Leveraging AI for Internal Quality Assurance: Current Trends and Future Outlook

Abstract

The origins of artificial intelligence trace back to the 1950s, John McCarthy organized a two-month workshop at Dartmouth College in the United States. It was in the proposal for this 1956 workshop that McCarthy first introduced the term “artificial intelligence.”. As a formal field of academic inquiry, it was established in 1956 during the Dartmouth Conference. Quality assurance departments of higher education institutions are beginning to explore the potential of artificial intelligence in enhancing implementation of internal mechanisms. While AI holds considerable promise for enhancing the accuracy, efficiency and transparency of quality assurance processes, there remains a lack of comprehensive understanding regarding how these tools are utilized. The study aims to explore the current level of adoption of AI-based tools within quality assurance processes and to assess their perceived effectiveness, identify the key challenges, analyze the perceived risks associated with over-reliance on AI, understand the types of institutional support required to facilitate a successful and responsible integration of AI tools, explore expectations for the future role of AI in quality assurance. As technologies based on artificial intelligence continue to evolve, it is important to assess how higher education institutions are preparing for the integration of more advanced AI-driven solutions and what steps they are taking to overcome the potential barriers to successful implementation. A mixed-methods approach, incorporating both qualitative and quantitative research methods, was employed in this study. The survey was distributed to academic staff, administrative personnel and students. A total of 48 representatives from 28 universities across Georgia and Europe participated in the research. The research reveals evolving landscape of integration artificial intelligence into internal quality assurance mechanisms. While a significant portion of respondents indicate that AI tools are not yet utilized in their institutions, there is a growing recognition of their potential effectiveness, particularly in enhancing efficiency . Concerns over data reliability, software costs, ethical compliance, personnel readiness, and the interpretability of AI-generated outcomes illustrate that the road to integration is neither straightforward nor universally embraced. Additionally, the perceived risks, such as over-reliance on AI, loss of human oversight, data security vulnerabilities, and resistance from institutional staff, highlight the need for a balanced and thoughtful approach to digital transformation in quality assurance. The findings of this study are particularly relevant for higher education policymakers, quality assurance professionals and staff involved in the design and implementation of internal quality assurance systems.

Ähnliche Arbeiten