AI Bot Research Articles

AI-CiciBot as Conversational Partners in EFL Education, focusing on Intelligent Technology Adoption (ITA) to Mollify Speaking Anxiety

This study examines the role of AI-CiciBot as a conversational partner in mitigating speaking anxiety among Indonesian EFL (English as a Foreign Language) students. The aim is to explore the integration of intelligent technology adoption (ITA) into classrooms to enhance language learning and reduce anxiety during speaking activities. The primary gap addressed in this research is the lack of technology-based solutions for oral language practice in Indonesia, where traditional curricula emphasize reading and writing over speaking. This study aims to fill the gap by introducing AI-CiciBot, a novel AI-powered conversational tool that provides a supportive, low-pressure environment for students to practice speaking English. The study uses a mixed-methods approach, combining quantitative data from pre-test and post-test scores and qualitative insights from student interviews. The research tools include a questionnaire and an AI-CiciBot platform for speaking practice. IBM SPSS Statistics analyzed the data, focusing on students' speaking performance and anxiety levels before and after using AI-CiciBot. Findings in this study show a significant improvement in students' speaking skills and a reduction in anxiety. The mean score for pre-tests was 49.17, which increased to 90.47 in post-tests, indicating AI-CiciBot's effectiveness. Interviews revealed that students initially hesitant to engage in speaking activities due to fear of judgment became more confident and willing to participate after practicing with the AI bot. This study contributes to the field of EFL education by demonstrating the potential of AI-driven tools like AI-CiciBot to address speaking anxiety and improve oral proficiency. It also highlights the importance of ITA in encouraging students to embrace AI-based learning solutions. To maximize learning outcomes, future research should investigate the long-term impact of AI-CiciBot on students' speaking proficiency and its integration with human instructors.

An exploratory study of AI participants' views on using AI in business hotels

This study presents a novel approach to exploring the viewpoints of AI bots regarding the utilization of AI in business hotels using thematic analysis. Interviews were conducted with AI bots serving as the subjects. The rationale for this approach is that AI bots have undergone extensive training using a wide range of data from different internet sources. Therefore, having an interview with an AI bot can be viewed as an interaction with an entity that embodies collective viewpoints and information derived from the internet. The study has unearthed crucial themes that offer invaluable insights to industry participants and policymakers, contributing to the expansion of existing literature by generating novel knowledge and fostering a comprehensive understanding. The pioneering approach of incorporating AI bots as participants in interviews opens up fresh avenues for future research endeavours and engenders discussions and debates concerning the use of AI bots as participants.

Say what you want: Evaluation and engagement with YouTube broadcasts on ChatGPT

ChatGPT caused a worldwide sensation upon its launch, as evidenced by the extensive coverage across traditional and new media. This paper delves into the diverse evaluative discourses about this AI chatbot both in YouTube news videos (originally, television broadcasts) and in user comments facilitated by YouTube’s interactivity. The qualitative-quantitative discourse analysis probes diversified evaluations within YouTube broadcast talk, together with their impact on viewers’ perceptions of the broadcasts’ stance and viewers’ evaluations of ChatGPT and the broadcasts, as reflected by user comments. The findings present balanced effects, speaking to polarised opinions and mixed feelings about this AI development, but point to a potential boomerang effect in users’ evaluation of the new AI bot relative to the broadcasts considered to be biased. The study also reports low user engagement with the broadcasts. This challenges the notion of user engagement with broadcast content as lying at the heart of user interactivity on YouTube.

An initial exploration of semi-automated tutoring

In this paper, we begin our process of incorporating an AI bot in an online chat tutoring setting as a support for the tutor. We explore how an AI bot could give suggestions for tutor messages, although the human tutor will control how to communicate with the student. Tutoring, an important dimension of networked learning, has long been seen as a beneficial approach to students’ learning. An AI bot has the potential to aid tutors in the tutoring process and contribute to the scalability. The present pilot study was conducted in the tutoring setting of the Math Coach program. In the program, teacher students aid students from upper primary school to upper secondary school in mathematics through an online text-based chat system. Llama2 was used as a large language model (LLM), fine-tuned for Swedish comprehension utilizing the Math Coach system's chat logs. Four coaches, teacher students at a technical Swedish university and active in the Math Coach program, were invited to interact with the AI bot and participate in a group discussion. The coaches interacted individually with the AI bot while the chat conversation was displayed on a monitor so all participants could discuss the interaction while it took place. A semi-structured interview approach was taken and the participants were also encouraged to 'think aloud' about their experience. In the discussions, the coaches expressed surprise by the AI's social aspect. They perceived the AI bot as friendly with a positive attitude and were especially surprised by its ability to correctly place appropriate emojis. The coaches agreed that the AI was able to ask both appropriate and helpful questions and share some good guidance for how to proceed in the problem-solving process. However, they felt that the AI bot was not able to offer sufficient mathematical guidance, oftentimes the AI bot was confidently wrong. It also wrote too long messages, which humans would typically separate into several chat messages, and did not wait for a response but instead moved too quickly towards the solution. Moving forward we plan to address the effects of improved prompts on the AI bot and continue finetuning the LLM. We will continue to conduct pilot studies and eventually conduct more large-scale empirical studies.

Inside the April 2024 Issue.

Dear Readers, We are thrilled to bring you the first issue of the ninth volume of POCUS Journal. Published since 2016, POCUS Journal is the only multi-disciplinary, peer-reviewed, POCUS-focused journal that is free for authors and readers alike. We are grateful for the vision of our founder Dr. Amer and for the support of CINQUILL Medical Publishers, Inc. as we enter our ninth year of publication. POCUS is an ever-changing field as clinicians seek out better and faster solutions for patient care at the bedside. We at POCUS Journal continue to evolve as well. Our editorial board is growing to meet the demands of our high volume and high-quality manuscript submissions. We welcome Dr. Andre Kumar of Stanford University and Dr. Manpreet Malik of Emory University to the Internal Medicine section. We also welcome Dr. Andrea of the University of Southern California to the Pediatrics section. Natalie joins us from Queen’s University as the Social media Editor. If you haven’t seen her high-yield on social media I highly encourage you to visit POCUS Journal on X and Instagram for summaries of research articles and other content published in our journal. A journal our size also needs help with statistics, and we are excited that Nicholas has joined our team from the University of Toronto as Statistical Editor. We also have a new Editorial Director of Artificial Intelligence, Dr. Bredon Crawford. We are the first journal to feature an AI bot on our website, which is thanks to Dr. Crawford. Its name is “PJ” and you can find it on the bottom right of our website: www.pocusjournal.com. And finally, we are excited to have had Kathryn Matsushita join our team as , helping us preserve our standard of high quality publishing as the volume of submitted articles and size of our issues continues to grow. With the addition of these new members, I also share the bittersweet news that our Managing Editor Braeden Hill will be leaving POCUS Journal to pursue MD/PhD studies at The University of Toronto. started at POCUS Journal in 2020 as Social Media Editor and his contributions these past few years have been remarkable. We welcome Laura Guzman of Queen’s University into the role of Managing Editor. Every issue I oversee at POCUS Journal strikes me as better than the last, with fascinating cases and important research that answers key questions related to point of care ultrasound. This issue is no exception. Please find our author guidelines here: https://pocusjournal.com/author-guidelines/ Sincerely, Benjamin T. Galen, MD Department of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY Editor-In-Chief POCUS Journal

The AI bot will see you now: how technology is changing the doctor-patient relationship

The AI bot will see you now: how technology is changing the doctor-patient relationship

Enhancing Students' Preparation for Math Exams using the Advanced Features of the Halomda Platform

This paper highlights the new enhancements of the Halomda platform designed to improve students' learning and exam preparation. The new features include a Graph Plotter, Algebraic Calculator and Context ChatGPT Recommended Key Guidelines. The use of these features is demonstrated though a working example of solving a double integral, illustrating how students' self-preparation for mathematics exams is supported by interactive context feedback. In addition, the system's ability to check intermediate steps of a solution, complemented by graphing, comparison of algebraic equivalence of algebraic operations, and guided AI bot assistance, contributes to an enriched learning experience and improved self-assessment for students. In case of insufficient understanding of the solution, the AI chat can be consulted for further clarification. Since the solution in the "Help" mode is developed by the teacher who prepared the test paper, in case of misunderstanding, the student can turn to external sources, and the fastest way is the AI chat.

Enhanced AI Bot with Facial Emotion Detection

Enhanced AI Bot with Facial Emotion Detection

A Conversational AI Bot for Efficient Learning: A Prototypical Design

A Conversational AI Bot for Efficient Learning: A Prototypical Design

Public relations' disruption model on chatgpt issue

With the rising of AI technology, a new disruption occurred in society. Disruption can actually be a social phenomenon that exists even in Indonesian society. However, the current state is making the disruption become a commercial interest. A bold example is Tesla's AI Bot named Optimus in the @TeslaAIBot account and a new GPT used for Twitter commentaries called @ReplyGPT. Content Analysis was the methodology for this research, with crawling data related to ChatGPT from Twitter from December 2022-January 2023. Both served as the bridge between humans and AI, and both pushed awareness that there are other creatures than humans. The sequential explanatory design method will discover a new perspective of GPT as advanced technology. People realise that the closest thing that could change human civilisation is language models.

RNMKRS PITCHPERFECTOR: ARTIFICIAL INTELLIGENCE TO BOOSTER STUDENTS’ SELF-EFFICACY IN DELIVERING ELEVATOR PITCH

ABSTRACT This article investigates the usage of an AI bot app called RNMKRS PitchPerfector to teach the sales skills necessary to deliver a successful elevator pitch. In particular, we wanted to know if the use of this app could increase student self-efficacy toward this critical selling skill. To analyze if RNMKRS had a real impact on students’ elevator pitch self-efficacy, surveys were administered before and after the implementation of the RNMKRS assignment. In addition, class modality (online asynchronous and in-person) was tested to identify potential differences in self-efficacy growth. We found that the usage of RNMKRS did increase student self-efficacy in the delivery of an elevator pitch. That impact was not influenced by classroom modality suggesting that it would be equally effective for an in-person class as it would be in an online asynchronous course. Finally, we discuss the challenges that we faced implementing RNMKRS with students.

Early detection of depression using a conversational AI bot: A non-clinical trial.

Artificial intelligence (AI) has gained momentum in behavioural health interventions in recent years. However, a limited number of studies use or apply such methodologies in the early detection of depression. A large population needing psychological-intervention is left unidentified due to barriers such as cost, location, stigma and a global shortage of health workers. Therefore, it is essential to develop a mass screening integrative approach that can identify people with depression at its early stage to avoid a potential crisis. This study aims to understand the feasibility and efficacy of using AI-enabled chatbots in the early detection of depression. We use Dialogflow as a conversation interface to build a Depression Analysisn (DEPRA) chatbot. A structured and authoritative early detection depression interview guide, which contains 27 questions combining the structured interview guide for the Hamilton Depression Scale (SIGH-D) and the inventory of depressive symptomatology (IDS-C), underpins the design of the conversation flow. To attain better accuracy and a wide variety of responses, we train Dialogflow with the utterances collected from a focus group of 10 people. The occupation of the focus group members included academics and HDR candidates who are conscious, vigilant and have a clear understanding of the questions. In addition, DEPRA is integrated with a social media platform to provide practical access to all the participants. For the non-clinical trial, we recruited 50 participants aged between 18 and 80 from across Australia. To evaluate the practicability and performance of DEPRA, we also asked participants to submit a user satisfaction survey at the end of the conversation. A sample of 50 participants, with an average age of 34.7 years, completed this non-clinical trial. More than half of the participants (54%) are male and the major ethnicities are Asian (63%), Middle Eastern (25%), and others 12%. The first group comprises professional academic staff and HDR candidates, the second and third groups comprise relatives, friends, and volunteers who were recruited via social media promotions. DEPRA uses two scientific scoring systems, QIDS-SR and IDS-SR to verify the results of early depression detection. As the results indicate, both scoring systems return a similar outcome with slight variations for different depression levels. According to IDS-SR, 30% of participants were healthy, 14% mild, 22% moderate, 14% severe, and 20% very severe. QIDS-SR suggests 32% were healthy, 18% mild, 10% moderate, 18% severe, and 22% very severe. Furthermore, the overall satisfaction rate of using DEPRA was 79% indicating that the participants had a high rate of user satisfaction and engagement. DEPRA shows promises as a feasible option for developing a mass screening integrated approach for early detection of depression. Although the chatbot is not intended to replace the functionality of mental health professionals, it does show promise as a means of assisting with automation and concealed communication with verified scoring systems.

Reimagining Humanity

‘Faced with inevitable collapse, leading scientists used some of the industrial world’s last remaining technological and energy resources to design and provide an AI bot for selected people on the planet.’’ The following short fiction story explores the next version of human settlement after the collapse of this one, as predicted by Bendell’s research into ‘Deep Adaptation’ (Bendell, 2020). Dr. Bendell warns us that, unless we find ways to radically change our lifestyle, ‘human societies will experience disruptions to their basic functioning within less than ten years due to climate stress. Such disruptions include increased levels of malnutrition, starvation, disease, civil conflict and war – and will not avoid affluent nations.’ Through this story, we illustrate the idea that a societal collapse may actually be what humanity, and most certainly what the earth, needs.

Therapy with a Human Face

Therapy with a Human Face Hannah Zeavin (bio) In the universe of Eliza, a video game and visual novel, the American mental health crisis is total: everyone needs treatment. For a start-up in Seattle, the solution is an AI bot, which inducts its patients into confessional quasi-therapy. Hooked up to a heart rate monitor, the patient is reduced to a data stream. The player controls Evelyn, a human counselor with no mental health training, who reads the computer's script. At the end of every session, she is awarded a star-based rating. Patients can leave a tip. The game takes its name from ELIZA, Joseph Weizenbaum's experiment with Natural Language Processing. In 1966 the computer scientist developed the first proto-therapy chatbot while trying to demonstrate the absurd superficiality of communicating with machines. Weizenbaum named his bot after George Bernard Shaw's Eliza Doolittle, the ultimate mimic. ELIZA, like Evelyn, was purportedly a woman who learned to listen like a therapist without being one. Eliza offers a critique of the state of contemporary mental healthcare. As rates of symptomatic anxiety and depression have soared, face-to-face, non-corporate therapy has become more difficult to access. Even those with insurance often find inadequate coverage, and many areas don't have enough providers. Today, more people might be “in therapy” than ever before, but what that means is vague. A five-minute text exchange with a cognitive-behavioral therapy (CBT) app and multi-session weeks with a psychotherapist are now rendered equivalent under that convening term: therapy. Mental health workers increasingly seek out employment on digital platforms like Talkspace and BetterHelp, which pay their therapists between $20 and $30 an hour. Nearly all digital apps are unregulated and the quality of care has declined alongside the compensation. And millions use an AI-powered chatbot to receive their mental healthcare. Eliza is supposed to depict a dystopia, but it's closer to a realist portrait. The game also includes a second, quieter exploration of the status of therapeutic labor. Who can perform it? For what fee? Is it any good? Is it at least better than what one can do alone? At the center of these questions lies a history of the feminization of therapy—the fight to include women in its practice and its subsequent devaluation. In the United States, the standard account of this story begins in the 1980s, when the requirement that an analyst must hold a medical degree was lifted. But there was already a flourishing psychology community in the early twentieth century, a third of whose members were women. For women in the West, therapy did not go from an impossible profession to a possible one; the feminization of the field happened again and again. To find the origins of our present-day crisis, we have to look back beyond Weizenbaum and his 1966 script to the beginnings of professional psychology in both Europe and the United States. This history is crucial if we want to understand how therapy became, paradoxically, both devalued and out of reach. On June 4, 1938, Sigmund Freud fled Nazi Vienna for the relative safety of London, accompanied by his youngest daughter. Anna Freud was also a famous psychoanalyst, both in her own right and because of her last name. She lacked the medical training that was typical of early psychoanalysts, but it wasn't only nepotism that allowed her to enter the field. In fin-desiècle Vienna, psychoanalysis had slowly opened up to accommodate a number of lay (non-medical) analysts, mostly women, especially if they treated children. While Freud was alive, psychoanalysis was synonymous with his theories, and analysts who departed from his beliefs were marginalized and kicked out of societies. After his death in 1939, the British Psychoanalytical Society was beset by two dilemmas: how to survive during the Second World War and how to practice analysis at all. These challenges generated a schism, with two major camps represented by two prominent women lay analysts, Anna Freud and Melanie Klein. [End Page 11] Their dispute, which became known as the Controversial Discussions, centered on Freud's disagreement with Klein's attribution of capacities like...

Proxy – An Innovative AI Monitored First Aid Bot

Basic first-aid is the need for every person, but not everyone is aware of the mechanisms in this busy world. In this paper the approach we have used is an Artificial Intelligence Bot combined with Natural Language Processing to assist in basic health care. Proxy is an AI bot designed to virtually assist in basic first aid and wound analysis. Proxy is a specialized bot that not only assists in basic first aid, but it also uses advanced machine learning techniques like image classification for wound detection and identification. It provides an easy usage by speech recognition techniques as well. Proxy is well designed to help assist people in areas of major crowd and necessity such as toll gates, public places, etc. Proxy provides a medical service which helps assist people as well as entertain people by its movable property controlled by a human.

Chatting with AI Bot: Vocabulary Learning Assistant for Saudi EFL Learners

In the AI field of language learning, chatterbots are an interesting area for language learning and practice. This research investigates Arabic EFL vocabulary learning using an interactive storytelling chatterbot. A chatterbot was created and equipped with four vocabulary tools: a dictionary, images, an L1 translation tool, and a concordancer. The target words were enhanced by these tools to provide the learners with interactive comprehensible input. This project seeks to identify which tools are mostly used when EFL learners are practicing English with a chatterbot. It also seeks to determine which tool could help most in vocabulary learning as well as retention. The results of the study indicate that the dictionary is the most favoured and effective tool for vocabulary learning. For retention, the findings uncover that L1 translation is slightly (but insignificantly) higher than the dictionary.
  

Token Authentication based Election System with AI BOT

Token Authentication based Election System with AI BOT

Automatic intersection extraction and building arrangement with StarCraft II maps

In StarCraft, buildings arrangement near the intersections is one of most the critical strategic decisions in the early stage. The high time complexity of the buildings arrangement makes it difficult for AI bot to make the real-time decision. This paper presents an approach to analyze the intersection in StarCraft II maps. We propose a radar-like algorithm to automatically detect the intersection and use a designed heuristic search algorithm to arrange the building for building the wall. Our method can obtain the optimal solution while meeting the real-time requirement.

Building Task-Oriented Dialogue Systems for Online Shopping

We present a general solution towards building task-oriented dialogue systems for online shopping, aiming to assist online customers in completing various purchase-related tasks, such as searching products and answering questions, in a natural language conversation manner. As a pioneering work, we show what & how existing NLP techniques, data resources, and crowdsourcing can be leveraged to build such task-oriented dialogue systems for E-commerce usage. To demonstrate its effectiveness, we integrate our system into a mobile online shopping app. To the best of our knowledge, this is the first time that an AI bot in Chinese is practically used in online shopping scenario with millions of real consumers. Interesting and insightful observations are shown in the experimental part, based on the analysis of human-bot conversation log. Several current challenges are also pointed out as our future directions.

Google reveals secret test of AI bot to beat top Go players

Updated version of DeepMind's AlphaGo program behind mystery online competitor.