Selection and Resource Allocation Strategies for Chatbot Technologies in Higher Education: An Optimization Model Approach - Suhan Wu; Min Luo (IEEE Access)
Chatbot technologies are increasingly integral to higher education, offering significant potential to enhance teaching effectiveness and student engagement. This study explores the strategic integration of chatbot technologies, focusing on optimal selection and resource allocation to maximize educational efficacy. We developed a mathematical optimization model and introduced the “Relative Net Utility Differential” (RNUD) as a pivotal metric to facilitate the decision-making process for chatbot technology selection and resource distribution. Our findings reveal that when RNUD values for all chatbots fall within a specified range, the model efficiently determines the optimal resource allocations, providing clear analytical solutions for each technology. Additionally, we devised an algorithm for scenarios where RNUD values vary significantly, effectively isolating optimal solutions by excluding suboptimal choices. Numerical analysis confirms that this algorithm maintains robust performance and high computational efficiency, particularly when the chatbot count does not exceed 20. This efficiency persists across various distributions of chatbots’ technical attributes, and increasing the number of candidate chatbots generally enhances overall educational utility. This suggests that a diverse technological environment can lead to more tailored and effective educational interventions. Moreover, our study highlights a competitive interaction among chatbots: those with superior technical attributes tend to receive more resources. However, when the technical attributes of one chatbot improve, the resources allocated to others decrease, and vice versa. These insights underscore the importance of not only providing more sophisticated chatbots but also carefully managing their interactions to maximize overall educational utility.