This research explores the opportunities of Generative AI (GenAI) in the realm of higher education through the design and development of a multimodal chatbot for an undergraduate course. Leveraging the ChatGPT API for nuanced text-based interactions and Google Bard for advanced image analysis and diagram-to-code conversions, we showcase the potential of GenAI in addressing a broad spectrum of educational queries. Additionally, the chatbot presents a file-based analyser designed for educators, offering deep insights into student feedback via sentiment and emotion analysis, and summarising course evaluations with key metrics. These combinations highlight the crucial role of multimodal conversational AI in enhancing teaching and learning processes, promising significant advancements in educational adaptability, engagement, and feedback analysis. By demonstrating a practical web application, this research underlines the imperative for integrating GenAI technologies to foster more dynamic and responsive educational environments, ultimately contributing to improved educational outcomes and pedagogical strategies.

The development of Generative AI Large Language Models (LLMs) raised the alarm regarding identifying content produced through generative AI or humans. In one case, issues arise when students heavily rely on such tools in a manner that can affect the development of their writing or coding skills. Other issues of plagiarism also apply. This study aims to support efforts to detect and identify textual content generated using LLM tools. We hypothesize that LLMs-generated text is detectable by machine learning (ML), and investigate ML models that can recognize and differentiate texts generated by multiple LLMs tools. We leverage several ML and Deep Learning (DL) algorithms such as Random Forest (RF), and Recurrent Neural Networks (RNN), and utilized Explainable Artificial Intelligence (XAI) to understand the important features in attribution. Our method is divided into 1) binary classification to differentiate between human-written and AI-text, and 2) multi classification, to differentiate between human-written text and the text generated by the five different LLM tools (ChatGPT, LLaMA, Google Bard, Claude, and Perplexity). Results show high accuracy in the multi and binary classification. Our model outperformed GPTZero with 98.5\% accuracy to 78.3\%. Notably, GPTZero was unable to recognize about 4.2\% of the observations, but our model was able to recognize the complete test dataset. XAI results showed that understanding feature importance across different classes enables detailed author/source profiles. Further, aiding in attribution and supporting plagiarism detection by highlighting unique stylistic and structural elements ensuring robust content originality verification.

Apple's spin on AI is finally here, and it already seems smarter than Microsoft Copilot and Google Bard. Apple Intelligence focuses on privacy and "personal intelligence," with a bit of an assist from ChatGPT. While we haven't tested it ourselves yet, Apple appears to be avoiding the pitfalls of Microsoft's Recall feature, as well as Google Bard's unfortunate early gaffes. The company isn't trying to capture everything you're doing on your computer, and it's being careful about how it's using larger AI models like ChatGPT. Shortly after the WWDC 2024 keynote ended, Engadget's Cherlynn Low and Devindra Hardawar discussed why they think Apple is taking a more thoughtful approach to AI.

The advent of Generative AI (GenAI) in education presents a transformative approach to traditional teaching methodologies, which often overlook the diverse needs of individual students. This study introduces a GenAI tool, based on advanced natural language processing, designed as a digital assistant for educators, enabling the creation of customized lesson plans. The tool utilizes an innovative feature termed 'interactive mega-prompt,' a comprehensive query system that allows educators to input detailed classroom specifics such as student demographics, learning objectives, and preferred teaching styles. This input is then processed by the GenAI to generate tailored lesson plans. To evaluate the tool's effectiveness, a comprehensive methodology incorporating both quantitative (i.e., % of time savings) and qualitative (i.e., user satisfaction) criteria was implemented, spanning various subjects and educational levels, with continuous feedback collected from educators through a structured evaluation form. Preliminary results show that educators find the GenAI-generated lesson plans effective, significantly reducing lesson planning time and enhancing the learning experience by accommodating diverse student needs. This AI-driven approach signifies a paradigm shift in education, suggesting its potential applicability in broader educational contexts, including special education needs (SEN), where individualized attention and specific learning aids are paramount

Background: The emergence of generative AI tools, empowered by Large Language Models (LLMs), has shown powerful capabilities in generating content. To date, the assessment of the usefulness of such content, generated by what is known as prompt engineering, has become an interesting research question. Objectives Using the mean of prompt engineering, we assess the similarity and closeness of such contents to real literature produced by scientists. Methods In this exploratory analysis, (1) we prompt-engineer ChatGPT and Google Bard to generate clinical content to be compared with literature counterparts, (2) we assess the similarities of the contents generated by comparing them with counterparts from biomedical literature. Our approach is to use text-mining approaches to compare documents and associated bigrams and to use network analysis to assess the terms' centrality. Results The experiments demonstrated that ChatGPT outperformed Google Bard in cosine document similarity (38% to 34%), Jaccard document similarity (23% to 19%), TF-IDF bigram similarity (47% to 41%), and term network centrality (degree and closeness). We also found new links that emerged in ChatGPT bigram networks that did not exist in literature bigram networks. Conclusions: The obtained similarity results show that ChatGPT outperformed Google Bard in document similarity, bigrams, and degree and closeness centrality. We also observed that ChatGPT offers linkage to terms that are connected in the literature. Such connections could inspire asking interesting questions and generate new hypotheses.

In the filing, Cohen wrote that he had not kept up with "emerging trends (and related risks) in legal technology and did not realize that Google Bard was a generative text service that, like ChatGPT, could show citations and descriptions that looked real but actually were not." To him, he said, Google Bard seemed to be a "supercharged search engine."

Jack Krawczyk discusses how Google Bard helps users connect and communicate -- and what the future holds for the platform. Michael Cohen, former President Trump's onetime fixer and lawyer, admitted in a filing unsealed Friday that he inadvertently gave his lawyer fake legal case citations generated by artificial intelligence in connection with a motion to end his supervised release early. U.S. District Judge Jesse M. Furman previously called the citations into question, writing earlier this month, "In the letter brief, Mr. Cohen asserts that, "[a]s recently as 2022, there have been District Court decisions, affirmed by the Second Circuit Court, granting early termination of supervised release." Furman added, "As far as the Court can tell, none of these cases exist." Cohen said in his sworn declaration released Friday that he had found the phony citations through Google Bard, an AI service that he said he thought was a "supercharged" search engine. Michael Cohen admitted to inadvertently citing fake legal cases in a motion to end his early release in a sworn declaration released Friday. "As a non-lawyer, I have not kept up with emerging trends (and related risks) in legal technology and did not realize that Google Bard was a generative text service that, like Chat-GPT, could show citations and descriptions that looked real but actually were not," Cohen said. "Instead, I understood it to be a super-charged search engine and had repeatedly used it in other contexts to (successfully) find accurate information online." In 2018, Cohen pleaded guilty to tax evasion, campaign finance charges and lying to Congress, spending more than a year in prison before he was put on supervised release. He was also disbarred as a lawyer. "It did not occur to me then and remains surprising to me now--that Mr. Schwartz would drop the cases into his submission wholesale without even confirming that they existed," he added, citing his lawyer David Schwartz. "I deeply regret any problems Mr. Schwartz's filing may have caused." He said Schwartz's alleged mistake was "a product of inadvertence, not any intent to deceive." E. Danya Perry, who represents Cohen and discovered the citations were fake, told the judge, "Mr.

Donald Trump's former "fixer," Michael Cohen, used Google Bard to cite made-up legal cases that ended up in a federal court. The New York Times reported Friday that Cohen admitted in unsealed court papers that he passed on documents referencing bogus cases to his lawyer, who then relayed them to a federal judge. Cohen reportedly wrote in the sworn declaration he hadn't stayed on top of "emerging trends (and related risks) in legal technology." Cohen's legal team filed the paperwork in a motion asking for an early end to court supervision from his 2018 campaign finance case, for which he served three years in prison. After Cohen's attorney, David M. Schwartz, presented the legal documents to the federal court, Judge Jesse M. Furman of the Federal District Court said he was having trouble finding the three decisions cited by Schwartz (via Cohen).

Much has been made of AI chatbots' ability to summarize PDF files or long Web pages. But there's a gigantic time-saver that Google's Bard is beginning to deploy, and it's worth checking out: Knowledge of the gazillion hours of YouTube video it's already archived. On November 21, Google took "the first steps in Bard's ability to understand YouTube videos," according to the list of Bard updates that Google publishes. Coming as it did the week of Thanksgiving and Black Friday, the improvement didn't generate much notice. But after playing around with it, I have to say that there's an enormous amount of hidden potential.

Artificial Intelligence (AI) is already revolutionising the way we work and conduct research, and its future impact is challenging to predict. Concerning qualitative content analysis, recent studies demonstrate its usefulness for coding research interviews, a fundamental tool for data collection across numerous academic disciplines (Lopezosa and Codina, 2023; Zhang et al., 2023). However, its use is incipient and there are still not many experiences in the scientific literature, despite the need to analyse and closely monitor the development of tools with the potential to bring about such profound changes. Consequently, this paper illustrates its practical application in a real case where interviews were initially manually coded using expert criteria. These interviews were carried out as part of a broader study aimed at evaluating the changes in landscape quality that occurred on a small island in Cuba (Cayo Santa María) as a result of tourism development (Burgui et al., 2018).