With the evolution of China's market economy, the securities market is increasingly anchoring a pivotal role in the nation's economic landscape. Consequently, stock trend forecasting has garnered heightened attention among scholars and practitioners. This research pioneers the use of multimodal information to predict stock market fluctuations. Based on our experimental results, LSTM + Transformer performs better in handling multimodal data for stock movement prediction tasks regarding accuracy, F1-score, precision, and recall. Additionally, we employed the Granger causality test and Impulse response test to investigate the causal relationships between sentiment and stock trends, as well as the interplay between COVID-related indicators and stock trajectories. We identified discernible causal links between sentiments, COVID indicators, and stock trends for select pharmaceutical stocks. Our findings can provide valuable guidance for investors and market regulators, especially within the pharmaceutical industry. Understanding investor sentiment and the impact of the pandemic on severity can assist in effective stock commentary management and improve investment strategies.

University students often face a myriad of challenges that can adversely affect their learning and overall health. However, re-search has shown that a minimal number of students access sup-port for their learning and well-being problems. In this project, we aim to evaluate the feasibility of using artificial intelligence (AI) chatbots powered by the generative pre-trained transformer (GPT) to support student learning and well-being. We consulted with clinical psychologists and counselors to identify objectives and iteratively develop the AI conversational agent to align closely with real-world scenarios. 63 questions from different domains students might pose are designed to assess the effectiveness and professionalism of the developed AI conversational agent’s re-sponse. The chatbot's performance was evaluated using context-specific metrics. Additionally, students were recruited to engage in conversations with the chatbot, and their feedback was collect-ed for thematic analysis. Our findings suggest that the develop-ment and evaluation of this AI conversational agent present a promising avenue for addressing student well-being concerns within university environments, while there remains a need to advance their capacity for personalized, empathetic interactions to meet users' needs and expectations fully.

Artificial intelligence (AI) systems and tools are increasingly reshaping educational practices. This study examines perspectives shared in student-focused online communities on AI’s impact on education, comparing those of computer science (CS) and business students through an analysis of Reddit posts. Using natural language processing (NLP), sentiment analysis, and Latent Dirichlet Allocation (LDA) topic modeling, we analyzed 1108 posts collected from six subreddits. Results reveal distinct thematic focuses: CS students emphasize technical aspects, including programming efficiency, coding assistance, and concerns about job displacement, while business students focus on decision-making enhancement, financial analysis applications, and operational efficiency. Sentiment analysis indicates that the Business/Finance-oriented corpus is slightly more positive than the CS-oriented corpus (51.9% vs. 50.1% positive). The CS-oriented corpus also contains a higher proportion of negative posts (36.0% vs. 33.2%). These differences reflect discipline-specific epistemological frameworks shaping AI perception. The findings provide educators with guidelines for developing tailored AI integration strategies that address discipline-specific concerns and opportunities. This study contributes to understanding how academic background influences perceptions of AI in education, offering insights for curriculum design and policy development.