For over a decade, learning analytics has significantly enhanced understanding and development of learning and teaching processes in higher education. In this setting, prompts, cues, and nudges, among others, have gained attention as specific analytics-informed interventions aimed at influencing learners’ behaviour, engagement, and self-regulation. Prompts and cues primarily stem from educational psychology, while nudges derive from behavioural economics, but all three aim to direct learners towards more effective learning decisions. This paper explores the effectiveness of prompts, cues and nudges, as well as the opportunities and challenges associated with their implementations in higher education. Using a systematic literature review guided by the PRISMA framework, the study synthesizes 39 experimental and quasi-experimental studies reported in 31 papers. The results show that these interventions are implemented across diverse higher education settings and differ in their goals, technological designs, and targeted outcomes. Overall, the evidence suggests positive but domain-specific effects: prompts are most often linked to self-regulation, engagement, and knowledge-related outcomes, cues to cognitive and attentional processes, and nudges to behavioural engagement, while all three are associated with positive outcomes in learning and academic performance. Prompts were the most frequently studied intervention and showed the widest range of measured outcomes. This study also highlights Perceptions and Affective Outcomes as an important but underexplored area for future research across analytics-informed prompts, cues, and nudges. The findings further highlight the importance of experimental and quasi-experimental studies for strengthening evidence in learning analytics research.
Generative artificial intelligence (GenAI) is increasingly embedded in higher education, yet limited research has examined how GenAI-supported learning operates alongside geographically distributed peer collaboration. This qualitative study explores student experiences in an AI-enhanced cross-campus cybersecurity learning environment connecting university campuses in California, United States, and Ontario, Canada. Anonymous reflections were collected from 19 students (nine in California and ten in Ontario) about their experiences with GenAI-supported learning, cross-campus interaction, associated challenges, and recommendations for improving the learning experience. The findings indicate that students perceived GenAI as supporting concept clarification, greater perceived learning efficiency, idea development, and problem-solving, while also emphasizing the importance of critically evaluating GenAI-generated information. Cross-campus interaction was perceived as exposing students to diverse perspectives, experiences, and approaches to cybersecurity, which participants described as broadening their understanding of collaborative problem-solving across contexts. Students nevertheless identified challenges involving scheduling, technology, differences in experience, GenAI accuracy, and responsible use. They recommended more structured cross-campus projects, hands-on activities, clearer guidance on ethical GenAI use, and greater engagement with industry professionals. The study contributes to research on technology-enhanced learning in higher education by suggesting how GenAI-supported learning and geographically distributed peer interaction can provide complementary educational resources: GenAI offers immediate cognitive and informational support, while cross-campus interaction provides access to situated human perspectives. Together, these experiences highlight the importance of verification, contextual judgment, critical AI literacy, and intentional pedagogical design in GenAI-enhanced learning environments.
Research funding bodies have significant influence on what research is prioritised, how it is conducted, and what standards are enforced. While a number of global frameworks provide normative guidance on AI ethics, there is a clear gap in understanding how these principles are implemented at the funding-body level. This study aims to examine the current AI policies and guidelines of research funding bodies across different geographical regions. Inductive thematic analysis was adopted to analyse 56 AI-related policies from 46 funding agencies across the US, the UK, Europe, Australia, Singapore, Mainland China, and Hong Kong. The analysis revealed five common themes: human-centredness, safeguarding, embeddedness, governance, and AI readiness. It was also found that current funding-specific policies lack explicit guidelines on how broad principles and macro-level regulations can be interpreted and applied in AI-enabled research funding processes. The findings underscore the important role of funding agencies as key actors in defining responsible AI use in research processes. Finally, the practical implications of this study for regulators, funding bodies, and researchers are discussed, highlighting the value of policy document analysis in shaping ethical, transparent, and responsible AI frameworks.
Although previous studies have shown that GenAI can support learning engagement and motivation, it remains unclear how GenAI-assisted development of offline educational games based on Scripted Mystery Role-Playing Games (SMRPG) influences students’ learning experiences and outcomes. Based on the SMRPG format, this study developed an offline educational game with the assistance of Gemini 3 Pro to explore its potential value for students’ learning outcomes and experiences in higher education contexts. A sequential explanatory mixed-methods design was employed with 84 undergraduate students majoring in law as participants. First, a randomized controlled experiment compared learning outcomes between an experimental group (N = 42) and a control group (N = 42). Subsequently, qualitative interviews were conducted with 24 students who participated in the game to examine their learning experiences during the gameplay process. Quantitative results showed that, after controlling for pre-test scores, the experimental group achieved significantly higher post-test scores than the control group (F(1, 81) = 105.61, p <.001, partial η² = 0.566). Qualitative findings indicate that participants described an immersive learning experience involving reasoning scenarios and role interaction in the GenAI-assisted game. This experience was associated with greater willingness to engage in learning, provided opportunities for the mobilization and application of relevant knowledge, and supported sustained motivation and reflective learning. This study provides empirical evidence on the learning experiences and learning outcomes associated with applying GenAI to develop legal educational games based on SMRPG, offering insights for future research and practice on GenAI-enabled educational game development.
Large language models (LLMs) are rapidly becoming embedded in higher education, yet their benefits may be unevenly distributed. This study conceptualizes prompt engineering competence (PEC) as an equity-relevant interaction competence and examines whether differences in PEC are associated with perceived academic gains and perceived educational inequity. We adopt a two-stage quantitative design combining a cross-sectional survey with a within-subject prompt-quality experiment. Stage 1 reports a survey of Chinese undergraduates who have used LLMs (N = 380), measuring PEC, perceived academic performance gains, perceived educational inequity, family digital capital, gaming experience, and a planning/independent-work tendency. Regression and mediation analyses show that PEC was strongly associated with perceived academic gains, while its association with perceived inequity was statistically significant but small. Background resources and learning-related experiences and dispositions explained substantial variance in PEC, especially gaming experience and planning/independent-work tendency, and the association between family digital capital and perceived academic gains was largely indirect through PEC. Stage 2 conducts a within-subject prompt-quality experiment across four higher-education task types and four popular models, comparing basic prompts with a student-oriented five-component refined prompt framework (Role–Goal–Input–Process–Output). Analysis of 400 matched prompt comparisons, comprising 800 model outputs, indicates that the refined prompt scaffold consistently outperforms basic prompts. Overall, the findings identify PEC as a potential competence pathway through which students may differ in their ability to convert LLM access into learning-related benefits, while structured prompt scaffolds may provide a scalable approach to reducing prompt-quality barriers in LLM-supported higher education.
