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Our way of life has evolved due to the advent of AI. Artificial intelligence (AI) is already having an impact on many parts of the industry and will likely find additional uses in the future. This research study investigates the students' readiness to adopt AI. An exploratory research design undergirds this investigation. A questionnaire (based on a prior study) survey was used to gather data. The SPSS and AMOS Graphics are used to conduct EFA and CFA. 387 respondents were taken into consideration for the study. In this investigation, the KMO value is 0.839. Factor loading is larger than 0.45, with 22 items. This study extracts five components: educational content (EC), institutional facilities (IF), artificial intelligence awareness (AW), acceptance barriers (AB), and usage/application (AP). The Eigen values of those components are 5.056, 2.420, 1.640, 1.411, and 1.000, explaining 52.4% of the total variation. The CFA model fit findings show that CMIN/DF=2.360, GFI= 0.0.913, AGFI= 0.884, CFI= 0.880, RMSEA=0.05, SRMR=0.05, Hoelter's N returns value at 5% significant level = 195 and at 1% significant level = 210, Chi square= 372.870, df=158, p=0.000. Today, AI may boost tertiary students' productivity. Engaging pupils with technology in our country helps determine their AI interests. The study shows how educational content, institutional facilities, artificial intelligence knowledge, acceptance hurdles, and usage/application affect students' preparedness to adopt AI. This research will help higher education authorities to introduce IT infrastructure, teach students to understand AI technology, and inspire future academics to contribute to this era of intelligent robots.

Financial inclusion aims to include the unbanked population in the official financial system. To quickly serve the unbanked population, this study aims to investigate the concept of agent banking as a means of financial inclusion and to assess the elements that influence the acceptance of agent banking in Bangladesh. The study also covered the main advantages of agent banking and pertinent barriers to its adoption. Using a basic judgmental sampling technique, 195 respondents from the Chittagong region made up the study's sample. Face-to-face interviews and a structured questionnaire were used to collect the data. The impact was investigated using statistical measures such as mean, standard deviation, correlation, and regression. Kendall's W test, also known as Kendall's coefficient of concordance, was utilized to evaluate the benefits and drawbacks of accepting agent banking in this study. According to the result, there is a significant positive correlation between acceptance of agent banking and independent variables - perceived trust, convenience, perceived risk, awareness, and service quality. The study also found that easy banking is the key reason for agent banking, marked by the respondents, followed by the nearest location and adding new customers. Lack of training is the major challenge, followed by a lack of logistic support and cyber security risk in implementing agent banking in Bangladesh.