In recent years, large language models (LLMs) have been employed significantly in different domains of computing education. Nevertheless, these models have been focused on essential adherence to their integration as coding assistants in computing education. However, attention has been switched to thoroughly examining and analyzing LLM behavior, particularly in computing education for programming tasks such as code generation, code explanation, and programming error message explanation. Therefore, it becomes imperative to understand their behavior to examine potential pitfalls. This article addresses this gap systematically and details how different LLM-based coding chatbots, such as ChatGPT, Codex, Copilot, and others, react to various coding inputs within computing education. To achieve this objective, we collected and analyzed articles from 2021 to 2024, and 72 studies were thoroughly examined. These objectives include investigating the existing limitations and challenges associated with utilizing these systems for coding tasks, assessing their responses to prompts containing coding syntax, examining the impact of their output on student learning, and evaluating their performance as debugging tools. The findings of this review highlight that it is premature to incorporate these systems into computing education due to their limitations that may limit their effectiveness as comprehensive coding assistants for computer science students. These limitations include issues with handling prompts containing code snippets, potential negative impacts on student learning, limited debugging capabilities, and other ineffectiveness. The finding also reports multiple research directions that can be considered in future research related to LLMs in computing education.

Financial economic research has extensively documented the fact that the impact of the arrival of negative news on stock prices is more intense than that of the arrival of positive news. The authors of the present study followed an innovative approach based on the utilization of two artificial intelligence algorithms to test that asymmetric response effect. Methods: The first algorithm was used to web-scrape the social network Twitter to download the top tweets of the 24 largest market-capitalized publicly traded companies in the world during the last decade. A second algorithm was then used to analyze the contents of the tweets, converting that information into social sentiment indexes and building a time series for each considered company. After comparing the social sentiment indexes’ movements with the daily closing stock price of individual companies using transfer entropy, our estimations confirmed that the intensity of the impact of negative and positive news on the daily stock prices is statistically different, as well as that the intensity with which negative news affects stock prices is greater than that of positive news. The results support the idea of the asymmetric effect that negative sentiment has a greater effect than positive sentiment, and these results were confirmed with the EGARCH model.

Transfer Entropy was applied to analyze the correlations and flow of information between 200,500 tweets and 23 of the largest capitalized companies during 6 years along the period 2013-2018. The set of tweets were obtained applying a text mining algorithm and classified according to daily date and company mentioned. We proposed the construction of a Sentiment Index applying a Natural Processing Language algorithm and structuring the sentiment polarity for each data set. Bootstrapped Simulations of Transfer Entropy were performed between stock prices and Sentiment Indexes. The results of the Transfer Entropy simulations show a clear information flux between general public opinion and companies’ stock prices. There is a considerable amount of information flowing from general opinion to stock prices, even between different Sentiment Indexes. Our results suggest a deep relationship between general public opinion and stock prices. This is important for trading strategies and the information release policies for each company.

In this study, a database of approximately 50 million tweets was used for the estimation of the positive and negative sentiment factors for 2557 companies operating in US stock market. For each company, the sentiment factors were calculated through the mean equations on GARCH models of different orders. Our findings show that, for 503 companies the negative factor effect has a greater impact than the positive factor effect. The period analyzed was from October 2022 to January 2023, using hourly observations. Results provide evidence to support that there is an asymmetric effect from the factors traveling to the stock market and it takes at least an hour the signal to travel. The investors and regulatory agents can find useful the results given that news has been demonstrated a source of influence in the market. Therefore, news impact can be modeled into portfolio theory using GARCH which is easy to implement and to interpret. Given the exposure of prices and volatility to news, it can be considered that these findings provide evidence to support efficient market hypothesis. Modeling returns and volatility for the assets through GARCH family is a widely known tool. Including the news sentiment on social media is dually a novelty: the empirical demonstration of the effects of social comments on the stock performance and volatility, in addition to the use of a large data set of social network comments in an hourly frequency.

El artículo analiza el impacto de los comentarios en redes sociales sobre el rendimiento de las acciones de los bancos en el mercado de valores de EE.UU. Se empleó inteligencia artificial para monitorear y extraer comentarios en tiempo real, y se utilizó el procesamiento de lenguaje natural para calcular el sentimiento de cada comentario. Los comentarios se clasificaron como positivos o negativos y se agregaron, por hora, para cada banco durante el período observado. Los resultados mostraron que tanto los comentarios positivos como los negativos tienen un efecto significativo en el rendimiento de las acciones, con un impacto asimétrico más pronunciado en el caso de los comentarios negativos. Este estudio contribuye a la comprensión de cómo la interacción en redes sociales puede influir en el valor de mercado de las empresas y destaca la importancia para las compañías que cotizan en bolsa de monitorear y gestionar la percepción en línea.