Paving the Way for AI-based Coaching System: A Systematic Review and Future Research Directions of Technology-Based Interventions for Novice Drivers

Young drivers remain disproportionately represented in crash and fatality statistics worldwide, with inexperience and skills deficiencies identified as critical factors. Current state of practices, such as Graduated Licensing Systems (GLS), have reduced crash risks but may have some limitations in preparing drivers for independent operation. In response, technological interventions ranging from simulation-based studies and field-based studies to emerging multimodal large language model (MLLM) applications have been developed to provide both retrospective feedback and real-time advice. This systematic review analysed 46 studies retrieved from six databases (Scopus, IEEE Xplore, ScienceDirect, Web of Science, TRID, and Taylor & Francis) published between 2006 and 2025. The types of technology-based intervention systems designed to improve novice driver performance, the driving skills targeted, the modalities of data collection, the effectiveness of different intervention strategies were analysed. This review found that current systems in literature (field-based, simulationbased, and emerging AI-based) target a limited range of skills, with most focusing on low-order driving skills such as vehicle control, vehicle manoeuvring, and road rules. Field studies relied mainly on kinematic data, occasionally supplemented by manually annotated videos, and emphasised retrospective feedback, which rarely produced significant behavioural changes unless combined with incentives or parental involvement. Real-time interventions were largely restricted to simple auditory or visual reactive alerts. Simulation studies utilised richer, multimodal data and supported diverse interventions, achieving improvements in skills such as vehicle control, observation, and hazard perception, although the effects remained confined to 2 virtual settings. Emerging AI-driven approaches using LLMs and MLLMs show promise but remain in their infancy. Research gaps and future research directions needed to develop a coaching system for novice drivers were identified. Persistent challenges include reliance on single-modality data, lack of real-world validation, centralized architectures, and the absence of human-centric, prioritized real-time advice. This review highlights the potential of lightweight, edge-deployable MLLMs, multimodal integration, adaptive prioritization frameworks, lack of personalised and adaptive coaching systems, and limited number of targeted skills to overcome these limitations. Collectively, the findings underscore the need for comprehensive, skill-based, and personalised coaching systems that can further prepare novice drivers for the complexities of independent driving.