Evolution of the use of conversational agents in business education: Past, present, and future

Abstract

Purpose: The primary objective of this article is to answer the proposed research questions by analyzing the publications on artificial intelligence (AI), education, business, and conversational agents (CAs) or chatbots within the Scopus database to identify the most frequent terms used by scholars over the years, as well as to classify the research evolution based on the most important thematic trends. Originality/value: The study contributes to the literature by offering insights into the thematic landscape of AI-related research across multiple domains. By employing a comprehensive methodology, the article provides a nuanced understanding of the intersection between AI, education, business, and CAs. Design/methodology/approach: The methodology comprises two main components. Firstly, topic modeling is applied by the year of publication to summaries of articles meeting the search criteria. Secondly, grounded theory coding was used to categorize generated themes into more meaningful classifications. This dual approach ensures a rigorous data analysis and facilitates the identification of overarching thematic trends. Findings: The results reveal five thematic trends investigated in the analyzed publications: 1. student-centered learning in higher education; 2. interactive methods using the natural language processing (NLP) approach; 3. technological solutions and ChatGPT in a university context; 4. enhancing education through intelligent platforms; and 5. challenges for social and academic integration of AI tools. Additionally, the study proposes a research agenda with some questions as future avenues of inquiry.