Teaching Reform and Practice of the Asset Valuation Course Enabled by Digital Intelligence

Abstract

Digital intelligence technologies have profoundly reshaped the methodologies and operational paradigms of the asset valuation industry, driving higher education to transform asset valuation teaching from traditional knowledge delivery toward fostering students' data-driven thinking and competencies in applying intelligent technologies. Against this backdrop, this study systematically examines the major challenges facing the current Asset Valuation course, including outdated curriculum content, limited pedagogical approaches, insufficient practical training, and inflexible assessment methods. Building on this analysis, a four-dimensional digital-intelligence-enabled teaching reform framework is proposed, encompassing curriculum reconstruction, blended instruction, simulation-based learning, and intelligent assessment. Two representative teaching cases-the Virtual Simulation Experiment for Mass Real Estate Valuation and the Data Asset Valuation Simulation-are employed to demonstrate the implementation process and evaluate students' learning outcomes. Quantitative comparisons between an experimental class and a control class across multiple performance dimensions reveal that the proposed teaching model significantly reduces valuation errors, enhances students' ability to integrate and analyze multisource data, and strengthens their confidence in handling complex valuation scenarios. These findings provide a replicable pedagogical framework and empirical evidence to support the digital transformation of core business and economics curricula.