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While most language departments of the university offer service-learning (SL) subjects based on language teaching, such as “Teaching Chinese as a Second Language in Local Schools” and “Serving the Community through Teaching English,” this paper aims to argue that teaching students to teach language(s) is yet to be the best strategy to serve the service recipients.

SL is widely understood as an experiential learning pedagogy that integrates academic focus, reflection and community service and is shown to be impactful. In Hong Kong, the first university that has made SL a graduation requirement is the Hong Kong Polytechnic University (the University). Considering this, new SL courses have proliferated over the past decade. Adopting a narrative inquiry approach, this paper examines personal narratives from a new SL subject aiming to raise awareness of refugees in Hong Kong. The data includes students’ reflective journals, co-created personal narratives and podcasts and semi-structured interviews.

This paper finds that crafting and recording narratives of shared experiences deepens cultural understanding, cultivates empathy and facilitates language learning in a genuine setting.

Ultimately, this paper advocates a well-designed SL that combines language, content and technology as a powerful, transformational experience for both college students and service recipients.

This paper focuses on a brand new SL course, “Storytelling for Understanding: Refugee Children in Hong Kong,” offered in Semester 1, 2022–2023. The subject was developed by the two authors from a language division affiliated to the University. The deliverables were podcast recordings, co-authored and co-edited by the students and the children.

Although scholars argue that artificial intelligence (AI) represents a tool to potentially smoothen the bullwhip effect in the supply chain, only little research has examined this phenomenon. In this article, the authors conceptualize a framework that allows for a more structured management approach to examine the bullwhip effect using AI. In addition, the authors conduct a systematic literature review of this current status of how management can use AI to reduce the bullwhip effect and locate opportunities for future research.

Guided by the systematic literature review approach from Durach et al. (2017), the authors review and analyze key attributes and characteristics of both AI and the bullwhip effect from a management perspective.

The authors' findings reveal that literature examining how management can use AI to smoothen the bullwhip effect is a rather under-researched area that provides an abundance of research avenues. Based on identified AI capabilities, the authors propose three key management pillars that form the basis of the authors' Bullwhip-Smoothing-Framework (BSF): (1) digital skills, (2) leadership and (3) collaboration. The authors also critically assess current research efforts and offer suggestions for future research.

By providing a structured management approach to examine the link between AI and the bullwhip phenomena, this study offers scholars and managers a foundation for the advancement of theorizing how to smoothen the bullwhip effect along the supply chain.