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This article investigates the application of generative artificial intelligence (GenAI) in experiential learning for authentic assessment in higher education. Recognized for its human-like content generation, GenAI has garnered widespread interest, raising concerns regarding its reliability, ethical considerations and overall impact. The purpose of this study is to explore the transformative capabilities and limitations of GenAI for experiential learning.

The study uses “thing ethnography” and “incremental prompting” to delve into the perspectives of ChatGPT 3.5, a prominent GenAI model. Through semi-structured interviews, the research prompts ChatGPT 3.5 on critical aspects such as conceptual clarity, integration of GenAI in educational settings and practical applications within the context of authentic assessment. The design examines GenAI’s potential contributions to reflective thinking, hands-on learning and genuine assessments, emphasizing the importance of responsible use.

The findings underscore GenAI’s potential to enhance experiential learning in higher education. Specifically, the research highlights GenAI’s capacity to contribute to reflective thinking, hands-on learning experiences and the facilitation of genuine assessments. Notably, the study emphasizes the significance of responsible use in harnessing the capabilities of GenAI for educational purposes.

This research showcases the application of GenAI in operations management education, specifically within lean health care. The study offers insights into its capabilities by exploring the practical implications of GenAI in a specific educational domain through thing ethnography and incremental prompting. Additionally, the article proposes future research directions, contributing to the originality of the work and opening avenues for further exploration in the integration of GenAI in education.

The concept of sustainable performance is gaining attention from researchers all over the world for the betterment of society as a whole. Pollution and climate change has instigated an awareness to switch to sustainable built environment. The present research aims to analyze the indicators of adoption of smart buildings and their impact on dimensions of sustainability, namely, economic, social and environmental performance.

Data was collected from 332 respondents staying in smart buildings. Structural equation modeling was applied to analyze the data.

The results of the study indicated adoption of smart buildings depicted a stronger perceived sustainable environmental and economic performance while social performance emerged as a weak outcome variable as compared to the other two performances.

The study thus has tremendous implications for construction companies so that they may design smart buildings by adequately using artificial intelligence. To the best of the authors’ knowledge, this research is one of the initial studies to understand the perception of residents of smart buildings using smart technology in India.