Abstract:

The extensive development of information and communication technologies over the years has enabled different interactive elements to be used in the educational process. These are quick and efficient to use and have the potential to assist people in making decisions (Hohenberger et al., 2019). The degree of interactivity provided varies between different multimedia educational formats, ranging from videos of a mainly narrative nature, where interactions are limited (functions of fast-forward, rewind, repeat, pause, etc.), to resources such as the chatbot, which is much more interactive. The chatbot is one of the most popular artificial intelligence applications and a prominent subject in human–computer interaction research (Nguyen et al., 2022). It can deliver learning content and feedback interactively and has been shown to hold great potential in educational settings (Wollny et al., 2021). Chatbots have also been found to be more effective, compared to non-interactive courses providing the same content, in improving individuals’ reasoning abilities in classical reasoning problems (Le & Wartschinski, 2018). However, chatbot research is still relatively new and limited in scope, especially in education (Hwang & Chang, 2021).

Based on the framework of dual-process theories of cognition, the present study aims to address the under-researched question of how interactive learning tools (video vs. chatbot) help improve decision-making, specifically in a debt-related scenario, even when intuitive thought-processing is used. Two experiments are conducted for this purpose. The first one seeks to evaluate the differences in decision-making according to processing type, deliberative vs. intuitive. The second experiment analyzes whether a brief training input (delivered via video or chatbot) on the basic parameters of sound financial decision-making improves intuitive decision-making.

The results show that individuals can make sound borrowing decisions both through deliberative processing (which requires time and consideration, combined with numerical and financial competence) and by using intuitive processing following a brief computer training session on the logical structure and basic elements of this financial decision, but only when the more interactive learning format of the chatbot is used. Theoretical and practical implications for decision-makers and the design of computerized tools for financial training and learning are discussed.

References:
[1] Hohenberger, C., Lee, C., & Coughlin, J. F. (2019). Acceptance of robo‐advisors: Effects of financial experience, affective reactions, and self‐enhancement motives. Financial Planning Review, 2(2), e1047.
[2] Hwang, G. J., & Chang, C. Y. (2023). A review of opportunities and challenges of chatbots in education. Interactive Learning Environments, 31(7), 4099-4112.
[3] Le, N. T., & Wartschinski, L. (2018). A cognitive assistant for improving human reasoning skills. International Journal of Human-Computer Studies, 117, 45-54.
[4] Nguyen, Q. N., Sidorova, A., & Torres, R. (2022). User interactions with chatbot interfaces vs. Menu-based interfaces: An empirical study. Computers in Human Behavior, 128, 107093.
[5] Wollny, S., Schneider, J., Di Mitri, D., Weidlich, J., Rittberger, M., & Drachsler, H. (2021). Are we there yet? A systematic literature review on chatbots in education. Frontiers in Artificial Intelligence, 4, 654924.

Keywords:

Interactive learning, Chatbot, Dual-process theory, Human–computer interaction, Debt-related decision-making.