The burgeoning development of generative artificial intelligence (GenAI) and the widespread adoption of large language models (LLMs) in educational settings have sparked considerable debate regarding their efficacy and acceptability.Despite the potential benefits, the assimilation of these cutting-edge technologies among educators exhibits a broad spectrum of attitudes, from enthusiastic advocacy to profound skepticism.This study aims to dissect the underlying factors influencing educators' perceptions and acceptance of GenAI and LLMs.We conducted a survey among educators and analyzed the data through the frameworks of the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT). Our investigation reveals a strong positive correlation between the perceived usefulness of GenAI tools and their acceptance, underscoring the importance of demonstrating tangible benefits to educators. Additionally, the perceived ease of use emerged as a significant factor, though to a lesser extent, influencing acceptance. Our findings also show that the knowledge and acceptance of these tools is not uniform, suggesting that targeted strategies are required to address the specific needs and concerns of each adopter category to facilitate broader integration of AI tools.in education.

In recent years, JavaScript has become the most widely used programming language, especially in web development. However, writing secure JavaScript code is not trivial, and programmers often make mistakes that lead to security vulnerabilities in web applications. Large Language Models (LLMs) have demonstrated substantial advancements across multiple domains, and their evolving capabilities indicate their potential for automatic code generation based on a required specification, including automatic bug fixing. In this study, we explore the accuracy of LLMs, namely ChatGPT and Bard, in finding and fixing security vulnerabilities in JavaScript programs. We also investigate the impact of context in a prompt on directing LLMs to produce a correct patch of vulnerable JavaScript code. Our experiments on real-world software vulnerabilities show that while LLMs are promising in automatic program repair of JavaScript code, achieving a correct bug fix often requires an appropriate amount of context in the prompt.

Large language models (LLMs) are able to engage in natural-sounding conversations with humans, showcasing unprecedented capabilities for information retrieval and automated decision support. They have disrupted human-technology interaction and the way businesses operate. However, technologies based on generative artificial intelligence (GenAI) are known to hallucinate, misinform, and display biases introduced by the massive datasets on which they are trained. Existing research indicates that humans may unconsciously internalize these biases, which can persist even after they stop using the programs. This study explores the cultural self-perception of LLMs by prompting ChatGPT (OpenAI) and Bard (Google) with value questions derived from the GLOBE project. The findings reveal that their cultural self-perception is most closely aligned with the values of English-speaking countries and countries characterized by sustained economic competitiveness. Recognizing the cultural biases of LLMs and understanding how they work is crucial for all members of society because one does not want the black box of artificial intelligence to perpetuate bias in humans, who might, in turn, inadvertently create and train even more biased algorithms.

This research paper delves into the evolving landscape of fine-tuning large language models (LLMs) to align with human users, extending beyond basic alignment to propose "personality alignment" for language models in organizational settings. Acknowledging the impact of training methods on the formation of undefined personality traits in AI models, the study draws parallels with human fitting processes using personality tests. Through an original case study, we demonstrate the necessity of personality fine-tuning for AIs and raise intriguing questions about applying human-designed tests to AIs, engineering specialized AI personality tests, and shaping AI personalities to suit organizational roles. The paper serves as a starting point for discussions and developments in the burgeoning field of AI personality alignment, offering a foundational anchor for future exploration in human-machine teaming and co-existence.

This paper looks at the ability of large language models to participate in educational guided reading. We specifically, evaluate their ability to generate meaningful questions from the input text, generate diverse questions both in terms of content coverage and difficulty of the questions and evaluate their ability to recommend part of the text that a student should re-read based on the student's responses to the questions. Based on our evaluation of ChatGPT and Bard, we report that, 1) Large language models are able to generate high quality meaningful questions that have high correlation with the input text, 2) They generate diverse question that cover most topics in the input text even though this ability is significantly degraded as the input text increases, 3)The large language models are able to generate both low and high cognitive questions even though they are significantly biased toward low cognitive question, 4) They are able to effectively summarize responses and extract a portion of text that should be re-read.

Popular AI chatbots like ChatGPT and Bard have been designed to replicate human speech as closely as possible. And as deep learning technology gets more and more sophisticated, it's becoming difficult to discern these computer models from real people. Now, a free online game gives you two minutes to have a conversation with someone (or something) and guess whether they're a fellow human or an AI. 'Human or not?' was inspired by the Turing Test, devised by legendary British computer scientist Alan Turing in 1950. A computer passes the so-called test when someone cannot correctly tell the difference between a response from a human and a response from an AI.

Originally published on Towards AI. Are current state-of-the-art AI systems creative? Based on the state of my social feeds, it certainly seems like it. Hardly a day goes by when I don't see some incredible new "creative" feat achieved by an AI system like Bard, ChatGPT, or GPT-4. This apparent creativity is driving unprecedented public hype around AI.

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In today's world of generative AI chatbots, we've witnessed the sudden rise of OpenAI's ChatGPT, introduced in November, followed by Bing Chat in February and Google's Bard in March. We decided to put these chatbots through their paces with an assortment of tasks to determine which one reigns supreme in the AI chatbot arena. Since Bing Chat uses similar GPT-4 technology as the latest ChatGPT model, we opted to focus on two titans of AI chatbot technology: OpenAI and Google. We tested ChatGPT and Bard in seven critical categories: dad jokes, argument dialog, mathematical word problems, summarization, factual retrieval, creative writing, and coding. For each test, we fed the exact same instruction (called a "prompt") into ChatGPT (with GPT-4) and Google Bard.

Generative AI models are the hot new thing in the Big Tech world, and everyone is joining the race. The buzz really only started with OpenAI's ChatGPT chatbot, a generative AI language model that is incredibly good at predicting which words should follow one another when you feed it with prompts. Google has long been working on a similar technology, dubbed LaMDA, and with ChatGPT taking the world by storm, the company saw itself forced to release some version of its AI model to the world. That's how we got Bard, Google's first publicly available chat-based generative language model, with access to many parts of the internet. But is Google really at the same level as ChatGPT already?