Vol. 1 No. 1 (2026): Quarterly Issue 1

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

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.

As artificial intelligence (AI) transforms higher education, institutions face critical challenges in developing appropriate governance frameworks, particularly in Hispanic-Serving Institutions (HSIs) where technological integration

As artificial intelligence (AI) transforms higher education, institutions face critical challenges in developing appropriate governance frameworks, particularly in Hispanic-Serving Institutions (HSIs) where technological integration intersects with educational equity concerns. Drawing on institutional isomorphism theory and the framework of policy diffusion, this study conducts a systematic comparative analysis of artificial intelligence (AI) integration policies across nineteen Hispanic-Serving Institutions (HSIs) in Texas, revealing potentially divergent institutional approaches to AI governance in higher education. Through comprehensive document analysis incorporating qualitative content analysis, policy network analysis, and quantitative scoring methods, the research examines how different types of institutions are developing and implementing AI integration frameworks at the institutional policy level. The analysis reveals seven distinct policy approaches: Holistic Integration, Research and Innovation Driven, Teaching and Learning Focused, Ethically Anchored, Community and Workforce Oriented, Cautious Adoption, and Emerging Framework. Policy network analysis demonstrates strong policy diffusion patterns among institutions within the same university systems and geographic regions, while quantitative scoring indicates significant disparities in policy comprehensiveness between research-intensive universities and other institutions. The findings suggest that institutional characteristics, including research classification, size, and resources, significantly influence institutional AI policy development approaches. Notably, the study identifies a potential policy development gap between research-intensive universities and teaching-focused institutions or community colleges, raising important questions about equity in AI integration across different institutional types. These findings provide crucial insights for policymakers and institutional leaders working to develop effective AI integration frameworks, particularly in the context of serving historically underrepresented student populations in higher education.

ChatGPT is a rapidly evolving Artificial Intelligence tool which has attracted wide interest in higher education. This study aims to provide evidence on the experiences of student teachers who have used ChatGPT for academic-educational

ChatGPT is a rapidly evolving Artificial Intelligence tool which has attracted wide interest in higher education. This study aims to provide evidence on the experiences of student teachers who have used ChatGPT for academic-educational activities. The participants were 17 Greek preschool student teachers and the data was collected via interviews. The findings indicated that positive experiences regard general benefits (e.g., fast generation of answers, ease of use, source of information), ChatGPT’s support for educational activities (planning educational practices, interventions, lesson plans), and provision of different perspectives and approaches (including strengthening children’s social skills and ICT integration). Experienced limitations-challenges include reliability, validity of information, general/vague answers (e.g., ideas for educational practices), and limited contextual understanding. Submission of multiple queries/prompts for the generation of more relevant information was experienced both as benefit and limitation. Students value and recommend ChatGPT as a useful, supportive tool for educational purposes, and highlight the necessity for enhancement of student skills and practices (e.g., to evaluate responses/output and sources of information, and ask specific queries). Implications for students, university policy and educators are discussed. Universities need to respond to AI technology changes, adopt-establish new policies and enforce sustainability.

Educational recommender systems (ERSs) traditionally prioritize prediction accuracy, often overlooking the impact of recommendation diversity on user satisfaction. This study aims to understand how recommendation diversity and user

Educational recommender systems (ERSs) traditionally prioritize prediction accuracy, often overlooking the impact of recommendation diversity on user satisfaction. This study aims to understand how recommendation diversity and user psychological traits, such as need for cognitive closure, affect user satisfaction and preferences in ERSs. In two experiments involving university students, we analyzed subjective perceptions of recommendation qualities—including accuracy, novelty, and usefulness—and evaluated the effects of psychological priming on user evaluations. The results reveal that user satisfaction depends not only on perceived accuracy but also on the interplay of diversity, perceived usefulness, and novelty. Furthermore, priming users to consider accuracy or diversity prior to using the system appeared to mitigate the influence of psychological traits, resulting in more consistent evaluations. These findings highlight the potential of task-based strategies, psychological trait personalization, and priming for designing ERSs that foster more effective and satisfying learning experiences.

The advancement of Generative Artificial Intelligence (AI) chatbots, such as ChatGPT, presents significant and transformative challenges in higher education teaching and learning, such as assessment and evaluation practices. While this is

The advancement of Generative Artificial Intelligence (AI) chatbots, such as ChatGPT, presents significant and transformative challenges in higher education teaching and learning, such as assessment and evaluation practices. While this is acknowledged, there has been very little research into what this might look like in daily practice in higher education. This study explored these challenges in one area of higher education practice: developing students’ transferable skills, including writing, critical thinking, and information literacy among undergraduate engineering students at RMIT University, Melbourne, Australia. Using a cohort comparison design, this study evaluated the impact of ChatGPT on students' attainment of transferable skills. The effectiveness of AI tools in enhancing educational outcomes was assessed with a standardised assessment framework used by independent assessors to grade students’ reports. The results, analysed using the Mann-Whitney U test and the intraclass correlation coefficient, revealed significant improvements in critical thinking and information literacy among those students who used ChatGPT. The study also explored the ethical implications of using AI in educational settings and highlighted the need for rigorous academic standards and the implementation of measures to ensure the responsible use of AI technologies. While the preliminary findings suggest that AI tools, particularly ChatGPT in this study, can positively impact certain students’ skills, more detailed and controlled studies are necessary to validate these results and explore further the mechanisms through which AI tools influence learning and skill development.

In an era characterised by rapid technological advancements, educational tools have become integral to academic success. Tools such as Grammarly, Studiosity, assessment exemplars, and more recently, Generative Artificial Intelligence

In an era characterised by rapid technological advancements, educational tools have become integral to academic success. Tools such as Grammarly, Studiosity, assessment exemplars, and more recently, Generative Artificial Intelligence (GenAI) technologies like ChatGPT, offer students unprecedented support. This paper considers the critical role of the productive struggle as part of the journey to meta-learning and meta-cognition and emphasises the risk of educational tools in sidestepping this crucial process. To explore how educators can support students in achieving learning success when tempted by product-oriented educational tools, this paper presents the Responsible Use of Educational Tools Thinking Framework which offers a pragmatic approach for assisting educators and students in applying critical thinking when integrating educational tools into the assessment process. This proposed framework, complemented by a teaching model, serves as a guide to help students navigate their path and use critical thinking while using educational tools. To further support this thinking framework, this conceptual paper presents three key practices essential for embedding educational tools into the learning journey. First, students must purposefully engage with educational tools to develop technological competence. Second, they must exercise critical thinking and judgment during the productive struggle of idea generation, and third, students must employ ethical decision-making to ensure the responsible and morally conscious use of these tools. Aligned with the imperative of cultivating technological competence, exercising critical thinking amidst the productive struggle, and instilling ethical decision-making, the framework functions as a practical solution to ensure that students not only acquire knowledge through the assessment process, but also cultivate essential skills for critical thinking and managing educational success.

This pilot study aims to evaluate the AI-Teacher Teaching Task Spectrum (AITTTS), a framework designed to categorise human-AI intervention levels based on the teaching tasks and their suitability for AI or human intervention. The primary

This pilot study aims to evaluate the AI-Teacher Teaching Task Spectrum (AITTTS), a framework designed to categorise human-AI intervention levels based on the teaching tasks and their suitability for AI or human intervention. The primary objective was to provide preliminary validation of the framework’s practical utility by examining its alignment with current literature and gathering practitioner feedback. A systematic literature review was conducted, focusing on three key studies that offered insights into AI-teacher task delegation. Additionally, a structured survey was used to collect data from three expert practitioners in AI and education, with Fleiss’ Kappa applied to measure agreement. The findings indicated substantial agreement (Fleiss’ κ = 0.73) on the framework’s validity, particularly for identifying tasks suitable for AI, such as procedural and knowledge-based activities. However, the study’s small sample size and limited geographic diversity restrict the generalisability of the findings. Disagreements, especially regarding AI’s role in creative and relational tasks, highlight areas requiring further exploration. As the first step in an iterative research agenda, this pilot study provides foundational insights into the framework’s potential. Future research will involve expanded participant samples, broader educational contexts, and iterative refinements to enhance the framework’s applicability and generalisability across varied educational settings.

Since the introduction of ChatGPT in 2022, the discourse on AI in higher education has evolved from the focus and concerns on its impact on academic integrity to embracing AI in teaching and learning towards enhancing students’ AI

Since the introduction of ChatGPT in 2022, the discourse on AI in higher education has evolved from the focus and concerns on its impact on academic integrity to embracing AI in teaching and learning towards enhancing students’ AI literacy. With many AI literacy frameworks, such as UNESCO AI competency and AI in teaching and learning frameworks developed to support educators in adopting AI, there is still a gap in practically applying the framework in teaching. This presents an opportunity to explore developing a holistic AI in teaching approaches that enable educators to incorporate AI in their teaching and provide practical examples to do this. This paper aims to address this gap twofold: first, it presents the five-step approach to integrating AI in teaching, and second, it demonstrates through a case study how to integrate AI in teaching using the AI in teaching and learning framework and constructive alignment.

This paper proposes a Generative Artificial Intelligence (GenAI) Competence Framework for Engineering Curriculum Enhancement, designed to leverage GenAI’s potential to enrich teaching and learning, address challenges arising from misuse or

This paper proposes a Generative Artificial Intelligence (GenAI) Competence Framework for Engineering Curriculum Enhancement, designed to leverage GenAI’s potential to enrich teaching and learning, address challenges arising from misuse or unethical application of GenAI, and prepare graduates for AI-driven workplaces. Building upon the university’s AI literacy conceptual framework, this GenAI Competence Framework defines GenAI competence tiers, provides comprehensive strategies for integrating GenAI into engineering education, and offers practical implementation plans and impact assessment mechanisms that collectively form a sustainability roadmap. The framework's development was informed by extensive consultation with students, staff, and industry experts through surveys, focus groups, interviews, and program reviews, ensuring its practicality, adaptability, and alignment with real-world demands. By incorporating GenAI-empowered curriculum enhancements alongside supplementary extracurricular activities to bridge the digital divide, the framework aims to equip students with essential GenAI competencies while enriching their overall learning experience.

Researchers have found that GenAI places considerable pressure on the integrity of online summative assessments. My research strengthens this conclusion in two ways. I argue that the impact of GenAI for online assessments is as widespread

Researchers have found that GenAI places considerable pressure on the integrity of online summative assessments. My research strengthens this conclusion in two ways. I argue that the impact of GenAI for online assessments is as widespread as are already popular messaging applications such as Discord. I argue furthermore, on the basis of a study utilizing problem sets from my own PHIL 007 Introduction to Critical Thinking, that utilization of GenAI on online summative assessments is not necessarily mitigated by devising problem sets which test for higher-order learning. ChatGPT-4o performed swimmingly on problem sets that consisted almost entirely of challenging critical thinking exercises. While these two results may seem to entail a dire situation for online summative assessments, the way forward for higher education in online spaces will require reshaping assessment validity so that learning outcomes are not compromised by student usage of GenAI.

Generative artificial intelligence tools have sparked a debate about their successful integration into post-secondary educational pedagogy, spawning varying responses from institutions of higher education worldwide. Assuming the disruptive

Generative artificial intelligence tools have sparked a debate about their successful integration into post-secondary educational pedagogy, spawning varying responses from institutions of higher education worldwide. Assuming the disruptive nature of this technology and its potential for a paradigm shift in pedagogy, the focus of this paper is on the challenges faced by Canadian academics in integrating these tools and their responses to the evolving role of this new technology. Through a comprehensive literature review and analysis of existing policies and practices of 15 research universities in Canada, this study identifies problem areas associated with the management of learning assessments while underscoring the need for clear and ethically sound policies to empower Canadian professors. The paper concludes with recommendations for policy development and future research directions, emphasizing the importance of balancing innovation with ethical responsibility in the use of generative AI in the development and management of pedagogy in post-secondary education.

UNESCO’s 2024 AI Competency Framework for Teachers adroitly captures the polarised potential of Artificial Intelligence (AI) in Education, noting its capacity to “enhance learning experiences and support teacher tasks” (13) whilst also

UNESCO’s 2024 AI Competency Framework for Teachers adroitly captures the polarised potential of Artificial Intelligence (AI) in Education, noting its capacity to “enhance learning experiences and support teacher tasks” (13) whilst also warning of its “significant risks to students, the teaching community, education systems and society at large” (13). This practice paper recounts the efforts of a regional Australian university to offset these risks and accentuate the benefits of AI for student learning. The project commenced with the unexpected discovery that students were apprehensive about using AI for academic tasks, contrary to concerns that they would readily adopt it for unethical purposes. This fear appeared to be driven by academic integrity policies designed to prevent AI misuse. In the main, students were not using AI to enhance learning. Subsequently, a multi-disciplinary Community of Practice, the AI Pedagogy Project, created a student learning resource: AI Power Up: Supercharge Your Learning. This multimodal, active-learning resource sought to differentiate between the many benefits for AI in learning and its specialised use in assessment. Students were initially introduced to AI terminology, before exploring its benefits and limitations. Explicit instruction was provided on how to use AI to personalise and enhance the learning experience in an ethical and informed manner. This paper recounts the development of the resource, its contents, deployment, and impact on students and staff.