arXiv subjects

Mike Perkins

Publications and source records attributed to Mike Perkins.

At least 19 recordsLinked to original sources

arXiv2026

'A bit of chaos and madness': The AI Assessment Scale and the work of assessment reform

Generative artificial intelligence (GenAI) has intensified pressure on universities to redesign assessment while maintaining integrity, equity, and validity. Structured frameworks such as the Artificial Intelligence Assessment Scale (AIAS) offer one response, but evidence of how staff experience their implementation remains limited. This qualitative study examines AIAS implementation at a private international university in Vietnam and a public university in the United Kingdom. Data from five focus groups with 30 academic staff were analysed using hybrid thematic analysis, with Critical AI Literacy used as a sensitising concept. Six themes were developed: recognising and integrating AI, facilitating conditions, building capacity, pathways to adoption, ethics in practice, and reframing pedagogy. Staff valued the AIAS as a shared language for legitimising GenAI use, clarifying boundaries, and prompting reflection on assessment design. However, implementation was shaped by governance, tool access, staff confidence, workload, integrity concerns, disciplinary context, and alignment with learning outcomes. The findings show that the AIAS could prompt authentic assessment design and student engagement, but may become a compliance layer when disconnected from learning outcomes, disciplinary context, and staff capacity. This study contributes empirical evidence on the institutional conditions through which GenAI assessment frameworks move from policy adoption to pedagogical enactment.

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arXiv2026

Dramaturgies of Deception: AI Humanizers and the Performance of Legitimacy in Higher Education Assessment

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.

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arXiv2025

Assessment Twins: A Protocol for AI-Vulnerable Summative Assessment

Generative Artificial Intelligence (GenAI) is reshaping higher education and raising pressing concerns about the integrity and validity of higher education assessment. While assessment redesign is increasingly seen as a necessity, there is a relative lack of literature detailing what such redesign may entail. In this paper, we introduce assessment twins as an accessible approach for redesigning assessment tasks to enhance validity. We use Messick's unified validity framework to systematically map the ways in which GenAI threaten content, structural, consequential, generalisability, and external validity. Following this, we define assessment twins as two deliberately linked components that address the same learning outcomes through different modes of evidence, scheduled closely together to allow for cross-verification and assurance of learning. We argue that the twin approach helps mitigate validity threats by triangulating evidence across complementary formats, such as pairing essays with oral defences, group discussions, or practical demonstrations. We highlight several advantages: preservation of established assessment formats, reduction of reliance on surveillance technologies, and flexible use across cohort sizes. To guide implementation, we propose a three-step design process: identifying vulnerabilities, aligning outcomes, selecting complementary tasks, and developing interdependent marking schemes. We also acknowledge the challenges, including resource intensity, equity concerns, and the need for empirical validation. Nonetheless, we contend that assessment twins represent a validity-focused response to GenAI that prioritises pedagogy while supporting meaningful student learning outcomes.

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arXiv2025

A Peek Behind the Curtain: Using Step-Around Prompt Engineering to Identify Bias and Misinformation in GenAI Models

Step-around prompting is a form of adversarial prompt engineering in which a user strategically reframes, sequences, or contextualises requests to test whether a generative AI model's safety guardrails, alignment mechanisms, or bias mitigations can be undermined, inconsistently applied, or bypassed outright. This study examines this technique through the lens of academic ethics, situating it as a tool that has a clear impact on academic integrity, responsible conduct of research, duty of care to students, and institutional oversight of GenAI use in higher education. We argue that step-around prompting is one tool within the wider practice of audit, red-teaming, and institutional evaluation, and that its main value lies in documenting how representational, cultural, linguistic, disciplinary, and misinformation-related biases may appear across student-facing and research-facing uses of GenAI. To show why the ethical governance of this practice is required, we provide two illustrative examples of the technique in action, demonstrating how easily guardrails can be circumvented and what is at stake when they are. We clarify which bias categories are in scope and identify who should use the method and for what purposes. We conclude with an operational ethics-and-governance framework for controlled academic application, organised as two pillars (technical safeguards and ethical governance) and enacted through a decision and audit cycle that scales oversight to potential risk, grounded in harm minimisation, duty of care, transparency, proportionality, responsible disclosure, legal and contractual compliance, and student protection.

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arXiv2025

To Deepfake or Not to Deepfake: Higher Education Stakeholders' Perceptions and Intentions towards Synthetic Media

Advances in deepfake technologies, which use generative artificial intelligence (GenAI) to mimic a person's likeness or voice, have led to growing interest in their use in educational contexts. However, little is known about how key stakeholders perceive and intend to use these tools. This study investigated higher education stakeholder perceptions and intentions regarding deepfakes through the lens of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Using a mixed-methods approach combining survey data (n=174) with qualitative interviews, we found that academic stakeholders demonstrated a relatively low intention to adopt these technologies (M=41.55, SD=34.14) and held complex views about their implementation. Quantitative analysis revealed adoption intentions were primarily driven by hedonic motivation, with a gender-specific interaction in price-value evaluations. Qualitative findings highlighted potential benefits of enhanced student engagement, improved accessibility, and reduced workload in content creation, but concerns regarding the exploitation of academic labour, institutional cost-cutting leading to automation, degradation of relationships in education, and broader societal impacts. Based on these findings, we propose a framework for implementing deepfake technologies in higher education that addresses institutional policies, professional development, and equitable resource allocation to thoughtfully integrate AI while maintaining academic integrity and professional autonomy.

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arXiv2025

GenAI as Digital Plastic: Understanding Synthetic Media Through Critical AI Literacy

This paper introduces the conceptual metaphor of 'digital plastic' as a framework for understanding the implications of Generative Artificial Intelligence (GenAI) content through a multiliteracies lens, drawing parallels with the properties of physical plastic. Similar to its physical counterpart, GenAI content offers possibilities for content creation and accessibility while potentially contributing to digital pollution and ecosystem degradation. Drawing on multiliteracies theory and Conceptual Metaphor Theory, we argue that Critical Artificial Intelligence Literacy (CAIL) must be integrated into educational frameworks to help learners navigate this synthetic media landscape. We examine how GenAI can simultaneously lower the barriers to creative and academic production while threatening to degrade digital ecosystems through misinformation, bias, and algorithmic homogenization. The digital plastic metaphor provides a theoretical foundation for understanding both the affordances and challenges of GenAI, particularly in educational contexts, where issues of equity and access remain paramount. Our analysis concludes that cultivating CAIL through a multiliteracies lens is vital for ensuring the equitable development of critical competencies across geographical and cultural contexts, especially for those disproportionately vulnerable to GenAI's increasingly disruptive effects worldwide.

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arXiv2025

From Assessment to Practice: Implementing the AIAS Framework in EFL Teaching and Learning

Recent advances in Generative AI (GenAI) are transforming multiple aspects of society, including education and foreign language learning. In the context of English as a Foreign Language (EFL), significant research has been conducted to investigate the applicability of GenAI as a learning aid and the potential negative impacts of new technologies. Critical questions remain about the future of AI, including whether improvements will continue at such a pace or stall and whether there is a true benefit to implementing GenAI in education, given the myriad costs and potential for negative impacts. Apart from the ethical conundrums that GenAI presents in EFL education, there is growing consensus that learners and teachers must develop AI literacy skills to enable them to use and critically evaluate the purposes and outputs of these technologies. However, there are few formalised frameworks available to support the integration and development of AI literacy skills for EFL learners. In this article, we demonstrate how the use of a general, all-purposes framework (the AI Assessment Scale) can be tailored to the EFL writing and translation context, drawing on existing empirical research validating the scale and adaptations to other contexts, such as English for Academic Purposes. We begin by engaging with the literature regarding GenAI and EFL writing and translation, prior to explicating the use of three levels of the updated AIAS for structuring EFL writing instruction which promotes academic literacy and transparency and provides a clear framework for students and teachers.

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arXiv2024

Research Integrity and GenAI: A Systematic Analysis of Ethical Challenges Across Research Phases

Background: The rapid development and use of generative AI (GenAI) tools in academia presents complex and multifaceted ethical challenges for its users. Earlier research primarily focused on academic integrity concerns related to students' use of AI tools. However, limited information is available on the impact of GenAI on academic research. This study aims to examine the ethical concerns arising from the use of GenAI across different phases of research and explores potential strategies to encourage its ethical use for research purposes. Methods: We selected one or more GenAI platforms applicable to various research phases (e.g. developing research questions, conducting literature reviews, processing data, and academic writing) and analysed them to identify potential ethical concerns relevant for that stage. Results: The analysis revealed several ethical concerns, including a lack of transparency, bias, censorship, fabrication (e.g. hallucinations and false data generation), copyright violations, and privacy issues. These findings underscore the need for cautious and mindful use of GenAI. Conclusions: The advancement and use of GenAI are continuously evolving, necessitating an ongoing in-depth evaluation. We propose a set of practical recommendations to support researchers in effectively integrating these tools while adhering to the fundamental principles of ethical research practices.

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arXiv2024

The AI Assessment Scale Revisited: A Framework for Educational Assessment

Recent developments in Generative Artificial Intelligence (GenAI) have created significant uncertainty in education, particularly in terms of assessment practices. Against this backdrop, we present an updated version of the AI Assessment Scale (AIAS), a framework with two fundamental purposes: to facilitate open dialogue between educators and students about appropriate GenAI use and to support educators in redesigning assessments in an era of expanding AI capabilities. Grounded in social constructivist principles and designed with assessment validity in mind, the AIAS provides a structured yet flexible approach that can be adapted across different educational contexts. Building on implementation feedback from global adoption across both the K-12 and higher education contexts, this revision represents a significant change from the original AIAS. Among these changes is a new visual guide that moves beyond the original traffic light system and utilises a neutral colour palette that avoids implied hierarchies between the levels. The scale maintains five distinct levels of GenAI integration in assessment, from "No AI" to "AI Exploration", but has been refined to better reflect rapidly advancing technological capabilities and emerging pedagogical needs. This paper presents the theoretical foundations of the revised framework, provides detailed implementation guidance through practical vignettes, and discusses its limitations and future directions. As GenAI capabilities continue to expand, particularly in multimodal content generation, the AIAS offers a starting point for reimagining assessment design in an era of disruptive technologies.

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arXiv2024

Funhouse Mirror or Echo Chamber? A Methodological Approach to Teaching Critical AI Literacy Through Metaphors

As educational institutions grapple with teaching students about increasingly complex Artificial Intelligence (AI) systems, finding effective methods for explaining these technologies and their societal implications remains a major challenge. This study proposes a methodological approach combining Conceptual Metaphor Theory (CMT) with UNESCO's AI competency framework to develop Critical AI Literacy (CAIL). Through a systematic analysis of metaphors commonly used to describe AI systems, we develop criteria for selecting pedagogically appropriate metaphors and demonstrate their alignment with established AI literacy competencies, as well as UNESCO's AI competency framework. Our method identifies and suggests four key metaphors for teaching CAIL. This includes GenAI as an echo chamber, GenAI as a funhouse mirror, GenAI as a black box magician, and GenAI as a map. Each of these seeks to address specific aspects of understanding characteristics of AI, from filter bubbles to algorithmic opacity. We present these metaphors alongside interactive activities designed to engage students in experiential learning of AI concepts. In doing so, we offer educators a structured approach to teaching CAIL that bridges technical understanding with societal implications. This work contributes to the growing field of AI education by demonstrating how carefully selected metaphors can make complex technological concepts more accessible while promoting critical engagement with AI systems.

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arXiv2024

Generative AI in Self-Directed Learning: A Scoping Review

This scoping review examines the current body of knowledge at the intersection of Generative Artificial Intelligence (GenAI) and Self-Directed Learning (SDL). By synthesising the findings from 18 studies published from 2020 to 2024 and following the PRISMA-SCR guidelines for scoping reviews, we developed four key themes. This includes GenAI as a Potential Enhancement for SDL, The Educator as a GenAI Guide, Personalisation of Learning, and Approaching with Caution. Our findings suggest that GenAI tools, including ChatGPT and other Large Language Models (LLMs) show promise in potentially supporting SDL through on-demand, personalised assistance. At the same time, the literature emphasises that educators are as important and central to the learning process as ever before, although their role may continue to shift as technologies develop. Our review reveals that there are still significant gaps in understanding the long-term impacts of GenAI on SDL outcomes, and there is a further need for longitudinal empirical studies that explore not only text-based chatbots but also emerging multimodal applications.

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arXiv2024

Generative AI and Agency in Education: A Critical Scoping Review and Thematic Analysis

This scoping review examines the relationship between Generative AI (GenAI) and agency in education, analyzing the literature available through the lens of Critical Digital Pedagogy. Following PRISMA-ScR guidelines, we collected 10 studies from academic databases focusing on both learner and teacher agency in GenAI-enabled environments. We conducted an AI-supported hybrid thematic analysis that revealed three key themes: Control in Digital Spaces, Variable Engagement and Access, and Changing Notions of Agency. The findings suggest that while GenAI may enhance learner agency through personalization and support, it also risks exacerbating educational inequalities and diminishing learner autonomy in certain contexts. This review highlights gaps in the current research on GenAI's impact on agency. These findings have implications for educational policy and practice, suggesting the need for frameworks that promote equitable access while preserving learner agency in GenAI-enhanced educational environments.

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arXiv2024

Generative AI Tools in Academic Research: Applications and Implications for Qualitative and Quantitative Research Methodologies

This study examines the impact of Generative Artificial Intelligence (GenAI) on academic research, focusing on its application to qualitative and quantitative data analysis. As GenAI tools evolve rapidly, they offer new possibilities for enhancing research productivity and democratising complex analytical processes. However, their integration into academic practice raises significant questions regarding research integrity and security, authorship, and the changing nature of scholarly work. Through an examination of current capabilities and potential future applications, this study provides insights into how researchers may utilise GenAI tools responsibly and ethically. We present case studies that demonstrate the application of GenAI in various research methodologies, discuss the challenges of replicability and consistency in AI-assisted research, and consider the ethical implications of increased AI integration in academia. This study explores both qualitative and quantitative applications of GenAI, highlighting tools for transcription, coding, thematic analysis, visual analytics, and statistical analysis. By addressing these issues, we aim to contribute to the ongoing discourse on the role of AI in shaping the future of academic research and provide guidance for researchers exploring the rapidly evolving landscape of AI-assisted research tools and research.

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arXiv2024

The EAP-AIAS: Adapting the AI Assessment Scale for English for Academic Purposes

The rapid advancement of Generative Artificial Intelligence (GenAI) presents both opportunities and challenges for English for Academic Purposes (EAP) instruction. This paper proposes an adaptation of the AI Assessment Scale (AIAS) specifically tailored for EAP contexts, termed the EAP-AIAS. This framework aims to provide a structured approach for integrating GenAI tools into EAP assessment practices while maintaining academic integrity and supporting language development. The EAP-AIAS consists of five levels, ranging from "No AI" to "Full AI", each delineating appropriate GenAI usage in EAP tasks. We discuss the rationale behind this adaptation, considering the unique needs of language learners and the dual focus of EAP on language proficiency and academic acculturation. This paper explores potential applications of the EAP-AIAS across various EAP assessment types, including writing tasks, presentations, and research projects. By offering a flexible framework, the EAP-AIAS seeks to empower EAP practitioners seeking to deal with the complexities of GenAI integration in education and prepare students for an AI-enhanced academic and professional future. This adaptation represents a step towards addressing the pressing need for ethical and pedagogically sound AI integration in language education.

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arXiv2024

Understanding Student and Academic Staff Perceptions of AI Use in Assessment and Feedback

The rise of Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI) in higher education necessitates assessment reform. This study addresses a critical gap by exploring student and academic staff experiences with AI and GenAI tools, focusing on their familiarity and comfort with current and potential future applications in learning and assessment. An online survey collected data from 35 academic staff and 282 students across two universities in Vietnam and one in Singapore, examining GenAI familiarity, perceptions of its use in assessment marking and feedback, knowledge checking and participation, and experiences of GenAI text detection. Descriptive statistics and reflexive thematic analysis revealed a generally low familiarity with GenAI among both groups. GenAI feedback was viewed negatively; however, it was viewed more positively when combined with instructor feedback. Academic staff were more accepting of GenAI text detection tools and grade adjustments based on detection results compared to students. Qualitative analysis identified three themes: unclear understanding of text detection tools, variability in experiences with GenAI detectors, and mixed feelings about GenAI's future impact on educational assessment. These findings have major implications regarding the development of policies and practices for GenAI-enabled assessment and feedback in higher education.

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arXiv2024

Deepfakes and Higher Education: A Research Agenda and Scoping Review of Synthetic Media

The availability of software which can produce convincing yet synthetic media poses both threats and benefits to tertiary education globally. While other forms of synthetic media exist, this study focuses on deepfakes, which are advanced Generative AI (GenAI) fakes of real people. This conceptual paper assesses the current literature on deepfakes across multiple disciplines by conducting an initial scoping review of 182 peer-reviewed publications. The review reveals three major trends: detection methods, malicious applications, and potential benefits, although no specific studies on deepfakes in the tertiary educational context were found. Following a discussion of these trends, this study applies the findings to postulate the major risks and potential mitigation strategies of deepfake technologies in higher education, as well as potential beneficial uses to aid the teaching and learning of both deepfakes and synthetic media. This culminates in the proposal of a research agenda to build a comprehensive, cross-cultural approach to investigate deepfakes in higher education.

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arXiv2024

GenAI Detection Tools, Adversarial Techniques and Implications for Inclusivity in Higher Education

This study investigates the efficacy of six major Generative AI (GenAI) text detectors when confronted with machine-generated content that has been modified using techniques designed to evade detection by these tools (n=805). The results demonstrate that the detectors' already low accuracy rates (39.5%) show major reductions in accuracy (17.4%) when faced with manipulated content, with some techniques proving more effective than others in evading detection. The accuracy limitations and the potential for false accusations demonstrate that these tools cannot currently be recommended for determining whether violations of academic integrity have occurred, underscoring the challenges educators face in maintaining inclusive and fair assessment practices. However, they may have a role in supporting student learning and maintaining academic integrity when used in a non-punitive manner. These results underscore the need for a combined approach to addressing the challenges posed by GenAI in academia to promote the responsible and equitable use of these emerging technologies. The study concludes that the current limitations of AI text detectors require a critical approach for any possible implementation in HE and highlight possible alternatives to AI assessment strategies.

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arXiv2024

The AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI supported assessment- A Preprint

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.

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