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Enhancing insurance agents' learning and performance through AI-based training: a study within the Life Insurance Corporation of India

Benny Kurian & P.Uma Swarupa

International Journal of Learning and Intellectual Capital2025https://doi.org/10.1504/ijlic.2025.149670article
AJG 1
Weight
0.50

What the paper says

Artificial intelligence (AI) has improved insurance agents' learning and intellectual capital in the 21st century. This study examines how training influences insurance agents' performance within the Life Insurance Corporation of India (LIC) framework. This study examines how AI-based training influences LIC insurance representatives' knowledge and skill development to show how AI technology may improve insurance agents' expertise. Over an experiment month, LIC insurance brokers' effectiveness, population demographics, and voice personality factors were collected. Agent performance was measured by the average buy rate, which is the percentage of sales calls that resulted in loan renewal. A study hypothesis examines how AI-based training has affected LIC insurance agents' job performance. The relative effects of different parameters on agent purchase rates were assessed using multiple linear regression. The AI coach (AI trainer) greatly increased the purchase rate. The study also confirmed H1, revealing that middle-ranked agents improved their sales performance more than bottom- and top-ranked agents. Middle-ranked agents performed better after getting coaching remarks, moderating the inverted-U pattern, supporting Hypothesis 2.

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https://doi.org/https://doi.org/10.1504/ijlic.2025.149670

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@article{benny2025,
  title        = {{Enhancing insurance agents' learning and performance through AI-based training: a study within the Life Insurance Corporation of India}},
  author       = {Benny Kurian & P.Uma Swarupa},
  journal      = {International Journal of Learning and Intellectual Capital},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijlic.2025.149670},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.