Qian Liu

This systematic literature review of 39 peer-reviewed empirical articles outlines a usage framework comprising seven ways higher education students have utilized Generative Artificial Intelligence in their learning tasks since January 2018 and examines the resulting actual and perceived learning outcomes. Findings indicate that students’ actual learning outcomes achieved through Generative Artificial Intelligence were predominantly successful, while perceived outcomes vary, presenting a mixed picture of success and challenges. Specifically, when students used Generative Artificial Intelligence as a translator, refiner, navigator, evaluator, dialoguer, or self-regulatory supporter, they perceived higher success-to-challenge ratios in learning effectiveness, learning efficiency, interactivity, self-regulation, and personalized learning. By contrast, using Generative Artificial Intelligence as a creator resulted in an approximately equal proportion of successes and challenges. Notably, Generative Artificial Intelligence as a self-regulatory supporter resulted in the lowest success-to-challenge ratio among all usage types, with the few challenges attributed to insufficient holistic integrated self-regulated learning skills. The findings suggest that students need to enhance their comprehensive self-regulated learning capabilities to optimize Generative Artificial Intelligence use in learning task.

Embedding employability skills within university courses remains a significant challenge for educators, particularly in fields where professional readiness must complement rigorous academic training. This study presented a practical, theory‐driven curriculum intervention to redesign a final‐year Bachelor of Property course at a research‐leading university in New Zealand. Guided by constructive alignment principles, course learning outcomes were aligned with institutional graduate attributes and property industry standards, employing backward design to create assessments and activities that foster real‐world skills alongside disciplinary knowledge. Over a 3‐year period, the intervention was evaluated using pre‐ and post‐redesign data, revealing significant improvements in student performance, engagement and satisfaction. The findings demonstrated that integrating employability capabilities into curriculum design enhances student achievement and enriches the learning experience, making it relevant, engaging and professionally meaningful. Students responded to the redesigned course with increased motivation and perceived value, often attributing the course's structure to the instructor's organisation and relevance—evidence of the impact of alignment on learning efficiency. This study underscored the potential of constructive alignment as a strategic approach for embedding employability within university programmes, offering insights for educators seeking to balance academic rigour with practical applicability. Our results offered valuable implications for higher education professionals dedicated to bridging the gap between academic outcomes and real‐world professionalism, and they highlighted promising avenues for future research in curriculum development and employability.

ChatGPT has sparked heated discussion in higher education since its public release and increasing empirical studies have been made available examining its application to higher education teaching and learning. To capture and synthesize the initial scholarly developments in this topic, we undertook a scoping review of empirical research into the use of ChatGPT for higher education teaching and learning, published in the first eight months since its release. We identified 39 articles through a systematic literature search in the Web of Science and Scopus databases. Our analysis identified five thematic areas that extant research has contributed to, including (1) ChatGPT's performance in assessment; (2) ChatGPT's capacity in facilitating learning; (3) staff and students' perceptions of ChatGPT; (4) the adoption of ChatGPT, and; (5) ChatGPT-related policy implications. Drawing on the findings, we discuss current understanding and directions for future research in each of the thematic area. We then discuss issues emerged from the reviewed literature, including implications for improving research design, researching academic integrity, and explaining ChatGPT's pedagogical value in light of learning theories and other digital technologies.

Authentic assessment is widely recognized as a valuable method that reflects real-world contexts, allowing students to apply their knowledge to practical challenges and prepare for their futures. Despite the pervasive influence of digital technologies in modern work and life, their role in authentic assessment—an approach centered on real-world relevance—remains poorly understood. Key questions persist regarding how digital technologies are integrated into authentic assessment practices. This review examines the nature and extent of technology integration in authentic assessment within higher education literature. Through a systematic search, identification, analysis, and synthesis, we identified 52 relevant studies. Our findings reveal significant variation in technology use across the four steps of authentic assessment. While digital tools are commonly employed in assessment task design (Step 2), there is limited consideration of broader digital contexts (Step 1) or integration into evaluative judgments and feedback mechanisms (Steps 3 and 4). We also identify diverse approaches to incorporating technology within the design phase. These differences in technology use reflect varying conceptualizations of authentic assessment, influencing its design, implementation, and learning outcomes. To provide educators with practical guidance, we build on a widely adopted stepwise model by introducing a structured framework for integrating digital technologies into authentic assessment. Finally, we highlight areas for future research and practice that may enhance authentic assessment through technology.

There is a growing demand for advanced education from mature learners who seek postgraduate qualifications whilst maintaining career and family responsibilities, but recent research has identified concerns regarding their well-being. This study assesses students’ emotional well-being and examines how well-being is associated with the way students learn and the grades they receive. In a survey of 206 part-time postgraduate students who worked for 30 or more hours a week, we assessed activated aspects of affective well-being (in the form of self-reported anxiety and enthusiasm), learning behaviour, and grades. We observed positive relationships between anxiety, enthusiasm, and learning behaviour and grades. Our results suggest that learning behaviour mediates the relationship between positively activated well-being (enthusiasm) and grades and that negatively activated emotional well-being (anxiety) moderates the effect of lifelong learning on grades. The implications for theory and practice are discussed.