In this article, we assess the visualization literacy of two prominent Large Language Models (LLMs): OpenAI's Generative Pretrained Transformers (GPT), the backend of ChatGPT, and Google's Gemini, previously known as Bard, to establish benchmarks for assessing their visualization capabilities. While LLMs have shown promise in generating chart descriptions, captions, and design suggestions, their potential for evaluating visualizations remains under-explored. Collecting data from humans for evaluations has been a bottleneck for visualization research in terms of both time and money, and if LLMs were able to serve, even in some limited role, as evaluators, they could be a significant resource. To investigate the feasibility of using LLMs in the visualization evaluation process, we explore the extent to which LLMs possess visualization literacy-a crucial factor for their effective utility in the field. We conducted a series of experiments using a modified 53-item Visualization Literacy Assessment Test (VLAT) for and . Our findings indicate that the LLMs we explored currently fail to achieve the same levels of visualization literacy when compared to data from the general public reported in VLAT, and LLMs heavily relied on their pre-existing knowledge to answer questions instead of utilizing the information provided by the visualization when answering questions.

This study aimed to establish an accurate baseline for Facebook usage and to investigate its learning potential via biometric perspective. This was explored by asking college students to browse their own Facebook page and peruse the content while wearing a high-fidelity EEG headset to record brainwave activity they experienced. Their actions were then coded using the empirically based Interactive-Constructive-Active-Passive (ICAP) engagement model to identify elements that could potentially be leveraged for learning. Based on these results, Facebook does not seem to make an ideal learning platform, due to higher frustration levels during higher cognitive tasks compared to passively consuming personally relevant non-instructional content. However, it may have more positive impacts on learning as long as initial frustration level is transformed smoothly to engagement and motivation for some learning opportunities and activities.