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In the process of Human-Robot Interaction, improving the intelligibility of robots is crucial. Intelligibility refers to the degree to which humans can understand robot behavior and decision-making. When humans interact with low-intelligibility robots, it can lead to a series of problems, such as misunderstanding and trust issues. For old people, various impairments may occur in their physiological functions. Therefore, in the process of Human-Robot Interaction, when the target group is older adults, it is more critical to improve intelligibility. In this thesis project, we propose a solution to improve the intelligibility of robots when interacting with older adults. Specifically, we design an intelligent dialogue system that combines the Rasa dialogue management assistant, the Pepper robot, and the "Nerve" module to provide cognitive exercises. Among them, the user model is used to expand the Rasa dialogue management assistant to adjust the difficulty of cognitive exercises according to the user's hearing, seeing, and cognitive impairment. Rasa could interact with older adults through the Pepper robot. At the same time, we also consider environmental factors in Human-Robot Interaction, such as lighting and noise. For this purpose, we design a "Nerve" module that could adjust the communication modality and parameters during interaction according to environmental factors and user models. We also use some designed personas to evaluate the intelligent system from the five perspectives of speech recognition accuracy, user experience, system stability, intelligibility, and robustness. Finally, we analyze the limitations of the system properties and give a brief introduction to future work.