Leon Furze

Last year, I co-authored a paper with Dr Mike Perkins, Dr Jasper Roe, and Associate Professor Jason MacVaugh in which we proposed the AI Assessment Scale (AIAS). The first paper, available as a preprint on arXiv, outlines the rationale for a scaled approach to using AI versus a “use/don’t use” or a blanket ban. Since that initial publication, I also produced a set of resources on how the scale can be used across subjects, which can be accessed here:

This week, we published the second paper focusing on the AIAS and how it was implemented at British University Vietnam. The paper, titled “The AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI supported assessment”, has just been released as a preprint on arXiv.

The AIAS emerged from discussions with colleagues about the need for a more nuanced approach to integrating Generative Artificial Intelligence (GenAI) into assessments. We felt that a simple yes/no to using GenAI would be unsustainable and that we needed a framework that could account for the rapid advancements in the technology while also honouring our students’ integrity and creativity. The AIAS was designed from the start to be flexible, with the understanding that multimodal GenAI will impact all disciplines.

In the paper, we discuss the global responses to GenAI in higher education, which have often focused on banning or restricting the use of these tools. However, as the understanding of GenAI capabilities grew, institutions have started to adopt more nuanced approaches. We argue that the current discourse surrounding GenAI in education overemphasises academic misconduct, both minimising more pressing ethical concerns and neglecting the potential benefits of these technologies.

The case study of the AIAS implementation at British University Vietnam (BUV) forms the core of our paper. We detail the process of introducing the AIAS, from the initial discussions and policy adjustments to the launch and implementation of the framework. The results of the pilot study are promising, with a significant reduction in academic misconduct cases related to GenAI, an increase in student attainment, and a rise in module passing rates.

We also discuss the shift in pedagogical practices following the implementation of the AIAS, with faculty members incorporating GenAI tools into their modules, and students producing innovative multimodal submissions, including students for whom English is not their first language. This suggests that the framework not only addresses concerns about academic integrity but also creates new avenues for student creativity and engagement.

While the initial results are encouraging, we acknowledge the limitations of the study, including its scope within a single institution and the need for further validation through larger, more diverse studies. We also discuss the ethical implications of GenAI in education, such as bias and fairness, privacy and data security, and intellectual property rights.

We argue that the AIAS offers a practical, flexible, and adaptable framework for integrating GenAI into educational assessments. By adopting an approach that embraces the opportunities of GenAI while recognising its limitations, educators can support students in developing the skills needed for an increasingly AI-driven world.

Paper abstract:

The rapid adoption of Generative Artificial Intelligence (GenAI) technologies in higher education has raised concerns about academic integrity, assessment practices, and student learning. Banning or blocking GenAI tools has proven ineffective, and punitive approaches ignore the potential benefits of these technologies. This paper presents the findings of a pilot study conducted at British University Vietnam (BUV) exploring the implementation of the Artificial Intelligence Assessment Scale (AIAS), a flexible framework for incorporating GenAI into educational assessments. The AIAS consists of five levels, ranging from ‘No AI’ to ‘Full AI’, enabling educators to design assessments that focus on areas requiring human input and critical thinking. Following the implementation of the AIAS, the pilot study results indicate a significant reduction in academic misconduct cases related to GenAI, a 5.9% increase in student attainment across the university, and a 33.3% increase in module passing rates. The AIAS facilitated a shift in pedagogical practices, with faculty members incorporating GenAI tools into their modules and students producing innovative multimodal submissions. The findings suggest that the AIAS can support the effective integration of GenAI in HE, promoting academic integrity while leveraging the technology’s potential to enhance learning experiences.

The AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI supported assessment https://doi.org/10.48550/arXiv.2403.14692

If you’re interested in learning more about the AIAS and our pilot study at BUV, I recommend reading the full paper on arXiv:

https://arxiv.org/abs/2403.14692

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