Educators and researchers have analyzed various image data acquired from teaching and learning, such as images of learning materials, classroom dynamics, students' drawings, etc. However, this approach is labour-intensive and time-consuming, limiting its scalability and efficiency. The recent development in the Visual Question Answering (VQA) technique has streamlined this process by allowing users to posing questions about the images and receive accurate and automatic answers, both in natural language, thereby enhancing efficiency and reducing the time required for analysis. State-of-the-art Vision Language Models (VLMs) such as GPT-4V(ision) have extended the applications of VQA to a wide range of educational purposes. This report employs GPT-4V as an example to demonstrate the potential of VLM in enabling and advancing VQA for education. Specifically, we demonstrated that GPT-4V enables VQA for educational scholars without requiring technical expertise, thereby reducing accessibility barriers for general users. In addition, we contend that GPT-4V spotlights the transformative potential of VQA for educational research, representing a milestone accomplishment for visual data analysis in education.