Abstract Generative AI (GenAI) can create tremendous value for organizations; however, many organizations struggle to hire professionals with the knowledge needed to integrate GenAI into their operations. Business schools have the opportunity to develop students who can meet this requirement. We propose a framework that equips students with a structured approach to using GenAI to solve business problems and make decisions, along with a clear awareness of its challenges and limitations. This framework can serve as an initial conceptual model to help students understand the critical role of GenAI in business operations. Having this foundation will later help them develop more advanced GenAI skills and adapt to emerging applications, including agentic AI systems. Our framework is based on the premise that understanding GenAI's role in business operations is fundamental, with business operations defined as the transformation of inputs into products and services for customers. Our framework highlights GenAI's vital role in supporting problem‐solving and managerial decision‐making in this transformation process. We evaluated the framework's usefulness with business students. We found that students solved business problems more effectively after learning the framework, were more confident, and reported positive learning experiences. We discussed the framwork with several business executives and faculty. They responded that they liked the broad framework as a good starting point for students to learn about the benefits and challenges of using GenAI in business, and the importance of contextual knowledge in making good decisions with GenAI.
Abstract This teaching brief discusses the development and implementation of MBABot, a custom GPT‐based AI tool designed to support writing‐intensive decision sciences course titled Delivering Business Value Through Information Systems . This MBA‐level course examines the role of business leaders in evaluating information systems and information technology to deliver business value and sustain competitive advantage. Recognizing the importance of timely, structured feedback in developing analytical reasoning and professional decision writing, the initiative addresses instructional constraints such as large class sizes and limited grading time. MBABot integrates retrieval‐augmented‐generation (RAG) with course‐specific materials to provide context‐aware feedback aligned with explicit grading criteria and structured decision tools (e.g., the DECIDE model and a weighted decision matrix). Survey‐based evidence suggests MBABot supported several debiasing strategies, particularly problem decomposition, consensus building, and motivation to improve. Students also reported limitations, including redundant or unnecessary feedback that reduced trust in the tool. The brief underscores both the promise and constraints of AI‐enhanced feedback systems in decision sciences education, emphasizing responsible implementation and pedagogical alignment.
ABSTRACT In business schools, photographic images are widely used in teaching material but not in testing. To enhance student preparation for business decision making, it is desirable to provide real‐world context with photos not only for learning but also for assessment. In this teaching brief, we share our experiences with teaching warehousing knowledge for supply chain management and logistics concepts effectively using multimedia testing with photos. Using a modified Bloom's taxonomy as a framework and defining a question structure, we discuss photo‐question examples from actual exams. We provide educator guidelines for photo sources, technical improvements, assessment features, and question clarity. We detail student perspectives and their overwhelming support for photo assessments over text‐only assessments. We present the challenges and advantages of the method for the instructor and research opportunities.
Abstract This paper introduces a competitive, interactive classroom game designed to teach predictive analytics by simulating the end‐to‐end process of building machine learning models. The game is conducted in two rounds engaging students in tasks such as variable selection, model building, parameter calibration, and performance evaluation, all within the context of optimizing a fundraising campaign. Played in undergraduate and MBA programs, the game emphasizes hands‐on learning, teamwork, and decision‐making under time constraints. Analysis of model performance across two rounds showed that 74.3% of student teams improved their results after the debriefing session, demonstrating the game's effectiveness in reinforcing predictive modeling concepts through iterative feedback. Results from an anonymous survey (n = 78) demonstrated high levels of engagement and learning outcomes, with 93.6% of students recommending the game for future use. Key findings include a mean score of 2.10 (on a scale of –3 to +3) for enhancing the learning experience through competition, 1.94 for increasing interest in analytics careers, and 1.90 for encouraging deeper critical thinking compared to traditional exercises. These outcomes highlight the game's effectiveness in bridging theoretical concepts with real‐world applications, encouraging critical thinking, and preparing students for analytics‐focused careers.
Abstract This article presents a role‐playing activity designed to address a common challenge in analytical courses, where students rely on software outputs without fully understanding the underlying analytical concepts. The activity highlights the continued significance of human analysts in interpreting results, guiding analysis, and leveraging domain expertise, skills that persist as essential even with advancements in AI. The exercise simulates the software maintenance stage, requiring students to switch between user and developer roles to identify and fix software bugs. This structure promotes both technical proficiency and soft skill development, particularly in communication and collaborative problem‐solving. Student feedback, including qualitative reflections and quantitative survey results, demonstrated significant learning gains, with over 80% reporting improvements in analytical reasoning, debugging skills, and communication abilities. The activity aligns with Education 4.0 principles by promoting interdisciplinary collaboration, adaptive learning, and experiential engagement. The article concludes with practicl recommendations for integrating such activities into analytical coursework, emphasizing the importance of clear initial guidance and reflective debriefing to maximize learning outcomes.
