AI-enhanced legal pedagogy: a case study of generative AI integration in property law courses

Abstract The rapid emergence of generative artificial intelligence (GenAI) presents transformative opportunities for legal education, yet empirical research examining its pedagogical integration remains limited. This study investigates the impact of GenAI integration on legal reasoning development and learning outcomes in property law courses within Chinese higher education. Grounded in constructivist learning theory, the Technology Acceptance Model (TAM), and the Technological Pedagogical Content Knowledge (TPACK) framework, this research develops an integrated AI-Enhanced Legal Pedagogy Framework to guide effective technology integration in legal education. Employing a mixed-methods case study design, the study engaged 180 law students in a semester-long intervention using iFlytek Spark, combining quantitative assessments through pre-post legal reasoning tests and TAM-based surveys with qualitative data from interviews, reflection journals, and classroom observations. Results indicate significant associations between GenAI-enhanced instruction and improvements in legal reasoning abilities (between-group Cohen's d = 1.17), though this effect size warrants cautious interpretation given the non-randomized design and potential confounding from instructor enthusiasm and motivational differences. Technology acceptance was moderate to strong (R 2 = .59 for behavioral intention). Structural equation modeling reveals that perceived usefulness (β = .52) and instructor guidance (β = .23) were associated with learning outcomes. Qualitative findings highlight GenAI's potential role as a cognitive scaffolding tool while identifying challenges in preventing over-reliance. This study offers, theoretically, a preliminary conceptual integration of existing frameworks tailored to GenAI-enhanced legal education, and, practically, evidence-based strategies for legal educators to redesign pedagogy and assessment in the AI era.