Responsible AI-Readiness in Higher Education: Validating a Dual-Model Framework for Faculty Development

Aim/Purpose: This study validates a responsible artificial intelligence (AI) framework designed to strengthen AI-readiness among higher education faculty in India. Background: With the increasing use of AI in education, faculty require structured support; however, faculty development models for responsible AI integration remain limited. Methodology: A mixed-methods pilot study with ten humanities and performing arts faculty used a dual-model evaluation approach. Three sessions from the framework were implemented, and data were collected through knowledge-attitude-practice (KAP) surveys, cognitive-affective-psychomotor (CAP) performance rubrics, and reflective responses. Contribution: This study validates a dual-model framework for faculty development regarding responsible AI readiness. Findings: The adapted KAP survey demonstrated strong reliability, with higher AI knowledge associated with more positive attitudes. However, knowledge did not consistently translate into practice, highlighting the need for structured hands-on learning. CAP-based performance assessments and reflections indicated improved ethical awareness, critical engagement, and foundational AI-integration skills. Recommendations for Practitioners: Institutions should embed structured AI-training for faculty, with authentic instructional tasks. Recommendation for Researchers: Future research should test this approach across larger and more diverse institutional contexts. Impact on Society: Developing AI-ready faculty can foster ethical and future-focused learning environments. Future Research: Future studies should expand across disciplines and examine longer-term outcomes.