While living longer at home is the preferred option for the elderly, disease, accidents or general health decline might make this hard. Fall accidents, which are among the chief causes of unforced injury leading to hospitalization, must be viewed as a strong factor working against the possibilities for the elderly of living longer at home. Although many fall prevention programs have been developed and applied, and checklists and advice abound, most of these originate in the professional care area or depend on care professionals. In this paper, we present a fall prevention concept directed towards elderly people living at home, with the intention of providing a safer environment ensuring more years of doing this in a healthy manner. There are several obstacles for a successful implementation of fall prevention, many pending on the attitudes and thought systems of the elderly user community, including low risk awareness, technological stigmata, refusal to be associated with health problems and a general “not for me” world view. The paper presents an AI/IoT-driven learning environment for fall prevention which facilitates living independently and improved self-understanding to provide for easier everyday living. The central design idea is to lead the user through a learning process including acquiring knowledge of fall prevention, personal characteristics, taking fall prevention action and understanding the outcomes of changed behaviors. The concluding outcome is a discussion of a set of design variables for the effective implementation of fall prevention systems, including a holistic view of fall prevention and aging, open platform technologies, a flexible and adaptable module approach, feedback-based learning, IoT/sensor-driven learning, personalization and goal orientation and the dual use of technology avoiding technology stigmas.