Personalised AI Coaching Technology: A Systematic Mapping Review

Personalised AI coaching systems are increasingly used across healthcare, education, workplace training, and sport to support behaviour change and skill development through adaptive interventions. Despite this growth, the field remains fragmented, with limited systematic understanding of how such systems are designed, implemented, and evaluated. This systematic mapping review examines how personalised AI coaching systems are conceptualised and developed, focusing on application domains, target users, AI techniques, personalisation strategies, and interface modalities. Peer-reviewed journal and conference papers in English were included if they described a coaching technology, employed AI-driven methods to select or generate coaching interventions, and implemented automatic or semi-automatic personalisation. Following PRISMA-ScR guidelines, a comprehensive search across ACM Digital Library, IEEE Xplore, PubMed, and Web of Science retrieved 2072 records, of which 42 met the inclusion criteria. Data were systematically extracted on domains, user populations, coaching scope, AI techniques for intervention selection and personalisation, user modelling, theoretical grounding, interface modalities, and evaluation methods. Quantitative charting and qualitative synthesis were used to map trends and identify gaps. Results indicate that most systems targeted lay users, predominantly in healthcare, with fewer studies in education and minimal presence in professional or workplace settings. The recent uptake of deep learning introduced greater methodological diversity and enabled expansion across application domains. Personalisation largely focused on individual-level adaptation, was seldom grounded in theory or expert input, and was less frequently evaluated than coaching outcomes, often without independent assessment. Most systems relied on graphical user interfaces, while robotic, virtual reality, and game-based interfaces remained rare. Future research should extend beyond health to underrepresented domains and user groups, advance hybrid AI approaches that balance interpretability and performance, and strengthen theory-grounded and ethically informed design. Improved reporting transparency, more rigorous evaluation of personalisation effects, and clearer pathways from research prototypes to real-world deployment are also needed.