An Artificial Intelligence-Driven Adaptive Interaction Model for Adult Mobile Learning

Adult mobile learning is characterized by fragmentation, task orientation, and contextembedded features, which pose substantial challenges to conventional adaptive systems in generating real-time interaction strategies that simultaneously accommodate learners’ cognitive states and contextual constraints on mobile devices. To address these challenges, Mobile Adult Learning Adaptive Interaction Model (MAL-ADAPT), a context–cognition dual-channel adaptive interaction model driven by a lightweight on-device large language model agent, was proposed. Adaptive interaction was formulated as a constrained partially observable Markov decision process, in which learning gain, engagement, and learner autonomy were jointly optimized subject to latency, energy, memory, and privacy constraints. At the perception layer, behavioral, device, and environmental signals were integrated to construct a contextual state representation. At the cognitive layer, mastery probabilities and associated uncertainty were estimated through low-rank knowledge tracing. At the interaction layer, a unified state representation was constructed through a gated interaction mechanism. State-conditioned soft prompts were then injected into the on-device large language model, and executable adaptive actions were generated through low-rank adaptation-based parameter adaptation, action-space mapping, an adult-learning adaptation score, and feasibility masking. In addition, an edge–cloud hybrid inference gate was introduced to invoke cloud-enhanced inference under complex conditions when network connectivity permitted, while direct transmission of raw behavioral data was avoided. Six groups of experiments were conducted to evaluate component contributions, on-device performance, policy effectiveness, real-world use, robustness, and constraint sensitivity. The results demonstrate that MAL-ADAPT improved learner engagement, strategy acceptance, and self-regulated learning under mobile resource constraints while reducing response latency and dependence relative to cloud-based approaches. These findings provide a reusable technical pathway for on-device educational agents.