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Quality Assurance Framework For AI-Infused Teaching And Learning

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The American University of Bahrain’s Quality Assurance framework for AI-driven education ensures that AI integration meets academic standards while addressing ethical, privacy, and data accuracy concerns. The framework is being developed by Dr. Nina Abdul Razzak, Director of AUBH’s Director of the Centre for Teaching and Learning Excellence. 

Please share a brief background of your innovation.

Ensuring the quality of higher education is crucial for many reasons. Some of them are:

  • The demonstration of commitment to excellence on the part of higher education institutions (HEIs); 
  • The promotion of HEIs’ image and reputation;
  • The attraction of academically strong students;
  • The alignment of educational objectives and learning outcomes with the needs of the marketplace;
  • The creation of a skilled workforce; and
  • The enhancement of institutional accountability, effectiveness and continuous improvement.

Quality assurance (QA) processes contribute to maintaining and enhancing academic standards. This involves the implementation of a wide range of policies, procedures, practices, quality checks and reviews, and improvements. The quality reviews conducted by HEIs are regular, comprehensive, self-critical, and objective. Included in their wide scope are not just curricula and assessments but also mechanisms and practices related to student support, faculty development, infrastructure enhancement, scientific research, community engagement, and career guidance. These QA efforts help ensure that educational provisions in terms of academic programs on offer remain relevant, current and effective, adapting to the evolving demands of society, the job market, and the latest professional and educational trends internationally. 

The role of QA frameworks is vital in maintaining and enhancing academic standards. They provide a structured approach for HEIs to evaluate and enhance their educational provision against specific sets of standards and indicators. These frameworks typically prescribe for HEIs the key QA mechanisms that they must have in place internally, such as regular reviews and assessments. They also articulate for them the key points emphasized by external evaluations, such as accreditation processes to help them successfully meet the requirements and expectations of relevant QA and accrediting bodies.  

Considering the above, and realizing that the infusion of innovative technologies such as Artificial Intelligence (AI) tools in education, and particularly higher education, has become a fact that cannot be denied. This raises the question of how to assure quality education that integrates AI, in ways that ensure the achievement and enhancement of academic standards. 

There is no doubt that AI in education (AIEd) has many benefits:

  • Personalized learning experiences;
  • The provision of virtual tutoring;
  • Enhanced content delivery;
  • Improved administrative efficiency;
  • The acceleration of data-driven decision-making;
  • The fostering of inclusivity and support for the needs of diverse learners;
  • The facilitation of interdisciplinary research.

However, the truth of the matter remains that AIEd comes with challenges and risks. Some are ethical in nature, such as biases and the risk of cheating and plagiarism, and others that pertain to data privacy and security. There is also the risk of misinformation, as AI systems rely on specific data that they are trained on, which may not always be accurate or up to date. Hence, this makes the quality assurance of AIEd even more pressing.  To answer this question, the innovative idea of developing a QA framework specifically for AIEd was proposed by Dr. Nina Abdul Razzak, an Associate Professor at the College of Arts and Sciences and the Director of the Centre for Teaching and Learning Excellence of the American University of Bahrain (AUBH). 

How was the innovation planned?

The proposed framework was a result of intensive research conducted on the main benefits and challenges of AI in education (AIEd), in addition to research done on the key components of effective QA frameworks. The research was qualitative in design and took the form of a review of the scholarly literature and several focus groups with experienced faculty members from across the GCC. The aim of the focus groups was to elicit the participants’ insights on what they consider to be the main priorities and concerns when it comes to integrating AI into education. The results obtained from both the literature review and the focus groups were used as reference points to base the new framework and to develop its main standards and indicators.  

However, before conducting this research, the plan for proposing the innovative idea (i.e., the QA framework for AIEd) included the initial step of scoping the educational literature to ensure that no such framework or anything similar already existed. The results of the scoping showed that most of the available guidance on the AIEd topic is in the form of advisory notes, guidelines, or roadmaps that are independent of a specific system or framework. While documents such as these are valuable and informative, they, nevertheless, lack the authoritative and imperative nature of QA frameworks and established standards in education, which draw their authority from several key sources.   By establishing that no clear, authoritative and full-fledged QA framework for AIEd is yet in place, there was sufficient justification for why a comprehensive QA framework is needed and recommended to provide structure and guidance to the process of AI integration in higher education. The justification for the need for the framework was also amplified by the mere fact that AIEd, both as a concept and practice, is multifaceted and complex, and is, hence, in need of structure and regulation.

What were the challenges faced during implementation?

Developing the framework was not challenge-free; as, to be effective, the framework had to be as comprehensive as possible, which was cognitively strenuous due to having to think of so many details at one time. Additionally, it was a must that the framework be consistent with typical QA frameworks used to evaluate higher education provision and, so, effort was placed in ensuring that the product could be used to supplement existing frameworks at both the national and international levels.

Give us a brief assessment of your results.

Even though the proposed framework has not yet been practically implemented, it has been subjected to evaluation as a result/product of a long process of planning and research. The evaluation of the framework has been carried out on three different levels.  At the first level, it was peer-reviewed by a colleague at AUBH, who is an expert in QA of higher education. At the second level, the framework was presented at an international educational conference in July 2024, where feedback was provided by the conference attendees. Finally, the framework was presented at a local educational excellence forum in early October 2024 that comprised educators from different institutions and ministries in Bahrain. The forum attendees provided feedback on the potential of the framework as a practical tool that can be used by both HEIs and QA and accreditation bodies to guide and regulate AIEd and maintain its academic standards.  All elicited feedback at the different levels was positive, highlighting the framework’s promise and recommending its adoption by policymakers at the national level, to be publicly circulated to the relevant stakeholders and translated into practical applications.

In hindsight, what were the most valuable lessons learned while implementing the innovation? Could things have been done differently?

Looking back, the only thing that could have been done differently in the process of developing the framework was to involve a bigger number of educators as participants in the research, the results of which were used to base the framework. Efforts could also have been exerted from the initial stages of development to try to approach the relevant policymakers and sell the idea of the framework to them.

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