Leon Furze

Mike Perkins, Leon Furze, Jasper Roe et al.

Recent developments in Generative Artificial Intelligence (GenAI) have created a paradigm shift in multiple areas of society, and the use of these technologies is likely to become a defining feature of education in coming decades. GenAI offers transformative pedagogical opportunities, while simultaneously posing ethical and academic challenges. Against this backdrop, we outline a practical, simple, and sufficiently comprehensive tool to allow for the integration of GenAI tools into educational assessment: the AI Assessment Scale (AIAS). The AIAS empowers educators to select the appropriate level of GenAI usage in assessments based on the learning outcomes they seek to address. The AIAS offers greater clarity and transparency for students and educators, provides a fair and equitable policy tool for institutions to work with, and offers a nuanced approach which embraces the opportunities of GenAI while recognising that there are instances where such tools may not be pedagogically appropriate or necessary. By adopting a practical, flexible approach that can be implemented quickly, the AIAS can form a much-needed starting point to address the current uncertainty and anxiety regarding GenAI in education. As a secondary objective, we engage with the current literature and advocate for a refocused discourse on GenAI tools in education, one which foregrounds how technologies can help support and enhance teaching and learning, which contrasts with the current focus on GenAI as a facilitator of academic misconduct.

Leon Furze, Mike Perkins, Jasper Roe et al.

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. Refer to published version for final text.

Jasper Roe, Mike Perkins, Peter Bannister et al.

Artificial intelligence (AI) has disrupted assessment in higher education and accelerated a cycle of compounding performances. Institutional policies demand the demonstration of independent authorship, while commercial AI-enabled services allow students to simulate independent thought and writing. This has led to enhanced institutional surveillance, including AI detectors, which are subsequently circumvented using other technologies. AI humanizers, internet-based services that alter AI-generated text to avoid automated or human detection, are a recent symptom of this performative cycle. Little is known about how these services operate, how they appeal to users, and what they imply for educational assessment and integrity. This paper presents an exploratory, systematic investigation of AI humanizer websites, framed through Goffman's sociological account of dramaturgy. Using a systematic search and custom rubric, we cataloged 55 humanizer sites, assessed their performance of identity, and conducted an in-depth multimodal critical discourse analysis of a purposive sample of three sites. Findings show that humanizers are readily available, offer free and premium paid services, and appear to perform similar functions. These include the deletion and discursive absence of misconduct, the framing of AI humanization as a rational and defensible response to surveillance and flawed detection, and appeals to mystification through advanced technology and implied endorsement by universities and corporations. We argue that humanizer services should be viewed as a diagnostic signal: a legible node in a feedback loop of performative assessment. Disrupting this cycle requires structural assessment reform rather than technological solutionism.