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Entrepreneurship education (EE) through innovative teaching techniques (ITEE) and entrepreneurial intention (EI) are two essential components of entrepreneurship development. Using the assumptions of the Theory of Planned Behavior (TPB), we took three constructs: attitude (ATT), subjective norms (SUBNs), and perceived behavioral control (PBC), and from the assumption of Human Capital (HC) theory, we constructed another variable, ITEE. Thus, the fundamental objective of this study is to find out the essential predictor of EI between entrepreneurial antecedents (ATT, SUBNs, PBC) and ITEE through an artificial neural network (ANN).

Using the snowball sampling technique, a highly structured questionnaire was sent to respondents. Finally, a sample size of 397 business graduate students was chosen.

The findings revealed that two dimensions of entrepreneurial antecedents (attitude and subjective norm) positively impacted EI. Furthermore, ITEE partially mediated the relationship between two dimensions of entrepreneurial antecedents (attitude and subjective norm) and EI. Moreover, through ANN, we found that attitude (ATT) was a crucial predictor of EI among business graduate students in Bangladesh.

In this study, only business graduate students were included as respondents; thus, further research should include students from other departments or disciplines to generalize the findings.

This study covers numerous actors in terms of practical contributions, including students, academics, the government, and the state. This article should draw the attention of Bangladesh government policymakers regarding the significance of ITEE for developing entrepreneurship. The research framework of this study proposed that ITEE should implement antecedents of entrepreneurship into business education, boosting the ability of students to make judgments, which will also enhance EI in the future.

Integrating the TPB theory with human capital theory represents a significant scholarly advancement in business education for graduate students in Southeast Asia, namely Bangladesh. Furthermore, we developed a novel ITEE scale by synthesizing information from many literary sources, providing valuable insights for future researchers.

Research on training and/or L&D effectiveness is predominantly conducted in a traditional L&D context. Little research is conducted on training and/or L&D in the context of artificial intelligence (AI)-based learning. The present study aims to investigate the relationship between the adoption of AI-based learning systems and learners’ behavior. Drawing from the theory of planned behavior, the research examines the impact of attitude (ATT), subjective norm (SN) and perceived behavioral control (PBC) as AI-based learning intention (ALI) factors relate to changes in learners' behavior. Additionally, inspired by the self-determination theory by Deci and Ryan, the study further examines the mediating role of learner engagement between ALI and behavioral change.

Following a theoretical framework and using a systematic literature review method, the survey research has been planned by considering a sample from Indian industries. The collected data have been analyzed using SPSS-AMOS 27. While path analysis has been conducted to analyze the direct impact of ALI on learners' behavior, Hay’s PROCESS macro has been used to check the mediating impact of learner engagement between ALI and learners' behavior.

The results proved a significant and positive impact of all ALI factors such as ATT, SN and PBC on learners’ behavioral change. Further, the research found that learning engagement (LE) successfully mediates between AI learning intention and behavioral change.

In the absence of any empirical study in identifying the relationship among learning intention, LE and behavioral outcome, the result of this study may provide useful insights to researchers and practitioners.