Generative AI is transforming the educational landscape, raising significant concerns about cheating. Despite the widespread use of multiple-choice questions (MCQs) in assessments, the detection of AI cheating in MCQ-based tests has been almost unexplored, in contrast to the focus on detecting AI-cheating on text-rich student outputs. In this paper, we propose a method based on the application of Item Response Theory (IRT) to address this gap. Our approach operates on the assumption that artificial and human intelligence exhibit different response patterns, with AI cheating manifesting as deviations from the expected patterns of human responses. These deviations are modeled using Person-Fit Statistics (PFS). We demonstrate that this method effectively highlights the differences between human responses and those generated by premium versions of leading chatbots (ChatGPT, Claude, and Gemini), but that it is also sensitive to the amount of AI cheating in the data. Furthermore, we show that the chatbots differ in their reasoning profiles. Our work provides both a theoretical foundation and empirical evidence for the application of IRT to identify AI cheating in MCQ-based assessments.

This work explores the capability of conversational chatbots powered by large language models (LLMs), to understand and characterize predicate symmetry, a cognitive linguistic function traditionally believed to be an inherent human trait. Leveraging in-context learning (ICL), a paradigm shift enabling chatbots to learn new tasks from prompts without re-training, we assess the symmetrical reasoning of five chatbots: ChatGPT 4, Huggingface chat AI, Microsoft's Copilot AI, LLaMA through Perplexity, and Gemini Advanced. Using the Symmetry Inference Sentence (SIS) dataset by Tanchip et al. (2020), we compare chatbot responses against human evaluations to gauge their understanding of predicate symmetry. Experiment results reveal varied performance among chatbots, with some approaching human-like reasoning capabilities. Gemini, for example, reaches a correlation of 0.85 with human scores, while providing a sounding justification for each symmetry evaluation. This study underscores the potential and limitations of LLMs in mirroring complex cognitive processes as symmetrical reasoning.

Mutation testing is a technique aimed at assessing the effectiveness of test suites by seeding artificial faults into programs. Although available for many platforms and languages, no mutation testing tool is currently available for conversational chatbots, which represent an increasingly popular solution to design systems that can interact with users through a natural language interface. Note that since conversations must be explicitly engineered by the developers of conversational chatbots, these systems are exposed to specific types of faults not supported by existing mutation testing tools. In this paper, we present MutaBot, a mutation testing tool for conversational chatbots. MutaBot addresses mutations at multiple levels, including conversational flows, intents, and contexts. We designed the tool to potentially target multiple platforms, while we implemented initial support for Google Dialogflow chatbots. We assessed the tool with three Dialogflow chatbots and test cases generated with Botium, revealing weaknesses in the test suites.

Amid a barrage of Amazon-branded tablets and Alexa-powered tech, Dave Limp, SVP of Amazon Devices and Services, announced the company's digital assistant will soon tap into a purpose-built large language model (LLM) for almost every new Echo device. Amazon set out to design the LLM based on five foundational capabilities. One of these is ensuring interactions are "conversational," and the company claimed it "studied what it takes to make a great conversation. Still waiting on Amazon to add eyes and hand gestures to its Echo devices. Has anyone seen Astro recently?

Devansh Bansal is the VP of the Emerging Tech Business Unit at Damco Solutions. The widespread adoption of chatbots was imminent with the stellar rise and consolidation of instant messaging. However, the accelerated pace at which chatbots have evolved from accepting scripted responses to holding natural-sounding conversations has been unprecedented. According to Google Trends, the interest in AI Chatbots has increased ten-fold over the last five years! With chatbots getting smarter, value-driven, and user-friendly, it has fueled customer-led demand for chatbot-driven interaction at every touchpoint.

Latest trend in AI consists of referring to AI hallucinations, which AI Ethicists find to be ... [ ] misleading and altogether problematic. If you have been keeping up with the latest news about AI, you'd almost certainly believe that you were hallucinating. Wait, hold on, I meant to say that you would almost certainly believe that the AI was hallucinating. And you would have lots of solid reasons for believing so. The notion of AI that hallucinates seems to keep gaining rather wide popularity. This raises all manner of AI Ethics qualms and issues. For my ongoing and extensive coverage of AI Ethics and Ethical AI, see the link here and the link here, just to name a few.

Conversational AI and chatbots are making how we interact with humans more accessible by enabling brand-new ways to connect with those around us. Chatbot technology is quickly transforming businesses, disrupting industries, and changing how we use our time. Find out how conversational AI will change your life in this article! When it comes to conversational AI and chatbots, there's a lot of promise for the future. Chatbots are already being used to interact with customers in various ways, from customer service to marketing.

The pandemic has accelerated the onset of new technologies across industries. With artificial intelligence (AI) expected to grow by 21%, reaching $62 billion in 2022, it's no wonder nearly half of CIOs said they've either already deployed AI or plan to add it to their tech stacks in the next year. And with record-high numbers of Americans leaving their jobs, AI has moved from a "nice-to-have" technology to an essential way to optimize your teams' work and keep everyone feeling productive. The question for most businesses is no longer whether to adopt AI -- it's how to best integrate it into processes that scale with the company. Even though companies have spent the last two years implementing new technologies, often under tight deadlines and without a clear plan of what will come next, the process can still feel intimidating.

The outbreaks of the coronavirus pandemic and digital transformation have created ample opportunities for the retail and e-Commerce industry across the world. The ever-changing industry is allowing retailers, merchants, business people, and many more to enhance e-Commerce businesses to meet customer satisfaction by leveraging advanced AI innovations and technologies. Multiple retail and e-Commerce companies are harnessing smart functionalities of artificial intelligence with a diverse range of products and services including smart payments, seamless transactions, fast delivery, and many more through online mode. Virtual Trial Rooms: Virtual trial rooms are emerging as one of the top AI innovations in the retail and e-Commerce industry that digitally manage customer tastes and preferences of apparel. It is developed with the integration of artificial intelligence and augmented reality to increase customer engagement in this industry.

Chatbots are becoming more critical in organizations, from start-ups to huge corporations, where automation is growing increasingly important. In particular, artificial intelligence-based chatbots are simplifying human-to-machine interactions in a variety of fields, including contact centers and digital banking. Automated service is benefitting banks, especially with conversational chatbots, since individuals are prone to making errors and misreadings, or service is delayed and occasionally unpleasant. In contrast, AI chatbots can work around the clock; they also never get ill, bored, unproductive, or take any time off. Using chatbots in the digital banking and healthcare sectors might save more than $8 billion per year by 2022, according to Juniper's research.