examines the AI transformation journey of India's private general insurance sector through a longitudinal content analysis of ICICI Lombard and Bajaj Allianz General Insurance. The study analyses corporate disclosures, annual reports, and publicly available documents from 2017 to 2025. The findings reveal a four-stage transformation pathway comprising digitalization foundation, process automation, AI integration, and intelligent insurance. Both insurers progressively expanded AI applications across underwriting, claims management, customer service, fraud detection, and risk assessment functions. The analysis further indicates a shift from operational automation toward enterprise-wide AI capabilities and Generative AI-enabled solutions, reflecting the emergence of intelligent insurance ecosystems. The study contributes to the literature by providing longitudinal evidence of AI-driven organizational transformation in the insurance industry and extending understanding of how firms develop and deploy AI capabilities over time. The findings offer practical insights for insurers seeking to accelerate their transition from digitalization to intelligent insurance through strategic AI implementation.