As the shortage of skilled workers continues to be a pressing issue, exacerbated by demographic change, it is becoming a critical challenge for organizations to preserve the knowledge of retiring experts and to pass it on to novices. While this knowledge transfer has traditionally taken place through personal interaction, it lacks scalability and requires significant resources and time. IT-based teaching systems have addressed this scalability issue, but their development is still tedious and time-consuming. In this work, we investigate the potential of machine learning (ML) models to facilitate knowledge transfer in an organizational context, leading to more cost-effective IT-based teaching systems. Through a systematic literature review, we examine key concepts, themes, and dimensions to better understand and design ML-based teaching systems. To do so, we capture and consolidate the capabilities of ML models in IT-based teaching systems, inductively analyze relevant concepts in this context, and determine their interrelationships. We present our findings in the form of a review of the key concepts, themes, and dimensions to understand and inform on ML-based teaching systems. Building on these results, our work contributes to research on computer-supported cooperative work by conceptualizing how ML-based teaching systems can preserve expert knowledge and facilitate its transfer from SMEs to human novices. In this way, we shed light on this emerging subfield of human-computer interaction and serve to build an interdisciplinary research agenda.

翻译：随着技能型劳动力短缺问题因人口结构变化而日益严峻，组织正面临保留退休专家知识并将其传授给新手的重大挑战。传统知识转移依赖人际互动，这种方式缺乏可扩展性，且需要大量资源和时间。基于信息技术的教学系统虽解决了可扩展性问题，但其开发过程仍然繁琐耗时。本研究探讨了机器学习模型在组织情境中促进知识转移的潜力，以构建更具成本效益的IT教学系统。通过系统性文献综述，我们审视了核心概念、主题与维度，以更深入理解和设计基于机器学习的教学系统。为此，我们记录并整合了IT教学系统中机器学习模型的功能特性，归纳分析了相关概念及其内在关联。研究成果以核心概念、主题与维度的综述形式呈现，为理解基于机器学习的教学系统提供理论依据。在此基础上，本研究通过概念化机器学习教学系统如何保留专家知识并促进其从小型专家向人类新手的转移，为计算机支持协作工作领域做出理论贡献。由此，我们揭示了人机交互这一新兴子领域，并为构建跨学科研究议程奠定基础。