Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Rawat, Sanskriti; Ashton-Bell, Robert Linton Tavis Title A name given to the resource RESPONSIBLE AI-READINESS IN HIGHER EDUCATION: VALIDATING A DUAL-MODEL FRAMEWORK FOR FACULTY DEVELOPMENT Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Information Technology Education: Research;Volume;24;pp.1-20 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.28945/5654" target="_blank" rel="noreferrer noopener">https://doi.org/10.28945/5654</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105024532492?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105024532492?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Rawat S., Christ University, Bangalore, India; Ashton-Bell R.L.T., Christ University, Bangalore, India Description An account of the resource 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. Recommendations 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. (2025), (Informing Science Institute). All rights reserved. Subject The topic of the resource AI-readiness; artificial intelligence; dual-model validation; faculty development Publisher An entity responsible for making the resource available Informing Science Institute Relation A related resource ISSN: 15479714; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online