Alzheimer's disease (AD) is a neurodegenerative disorder that is characterised by the gradual deterioration of executive functions and memory. Its primary pathophysiological hallmarks are amyloid plaques and neurofibrillary tangles. Promising humanised knock-in AD mouse models have been developed that exhibit Aβ pathology, neuroinflammation, synaptic dysfunction, and behavioural impairment. A rigorous battery of behavioural tests and analyses was utilised to further our knowledge of Alzheimer's disease.Our study comprised young (6-months), middle-aged (12-months), and old (22-months) AppNL-G-F mice and their wild-type littermates. We developed a battery of behavioural tests to evaluate their motor performance, anxiety levels, and memory, including spatial, recognition, and episodic memory. We developed innovative analysis pipelines that incorporate machine learning techniques, such as DeepLabCut, to identify behavioural phenotypes.Our findings revealed that aged AppNL-G-F mice are more hyperactive in the Open Field and display increased thigmotaxis. They also exhibited decreased anxiety and increased exploration in the Elevated Plus Maze (EPM). Young AppNL-G-F mice have a spatial memory impairment, on the forced-choice alternation T-maze. Ongoing analysis will also assess their recognition and reference memory.Our data suggest that aged AppNL-G-Fmice are hyperactive and have decreased anxiety as assessed by EPM. Furthermore, we can detect spatial memory impairments from the age of six months. Our findings underscore the significance of employing rodent exploratory behaviour paradigms for advancing cognitive function understanding in AD. This study contributes to the ongoing effort to standardize behavioural experiments in AD mouse models, facilitating reproducibility and translational impact.