The field of Human-Computer Interaction (HCI) and Artificial Intelligence (AI) is rapidly evolving, with a focus on developing more sophisticated and human-like interfaces. Recent studies have explored the use of Large Language Models (LLMs) in HCI, including their application in heuristic evaluation, generative AI, and recommendation systems. While LLMs have shown promise in these areas, they also present challenges, such as the need for careful evaluation and validation of their performance. Noteworthy papers in this area include:

• A comparative study on issue identification in heuristic evaluation, which found that LLMs can identify some issues, but struggle with others, and highlighted the need for careful consideration of their limitations.
• A survey of recent progress and future prospects in multi-objective recommendation systems based on generative AI, which highlighted the potential of these systems to improve the performance and versatility of recommendation systems.
• A study on detecting LLM-generated short answers, which demonstrated the potential of fine-tuned LLMs to detect LLM-generated responses with high accuracy, and highlighted the need for careful consideration of the potential for LLM misuse in online learning.
• A paper on curating art exhibitions using machine learning, which presented a series of artificial models that can learn from existing exhibitions and make similar curatorship decisions, and highlighted the potential of AI to support and augment human curatorship.
• A study on leveraging AI graders for missing score imputation, which proposed a novel method for imputing missing scores and demonstrated its potential to achieve accurate ability estimation in constructed-response tests.