This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge. The challenge focuses on processing eye movement recorded with event cameras and predicting the pupil center of the eye. The challenge emphasizes efficient eye tracking with event cameras to achieve good task accuracy and efficiency trade-off. During the challenge period, 38 participants registered for the Kaggle competition, and 8 teams submitted a challenge factsheet. The novel and diverse methods from the submitted factsheets are reviewed and analyzed in this survey to advance future event-based eye tracking research. The challenge aims to invite participants to explore algorithms for the event-based eye tracking task on a recorded eye tracking DVS dataset. By focusing on efficient algorithms capable of extracting meaningful information from sparse event streams, this challenge aims to pursue advancements in eye tracking technologies that are both energy-efficient and suitable for real-time applications in AR/VR technologies and wearable healthcare devices. The 3ET+ dataset contains real events recorded with a DVXplorer Mini event camera. There are 13 subjects in total, each having 2-6 recording sessions. The subjects are required to perform 5 classes of activities: random, saccades, read text, smooth pursuit and blinks. The total data volume is 9.2 GB. The ground truth is labeled at 100Hz and consists of two parts for each label: (1) a binary value indicating whether there was an eye blink or not; (2) human-labeled pupil center coordinates. The task is to predict the pupil center spatial coordinate at required timestamps in the input space. The primary metric used is p-accuracy. The challenge phases include dataset preparation, Kaggle competition, and submission system closure. The challenge results show that all teams achieved very high p10 accuracy on the task. Participants proposed various methods, including stateful models, computation and parameter efficiency, event representations, and other innovations. The challenge results show that the field of event-based visual processing is a newly emerging field with a large variety in event data processing. Hardware consideration is essential for researchers developing algorithms for event cameras. The challenge and existing works proved the feasibility of using an event camera for the eye-tracking task. Prototyping and more realistic settings are needed to step towards more mature event-based eye tracking systems.This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge. The challenge focuses on processing eye movement recorded with event cameras and predicting the pupil center of the eye. The challenge emphasizes efficient eye tracking with event cameras to achieve good task accuracy and efficiency trade-off. During the challenge period, 38 participants registered for the Kaggle competition, and 8 teams submitted a challenge factsheet. The novel and diverse methods from the submitted factsheets are reviewed and analyzed in this survey to advance future event-based eye tracking research. The challenge aims to invite participants to explore algorithms for the event-based eye tracking task on a recorded eye tracking DVS dataset. By focusing on efficient algorithms capable of extracting meaningful information from sparse event streams, this challenge aims to pursue advancements in eye tracking technologies that are both energy-efficient and suitable for real-time applications in AR/VR technologies and wearable healthcare devices. The 3ET+ dataset contains real events recorded with a DVXplorer Mini event camera. There are 13 subjects in total, each having 2-6 recording sessions. The subjects are required to perform 5 classes of activities: random, saccades, read text, smooth pursuit and blinks. The total data volume is 9.2 GB. The ground truth is labeled at 100Hz and consists of two parts for each label: (1) a binary value indicating whether there was an eye blink or not; (2) human-labeled pupil center coordinates. The task is to predict the pupil center spatial coordinate at required timestamps in the input space. The primary metric used is p-accuracy. The challenge phases include dataset preparation, Kaggle competition, and submission system closure. The challenge results show that all teams achieved very high p10 accuracy on the task. Participants proposed various methods, including stateful models, computation and parameter efficiency, event representations, and other innovations. The challenge results show that the field of event-based visual processing is a newly emerging field with a large variety in event data processing. Hardware consideration is essential for researchers developing algorithms for event cameras. The challenge and existing works proved the feasibility of using an event camera for the eye-tracking task. Prototyping and more realistic settings are needed to step towards more mature event-based eye tracking systems.