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Service-learning is a community-based learning approach that bridges academic knowledge with practical application through purposeful exploration, action, and reflection. In addition to enhancing academic learning in various disciplines, service-learning cultivates students’ self-awareness, personal values, and social responsibility, preparing them with essential skills for life beyond the classroom. However, due to its experiential nature, service-learning presents challenges for effective assessment. This study provides a concrete example of student reflections structured by the Objective, Reflective, Interpretive, and Decisional (ORID) model in practice. Content analysis was implemented by examining undergraduate students’ end-of-semester reflection papers while volunteering during the 2022 FIFA World Cup in Qatar. The results showed that the model provided a guided and structured format for students to reflect on their service-learning, going beyond reporting on factual details to engaging in profound reflections on the emotional, cognitive, and prospective aspects. Moreover, with the solution aspect added to the model, students could express their creativity, articulating on innovative solutions they proposed to overcome challenges and how they turned the challenges into favorable outcomes. Clearly, the model stands as a powerful tool for educators, offering deeper insights into students’ authentic experiences, fostering comprehensive, critical, and insightful reflection, and thereby facilitating the effective assessment of experiential learning.

In the digital age, the intersection of artificial intelligence (AI) and higher education (HE) poses novel ethical considerations, necessitating a comprehensive exploration of this multifaceted relationship. This study aims to quantify and characterize the current research trends and critically assess the discourse on ethical AI applications within HE. Employing a mixed-methods design, we integrated quantitative data from the Web of Science, Scopus, and the Lens databases with qualitative insights from selected studies to perform scientometric and content analyses, yielding a nuanced landscape of AI utilization in HE. Our results identified vital research areas through citation bursts, keyword co-occurrence, and thematic clusters. We provided a conceptual model for ethical AI integration in HE, encapsulating dichotomous perspectives on AI’s role in education. Three thematic clusters were identified: ethical frameworks and policy development, academic integrity and content creation, and student interaction with AI. The study concludes that, while AI offers substantial benefits for educational advancement, it also brings challenges that necessitate vigilant governance to uphold academic integrity and ethical standards. The implications extend to policymakers, educators, and AI developers, highlighting the need for ethical guidelines, AI literacy, and human-centered AI tools.