Readiness and responsible practices
Academics’ perspectives on AI integration through the Cair framework
Bibliographic Data
| ID | 22423716 |
|---|---|
| Authors | Zhao Cheng (0000-0003-4252-2141, Vrije Universiteit Brussel), James O G Thewissen (0000-0002-6600-3023, Louvain-la-Neuve), James Thewissen (Shanghai University), Meijie Bi (0000-0003-2328-3712, Zhejiang Normal University, corresponding author), Chang Zhu (0000-0002-0057-275X, Vrije Universiteit Brussel) |
| Year | 2026 |
| Pages | 1-16 |
| Publication date | 2026-03-16 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Innovations in Education and Teaching International (JOURNAL) |
| Journal identifiers | ISSN: 1470-3297 • E-ISSN: 1470-3300 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/14703297.2026.2644340 |
| OpenAlex | W7137121716 |
| Language | EN |
| References cited | 36 |
Artificial Intelligence (AI) has emerged as a transformative force in higher education (HE). However, the integration of AI raises ethical concerns. Guided by a responsible innovation framework, this study aims to illuminate the barriers and responsible practices perceived by academics from HE. Data were collected through participants’ discussions in a webinar organised by an international research network based in Europe. The CAIR framework (capacity, attitudes, institutional support, real-world use) is proposed in this study according to existing literature, this study adopts a content analysis approach using MAXQDA2022, with qualitative data coding guided by the CAIR framework. The findings identified key barriers and responsible practices. The CAIR framework was further translated into practical tools, including a checklist for course teams, a policy template, a staff development pathway, and an adaptable ‘traffic-light’ rubric for responsible AI integration, which can be helpful in leading AI innovation models that are ethical and future-ready.
Accountability · Context effect · Higher education · Perception · Qualitative research · Teaching method · Big Data and Business Intelligence · Ethics and Social Impacts of AI · Research Data Management Practices
Phenomenological research methods
Ethics of AI in Education
What is AI Literacy? Competencies and Design Considerations
Artificial intelligence in education
Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions
Digital competence and digital literacy in higher education research
Chatting and cheating
Attitudes of faculty members in Palestinian universities toward employing artificial intelligence applications in higher education
Academics’ Leadership Styles and Their Motivation to Participate in a Leadership Training Program in the Digital Era
Historical threads, missing links, and future directions in AI in education
Educational leadership styles and practices perceived by academics
Staying ahead with generative artificial intelligence for learning
Academics' motivation for joining an educational leadership training programme and their perceived effectiveness
Perceived changes in transformational leadership
Systematic review of research on artificial intelligence applications in higher education – where are the educators
Are We There Yet? Data Saturation in Qualitative Research
Qualitative research
Naturalistic inquiry
Member Checking
Three Approaches to Qualitative Content Analysis
| Citation velocity | historical |
|---|---|
| Highly cited | No |