This study explores action learning in the context of artificial intelligence (AI)-based assessment of descriptive answers, which can be institutionalized within organizations. It provides a process model and demonstrates its efficacy and validity. This research examines the long-term opportunities in AI usage for development and learning in organizations.
The methodology includes a human-in-the-loop process. The model consists of four sequential stages: AI-driven rubric generation, moderation of rubrics by faculty, AI-based grading and confirmation by faculty using random sampling.
The mean grades awarded by human professor and AI, showed no statistical difference. This validation is a step forward toward reinforcing the usage of AI tools in descriptive evaluation, through an action learning approach.
This study fills a research gap in the area of AI-based assessment methods and serves as a foundation for future research.
Educational organizations (EO) face unpredictable technological changes that demand a high degree of novelty. This study validates a novel AI-based assessment of descriptive answers, the learning from which can improve faculty efficiency. It offers potential to develop further faculty competencies to transform digitally and improve the entire teaching-learning experience. The study has implications for the industry and society as well.
Literature on innovative AI adoption by faculty for grading, in context of the bigger picture i.e. subsequent transformation to other academic areas, is scarce.
