Journal of University Teaching and Learning Practice

Higher education institutions, educators, and students continue to grapple with the wide-ranging implications of Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI). The ability of advanced GenAI models to complete educational assessments continues to be one of the most pressing issues for academia. In early 2024, we published the AI Assessment Scale (AIAS), which describes a practical framework for addressing GenAI use in educational assessment. The AIAS was well received and has been implemented in hundreds of institutions worldwide and translated into 30 languages. Building on our experience using the AIAS and drawing on feedback and critiques received worldwide, in this paper, we provide an updated version of the AIAS. The updated AIAS encompasses several important developments, including clarifying the theoretical basis of the scale through social constructivist principles, developing the AIAS to function as an assessment redesign framework to enhance validity, and adjusting the visual assets of the AIAS to function inclusively and non-hierarchically. Furthermore, in anticipation of future developments in the field of AI and the increasing focus on preparing students to function in an AI-augmented world, we introduce an additional level, ‘AI exploration’. Through this paper we also seek to clarify both the intended and unintended applications of the AIAS. We provide implementation guidance through practical examples and explain how the AIAS functions as both a communication tool and a framework for task redesign that strengthens validity.

The swift adoption of generative AI (GAI) technologies such as ChatGPT in higher education has raised important questions about their effect on students' critical thinking—an essential component of undergraduate education. This study adopts a mixed-methods approach to investigate the influence of AI-based text generation tools on the enhancement of cognitive skills among 100 undergraduate students, using Bloom’s Taxonomy as a framework. Employing a quasi-experimental design, participants were divided into a control group and an experimental group. Over a span of six weeks, the experimental group engaged with ChatGPT to complete a series of structured academic exercises. Pre- and post-test assessments, alongside thematic analysis of student surveys, revealed dual outcomes: while ChatGPT significantly enhanced foundational skills (remembering, understanding, applying), it showed negligible effects on higher-order critical thinking (analyzing, evaluating, creating). Students reported using AI for proofreading, structuring assignments, and literature summarization, freeing cognitive bandwidth for Higher tasks. However, challenges such as AI "hallucinations" (fabricated outputs), digital literacy gaps, and cultural biases underscored risks of over-reliance and superficial engagement. Qualitative findings highlighted ethical dilemmas, including concerns about diminished empathy and academic integrity, while also noting AI’s role in fostering metacognitive reflection through fact-checking. The study advocates for a balanced pedagogical approach, where AI complements—rather than replaces—human-guided instruction, and proposes institutional strategies for AI literacy training, ethical guidelines, and curriculum redesign. These findings contribute to the discourse on technology-enhanced learning, emphasizing the need to preserve cognitive rigor in an AI-driven educational landscape.

The emergence of widely available artificial intelligence (AI) tools has made assessment in higher education increasingly uncertain. Familiar (if problematic) assumptions about what assessment does or should measure, who or what is being assessed, and how judgments are made are all being reexamined. Educators and researchers are experimenting with new assessment designs, but the emerging research landscape is fragmented and difficult to navigate. There is little shared sense of what kinds of studies are most needed or how their findings might connect. To address this, a group of leading assessment scholars met in Melbourne, Australia in September of 2025 to develop a collective research agenda to help guide and connect future inquiry. This paper presents the outcomes of that collaboration, a set of guiding principles and framing questions – why, who, what, how, and where we assess – that together offer a structure for guiding and supporting the research we should be doing on assessment after AI.

The rapid maturation of generative artificial intelligence (GenAI) platforms has created significant challenges for higher education, particularly in relation to assessment. This project sought to explore the lived experiences and views of GenAI in relation to assessment for academics and students in higher education. A qualitative interpretivist approach was adopted, using semi-structured interviews. When data were thematically analysed, three themes were developed; emotions, academic role and identity; assessment must change, and the work and expertise required to change assessment. Academics and students experienced both positive and negative emotions in relation to GenAI. Concerns about the complexity of changing assessment and resources needed to change assessment were pervasive. Based on these findings we propose that “it takes a village” to redevelop assessments in a time of GenAI and therefore collaborative, program-wide approaches are the way forward.

This study explored how tertiary education professionals in Australia understand and apply Universal Design for Learning (UDL) in their practice, the barriers they face, and what more institutions can do to support its implementation. Semi-structured interviews were conducted with 11 professionals across various roles and institutions. Findings revealed that, while educators are using UDL principles to enhance engagement, accessibility, and assessment flexibility, their efforts are often ad hoc and unsupported by formal structures. Key barriers included limited time, confusion about how to apply UDL in specific contexts, and a lack of institutional mandates. Participants strongly advocated for embedding UDL into institutional policies, performance expectations, and strategic plans. Practical, discipline-specific professional learning and access to real-world examples were seen as critical for supporting implementation. The importance of local communities of practice and mindset shifts, positioning UDL as quality teaching for all students, were also discussed. These findings underscore the need for tertiary institutions to adopt a systemic and sustained approach to UDL, moving beyond compliance to embed inclusive design into the fabric of teaching and learning.

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.

Research at the University of New South Wales with Defence Force Academy students found time-management skills significantly improve the academic results of male students but not female students when controlling for the usual effect of prior academic ability. While much previous research has revealed a positive link between time management and academic results within civilian environments, there is a lack of research on this link in dual-military or employment and academic settings. Our research also extends on many national and international studies’ by researching the efficacy of a direct training intervention in time-management skills. The study is unique in evaluating the size of the likely effect on academic results. This study used a mixed-methods evaluation design and a standardised questionnaire to compare two groups of 70 first-year undergraduate students. The experimental group were taught time-management skills in a workshop setting, while a control group did not attend time-management workshops. While the time-management workshop interventions did not improve the academic performance of female students, female students did benefit comparably to males in fewer subject failings and improved attitudes towards time-management attributes. The findings confirm an effect between time-management skills and academic success that offers substantial savings from fewer lost students. Recommendations are made to improve time-management skills and academic performance for all transitioning university students and examine if this significant effect translates to other initial training employment.

The OpenAI’s ChatGPT-3, or Chat Generative Pre-Trained Transformer was released in November 2022 without significant warning, and has taken higher education by storm since. The artificial intelligence (AI)-powered chatbot has caused alarm for practitioners seeking to detect authenticity of student work. Whereas some educational doomsayers predict the end of education in its current form, we propose an alternate early view. We identify in this commentary a position where educators can leverage AI like ChatGPT to build supportive learning environments for students who have cultivated good character. Such students know how to use ChatGPT for good, and can engage effectively with the ChatGPT application. In building our ChatGPT argument, we acknowledge the existing literature on plagiarism and academic integrity, and consider leadership as a root support mechanism, character development as an antidote, and authentic assessment as an enabler. In doing so, we highlight that while ChatGPT – like papermills, and degree factories before it – can be used to cheat on university exams, it can also be used to support deeper learning and better learning outcomes for students. In doing so, we offer a commentary that offers opportunities for practitioners, and research potential for scholars.

This paper explores the academic integrity considerations of students’ use of Artificial Intelligence (AI) tools using Large Language Models (LLMs) such as ChatGPT in formal assessments. We examine the evolution of these tools, and highlight the potential ways that LLMs can support in the education of students in digital writing and beyond, including the teaching of writing and composition, the possibilities of co-creation between humans and AI, supporting EFL learners, and improving Automated Writing Evaluations (AWE). We describe and demonstrate the potential that these tools have in creating original, coherent text that can avoid detection by existing technological methods of detection and trained academic staff alike, demonstrating a major academic integrity concern related to the use of these tools by students. Analysing the various issues related to academic integrity that LLMs raise for both Higher Education Institutions (HEIs) and students, we conclude that it is not the student use of any AI tools that defines whether plagiarism or a breach of academic integrity has occurred, but whether any use is made clear by the student. Deciding whether any particular use of LLMs by students can be defined as academic misconduct is determined by the academic integrity policies of any given HEI, which must be updated to consider how these tools will be used in future educational environments.

Peer observation of teaching is seen as a supportive and developmental process for improving the quality of teaching in universities. Evidence is emerging that the process of observing is just as if not more valuable than being observed and given feedback. In this study lecturers completing a Foundations program in university learning and teaching were interviewed about their experience of participating in a reciprocal peer observation exercise. The benefits for observers include learning about a new strategy and enhancing their confidence to try this strategy in their own teaching. Receiving feedback was also perceived to be useful but not more beneficial than watching a peer teach. We discuss implications of our results for units and institutions planning to implement peer observation as part of a strategic approach to improve the quality of learning and teaching.