The GATE Framework: A Generative AI-Enhanced Active Learning Environment for Tourism Communication Competency Development

Generative artificial intelligence (GenAI) is being adopted in tourism and hospitality education more quickly than the pedagogical guidance needed to use it well. This study developed the GATE Framework, a GenAI-enhanced active learning environment intended to develop tourism communication competency, and examined its appropriateness through expert review. The work comprised two stages within the first research phase. An evidence-informed synthesis established three connected layers: Inputs, Core Process Components, and Outcomes. At the center of the Process Layer is an iterative Explore-Generate-Apply-Reflect cycle, supported by human-AI interaction, authentic tourism tasks, peer and instructor participation, reflection, and adaptive feedback. In the second stage, nine experts in tourism and hospitality, curriculum and instruction, educational technology, AI in education, and active learning then evaluated the framework using an 18-item, five-point scale and four open-ended questions. The overall rating was very high (x̄ = 4.54, S.D. = 0.58), and the five domain means ranged from 4.52 to 4.56. The experts gave their strongest ratings to the framework's theoretical alignment, relevance to tourism communication learning, and suitability for further development. Ratings remained favorable but were lower for the definition of individual roles and the practical sequence of the learning process. In response, the framework was revised to clarify scaffolding and human oversight, strengthen privacy and transparency provisions, and give greater weight to tourism content rooted in local culture. The resulting framework provides the conceptual basis for GATE-SLE, where GenAI assists disciplinary learning without displacing human judgment.