Institutional Quality in AI the Era: IQAC Opportunities and Challenges in Adopting Analytical Tools for improvements

The emergence of quality assurance (QA) in higher education through the use of artificial intelligence (AI) analytical tools is a challenging, but transformative phenomenon. Yet, as much as there is the sense of urgency, the IQACs are also being burdened with the responsibility in not only tapping into these transformative analytic synergies, but also with a range of requirements which are couched in organizational, technical as well as ethical terms. This paper discusses the opportunities and challenges faced by IQACs in Indian institutions of higher learning in enhancing AI analytical tools to ensure continuous quality improvement. The study is a convergent parallel mixed-methods design based on 50 IQAC coordinators and quantitative survey data and 10 in-depth interviews and six institutional case study as a source of qualitative input. Data collection instrument: A validated 20-item structured questionnaire relying on the Technology Acceptance Model (TAM), semi-structured interview guides, field notes of observations and review of documents. Synthesis is done with thematic analysis, statistical modelling and integration matrices. The preliminary findings indicate the variation in the level of AI tool adoption in various kinds of institutions, where perceived usefulness and digital readiness have been established as the determinants. The findings give a contextualized perspective of AISQ in limited resource-based academic settings.