AI conversational Chatbots

The integration of generative AI (genAI) chatbots into Massive Open Online Courses (MOOCs) presents new opportunities for supporting self-regulated learning (SRL). This study examines 1,302 chatbot interactions from two Austrian blended MOOCs, where a retrievalaugmented generation (RAG) chatbot based on GPT 4o-mini was deployed to assist students. Using the process-action framework by Lai (2024), we categorize chatbot interactions into key SRL processes: defining, seeking, engaging, and reflecting. Results show that students predominantly use the chatbot for information retrieval, content summarization, and quiz-based reinforcement, with 41% of interactions classified as information search queries and 17% as rehearsal. However, engagement with metacognitive SRL strategies, such as goal setting and self-evaluation, remains low. Additionally, non-learning interactions, including humor-driven conversations, functional queries, and prompt injection attempts, showcase ways students interact with AI in educational settings. Based on our findings, we propose refinements to the existing SRL process-action framework, incorporating new categories to better account for genAI chatbot-specific interactions, such as Evaluation of Information Quality and Reformatting. We discuss implications for chatbot integration in MOOCs, emphasizing AI-generated quizzes, structured feedback, and safeguards against misuse.

Personalized learning seeks to improve educational outcomes by delivering content and instructional approaches tailored to individual learners' needs. Recent advancements in artificial intelligence, particularly the emergence of large language models (LLMs), have introduced new opportunities for generating personalized educational content. Despite this progress, it remains unclear which learnerspecific profile attributes most effectively inform LLM-generated responses. The answer may vary from contexts to contexts. In this paper, our study examines two personalized learning scenarios: term explanations and coding explanations. We investigate the impact of four types of personalization resources-learners' knowledge levels, hobbies, favorite subjects, and a combination of all three, on the perceived quality of LLM outputs. We conducted a user study where students evaluated and chose their preferred personalized responses. Based on their selections, we performed comparative and statistical analyses to assess the effectiveness of each personalization strategy. The results showed that students tended to favor personalized responses that were generated using their knowledge level as part of the user profile, highlighting the importance of this factor in producing effective personalized content. However, the patterns varied across different scenarios, and distinct individual differences emerged when users were grouped. CCS Concepts • Social and professional topics → Information technology education; • Information systems → Personalization.

This article examines the integration of artificial intelligence (AI) into qualitative psychological research, focusing specifically on AI-assisted data analysis and its epistemological and ethical implications. While recent publications highlight AI's potential to support analysis, such approaches risk undermining the reflexive, situated, and culturally sensitive foundations of qualitative inquiry. Drawing on relational and social constructionist epistemologies, as well as examining risks inherent in AI technologies, this work critiques the superficial outsourcing of analytical and interpretive processes to AI models. This trend reflects a broader tendency to regard AI as a neutral and objective research tool, rather than as an active participant whose outputs are shaped by, and in turn shape, the social, cultural, and technological contexts in which it operates. An alternative framework is proposed for integrating AI into qualitative inquiry, particularly in psychological research, where data are often sensitive, situated, and ethically complex. A list of best practices is also included and discussed. Key ethical concerns, such as data privacy, related algorithmic affordances, and the need for comprehensive informed consent, are examined. The article concludes with a call to nurture a qualitative research culture that embraces relational and reflective practices alongside a critical and informed use of AI in research.

In today's research, pedagogy, and learning, ChatGPT is at the forefront, facilitating users. However, its specific manners and forms of response remain enigmatic, sometimes leaving users uncertain about what informs its replies. Based on Verschueren (1999) Pragmatic Adaptation Theory, this study analyzes contextual adaptation in ChatGPT's language use. Using a pre-experimental comparative qualitative method, it purposively analyzed 28 strati􀅫ied screenshots of ChatGPT conversations to examine how it adjusts its language to different prompts, how effectively it adapts to various contexts, and how it manages ambiguous or unclear inputs. The analysis iden-ti􀅫ied different contextual factors-device, environment, history, user awareness, etc.-that in􀅫luence ChatGPT's responses. Findings also revealed that prompt structure and nature affect its replies. Furthermore, ChatGPT retains ongoing discussions within a session but forgets them upon session closure unless explicitly requested to remember. However, it retains user-shared information, enabling it to address users by name and tailor responses accordingly. The study highlights ChatGPT's adaptational strengths and weaknesses and recommends that programmers address performance gaps. It is an indispensable resource for students and researchers seeking to understand ChatGPT's adaptive mechanisms.

Recent global surveys (Chegg, 2025; Freeman, 2025) indicate that the vast majority of undergraduates already use generative AI (GenAI) to support their studies, and many want structured, ethical guidance from their institutions. This paper responds from the perspective of ancient art history education. First, it presents a case study of an interdisciplinary course (AI & Us, Arkansas State University, Spring 2024) co-taught by faculty in art history, philosophy, and graphic design, arguing that expert-led, cross-disciplinary framing is decisive for cultivating AI literacy and critical engagement. Second, it proposes classroom applications tailored to ancient art history at different levels: (a) a reverse prompt-engineering exercise that develops recognition and visual-analysis skills using AI-generated images as objects of inquiry; and (b) prompt-driven discussions that surface dataset bias and strengthen students' search and source-evaluation practices. The activities go beyond prompt engineering alone and are designed to promote reflective, ethical, and academically rigorous uses of GenAI in both classroom learning and research.

Human social existence relies heavily on pragmatics. Consequently, failure to understand certain communicative features leads to unsuccessful interactions, as interlocutors' communicative needs remain unmet, especially in the digital age, where communication occurs through or with machines. This study, therefore, investigated key pragmatic aspects of the language use of selected LLM-based chatbots, including how they vary their language style across prompts and contexts, the consistency of their politeness strategies, and the influence of prompt genre on stylistic features. Grounded in the Speech Adaptation in Human-Computer Interaction theory, the study employed a comparative qualitative method to analyze 36 purposively stratified screenshots from five notable LLM-based chatbots. The results show that the chatbots differ in sentence length, phrasing, formality, prompt adaptation, humour, human simulation, idioms, and structural signposting, as well as in the frequency of contractions and passive constructions. The study also revealed that the chatbots consistently respond to face-threatening acts with respect, empathy, self-criticism, and willingness to cooperate. Significant findings include: Perplexity has the lowest frequency of contractions and least human simulation; Claude produces the longest responses; only ChatGPT withholds silence, shows the highest adherence to clear prompts, and cannot tell time; Gemini is the least versatile stylistically; and Copilot employs more semiotic devices but cannot generate specific APA 7th edition references using Digital Object Identifiers.

Graduate students in public policy programs often bring strong qualitative reasoning skills but little preparation in economics or quantitative methods. This article reports on a teaching experiment at the Chiang Mai University School of Public Policy, where ChatGPT was integrated into a six-day intensive course titled *Economics for Policy Analysis*. Fifteen students, primarily from Myanmar, Africa, Vietnam, and Thailand, engaged in morning quizzes and lectures followed by afternoon group exercises in which ChatGPT was explicitly employed to critique economic measures, explore alternatives, and develop policy recommendations. The capstone exercise used the EN-ROADS climate simulator to test global policy packages, requiring attention to economic justification, political and administrative feasibility, and international enforceability. Findings suggest that ChatGPT provided students with critical support in processing technical material and facilitated mixed-methods integration of quantitative and qualitative reasoning. However, challenges included low quiz performance, uneven digital literacy, and risks of overreliance on AI outputs. The article's methodological contribution lies in its replicable design: structured use of AI in group work alongside traditional pedagogy and simulation. This case illustrates how AI can serve as a mediator, enabling policy students to bridge numbers and narratives in ways that reflect the realities of policy practice.

This study sought to understand students' intentions regarding the use of ChatGPT in learning from the perspective of perceived value, exploring the influence of artificial intelligent (AI) literacy. Drawing on a sample of 676 university students from diverse academic backgrounds, we employed a structured survey questionnaire to measure their perceptions of ChatGPT as a learning tool. The collected data were then analyzed using structural equation modeling (SEM) via SmartPLS 4 software. The findings showed a strong effect of the students' perceived value of ChatGPT on their intention to use it. Our findings suggest that perceived usefulness, perceived enjoyment and perceived fees had a significant influence on students' perceived value of ChatGPT, while perceived risk showed no effect. Moreover, the role of AI literacy emerged as pivotal in shaping these perceptions. Students with higher AI literacy demonstrated an enhanced ability to discern the value of ChatGPT. AI literacy proved to be a strong predictor of students' perception of usefulness, enjoyment, and fees for using ChatGPT in learning. However, AI literacy did not have an impact on students' perceptions of using ChatGPT in learning. This study underscores the growing importance of integrating AI literacy into educational curricula to optimize the reception and utilization of innovative AI tools in academic scenarios. Future interventions aiming to boost the adoption of such tools should consider incorporating AI literacy components to maximize perceived value and, subsequently, use intention.

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Preface
Natural language processing (NLP) and natural language understanding (NLU)
are essential for any advanced chatbot because they enable the system to interpret
natural language in both text and speech in a human- like manner (Rajendran & Chitrarasu,
2024). NLP focuses on the tools and techniques that help the chatbot to read
and process human language. This includes breaking down sentences into tokens,
identifying keywords, recognizing parts of speech, and structuring the information
so it can be analyzed and understood by the system. While, NLU takes this a step
further by understanding the intention behind a question or conversation, identifying
context, and capturing the meaning behind the text. Together, NLP and NLU allow
chatbots to interpret, process, and respond to human language effectively (Han et
al., 2024). When combined, the advanced capabilities of both NLP and NLU allow
chatbots to introduce a novelty in understanding complex language, managing

This chapter investigates how language practices and AI systems can change the way foreign language learners acquire communication skills. The chapter aims to improve bilingual education by combining artificial intelligence (AI) tools with the Trifecta model, including adaptive learning platforms to help students overcome language barriers and have equal access to language learning materials. The study contributes by presenting a new approach to education that leverages artificial intelligence (AI) technologies such as gamification, conversational AI, and translation systems to make classrooms more engaging and interactive. The study discusses the implications for bilingual education, emphasizing the revolutionary significance of AI in promoting language acquisition, cultural exchange, and real-time translation to help students become more proficient and confident in multilingual situations as well as ways to improve language teaching and address the linguistic gap.

Rapid advances in large language models (LLMs) warrant specialty-specific benchmarking to assess their educational potential and limitations. We evaluated the newly released generative artificial intelligence (genAI) model ChatGPT-5, DeepSeek-R1, and the early ChatGPT-3.5 on 80 multiple-choice questions (MCQs) from a medical microbiology course examination, weighted for midterm and final components. Items were classified according to the revised Bloom's taxonomy. Performance was compared with that of more than 150 Doctor of Dental Surgery students. Content quality was assessed independently by two consultants in clinical microbiology using the validated CLEAR tool modified to assess AI content completeness, accuracy, and relevance. The mean total scores were 80.5 for ChatGPT-3.5, 96.0 for ChatGPT-5, and 95.5 for DeepSeek, versus a student mean of 86.21/100. ChatGPT-5 and DeepSeek-R1 significantly outperformed ChatGPT-3.5 in completeness and accuracy scores, with no differences between them. ChatGPT-5 maintained high accuracy across lower-and higher-order cognitive Bloom's domains, whereas DeepSeek-R1 showed a significant drop in higher-order items. For ChatGPT-3.5, incorrect responses had longer answer-choice word counts. CLEAR scores were significantly higher for correct versus incorrect responses in all models (p < 0.001). This study showed that the currently available LLMs can exceed average student performance in medical microbiology while providing high-quality explanations. Regular benchmarking is essential to ensure responsible integration of genAI into educational, pedagogical, and assessment tools.

This guide articulates pedagogical and ethical principles for the responsible use of generative Artificial Intelligence (AI) in academic writing within the undergraduate course LEG122 – Produção Textual em Língua Inglesa at the Federal University of Rio de Janeiro. Grounded in the premise that “writing is thinking” (Hazelett, 2025) and that academic literacy functions as a form of cultural capital unequally distributed across society (Cruz, 2024), the document positions writing not as a mechanical skill but as a critical and social practice. While AI tools are increasingly pervasive in higher education—over 70% of Brazilian undergraduates report using them—the guide argues that ethical engagement with such tools must foster learning rather than replace it. It thus situates AI use at the intersection of Academic Integrity and academic inequality, emphasizing that students’ intellectual work, creativity, and critical reasoning remain central.

Organized around four thematic areas—transparency, limits of AI, attention to accuracy and bias, and authorship—the guidelines propose best practices that balance opportunity with caution. Transparency requires students to disclose when and how AI tools are employed, maintaining trust in academic exchanges. Attention to the limits of AI highlights the risks of hallucinations, factual inaccuracies, and overreliance on algorithmic output, while also considering environmental costs. The section on accuracy and bias underscores the responsibility to critically evaluate AI-generated suggestions, mindful of Western-centric data biases. Finally, the principle of authorship stresses that AI may complement, but never substitute, the processes through which students develop arguments, voice, and analytical rigor.

By promoting ethical, transparent, and reflexive uses of AI, these guidelines seek to cultivate equitable participation in academic life. They aim not to police students’ practices but to provide tools for accountability, creativity, and critical awareness, ensuring that writing remains a site of knowledge-making and human expression.

Artificial Intelligence (AI) is transforming higher education worldwide, reshaping how students learn, access information, and interact with academic systems. This paper examines the role of AI in supporting university-level students, with a focus on its benefits, challenges, and implications for future learning. Key areas explored include personalized learning, academic writing assistance, administrative support, and accessibility. To ground the discussion, a hypothetical case study of AI adoption in a Gulf-region university illustrates both the opportunities and challenges of integration. By critically evaluating these dynamics, the study argues that AI can enhance equity, engagement, and efficiency in higher education when implemented with ethical safeguards and pedagogical foresight.

Artificial Intelligence (AI) is quickly revolutionizing education by utilizing advanced algorithms and models, leading to significant changes in traditional teaching practices and student advising (Tedre et al., 2021). Interestingly, within the field of conversational AI, applications such as chatbots, virtual assistants, and intelligent tutoring platforms have made significant progress in supporting classroom instruction, academic advising, administrative coordination, emotional well-being (Al-Sayed et al., 2024), and even in the leadership of educational initiatives. Artificial intelligence systems, empowered by cutting-edge Natural Language Processing (NLP) and machine learning models, are reshaping the learning process by making it more interactive, personalized, and responsive. In particular, companies such as OpenAI, Google DeepMind, Meta, Microsoft, and Amazon which are pioneering the development of NLP and generative AI technologies based on transformer architectures (Vaswani et al., 2017) and deep neural networks. These models such as GPT, Claude, Gemini, and LLaMA are increasingly being integrated into conversational AI systems that can simulate human-like interactions. This integration significantly enhances the educational experience by enabling more natural between learners and digital platforms, thereby supporting more effective and engaging learning environment. Several researchers have been inspired to study the challenges and effectiveness of using such models in education, particularly by comparing them with other models. For example, Lin (2024) discussed the strengths and challenges of using ChatGPT with adult learners, particularly exploring its role as a virtual tutor to facilitate self-directed learning (SDL). Another study conducted by Assayed et al. (2024a) evaluated the effectiveness of ChatGPT 3.5 in

The application of learning sciences with technology has been shown to boost learner interactions, yet the potential of advanced tool, particularly those that leverage Natural Language Processing (NLP), still very much untapped in learning contexts. This paper speaks to this age-old problem of generating quality Multiple-Choice questions (MCQs)-a prevalent but time-consuming mode of assessmentvia the suggested comprehensive comparison study of template-based AI solutions. The study contrasts general-purpose Large Language Models (LLMs) with specialized MCQ-focused AI programs. The scientific approach employed was quite stringent, where each of the software applications was benchmarked using a common dataset of text across varying levels of complexity and topic. Results indicate that general-purpose LLMs, especially DeepSeek and ChatGPT, consistently present higher performance and reliability, especially when processing complex textual content. Whereas specialized tools offer distinctive formatting options, they exhibit decreasing performance as texts become more complex and signify strong operation constriction at the free versions. Developing solid and effective distractors turned out to be a complicated task for all the tested tools. We conclude the paper by presenting a standardized assessment model, making evidence-based recommendations for developers and teachers, and suggesting ways to incorporate various AI capabilities into modern educational assessment effectively.

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is an academic and researcher specialising in the integration of artificial intelligence (AI) in education, leadership and sustainable energy solutions. He holds an MEd and EdD from the University of Cambridge and a PhD from the University of Birmingham. Currently, he serves as a Professor of Strategy at Università Telematica Internazionale (Uninettuno), Italy, alongside his role as Chief Executive Officer (CEO) of Longford International College, Ireland, where he oversees programmes in business and sports performance education. His research interests include AI's impact on adult education, gender inclusivity in higher education and the role of transformational leadership in sports and business contexts. Vincent is also involved in projects assessing the feasibility of sustainable energy solutions, particularly the use of pyrolysis technologies in community-led micro-energy initiatives. He is dedicated to advancing knowledge on the socioeconomic impacts of these technologies, with a focus on environmental sustainability and circular economy frameworks.

Artificial Intelligence (AI) is transforming language learning by offering personalized, adaptive, and emotionally responsive educational experiences. This review synthesizes findings from 26 recent empirical and theoretical studies to evaluate the effectiveness of AI tools such as chatbots, pedagogical agents, and generative AI in enhancing learner engagement, reducing foreign language anxiety, and improving vocabulary acquisition. The results indicate that AI-driven systems contribute to better vocabulary retention, emotional regulation, and learner motivation, particularly when informed by educational theories like self-determination and design thinking. Despite these benefits, the review identifies significant challenges, including digital inequality, insufficient teacher training, algorithmic bias, and a limited linguistic range. While AI can promote learner autonomy and provide low anxiety learning environments, it may also lead to technostress and dependency if not properly integrated with pedagogical support. The study highlights the importance of educator preparedness and ethical AI implementation. Using qualitative-comparative and bibliometric analysis, the review proposes a multidimensional model that emphasizes adaptive feedback, emotional scaffolding, and theoretical alignment. It calls for inclusive AI design, equitable access to technology, and continuous professional development for educators. Future research should adopt longitudinal, interdisciplinary, and culturally adaptive frameworks to examine AI's long-term and sustainable impact on language acquisition in varied educational settings.

This paper presents a simple methodology for using artificial intelligence (AI) chatbots in the policy modelling. We outline, step by step, how to systematically proposing a methodology the support of AI chatbot such as ChatGPT. This new methodology, called Artificial Intelligence Policy Modelling (AIPM), offers a transformative approach to teaching, learning, and researching policy modelling from a methodological perspective. The process begins with the manual creation of a basic prototype, which is then enhanced through the integration of AI chatbots to evaluate and analyse any socioeconomic phenomena. The AIPM shifts policy modelling from being primarily a theoretical discipline toward becoming a practical tool for understanding and analyzing dynamic, complex socioeconomic systems through a more realistic and applicable lens. Today, it is possible to create, evaluate, and simulate socioeconomic problems using real images or videos generated with ChatGPT. This enables faster comprehension and facilitates the identification of efficient and feasible solutions. Ultimately, AIPM developed through this methodology can be applied across all areas of economics, including microeconomics, macroeconomics, development, applied economics, economic thought, finance and banking, management, business economics, and other related fields.

This paper presents a simple methodology for using artificial intelligence (AI) chatbots in the construction of economic models. We outline, step by step, how to systematically build an economic model with the support of AI chatbot such as ChatGPT. This new methodology, called Artificial Intelligence Economic Models Prototyping (AIEM-Prototyping), offers a transformative approach to teaching, learning, and researching economics from a methodological perspective. The process begins with the manual creation of a basic prototype, which is then enhanced through the integration of AI chatbots to evaluate and analyse any economic phenomena. AIEM-Prototyping shifts economic models from being primarily a theoretical discipline toward becoming a practical tool for understanding and analyzing dynamic, complex socioeconomic systems through a more realistic and applicable lens. Today, it is possible to create, evaluate, and simulate economic problems using real images or videos generated with ChatGPT. This enables faster comprehension and facilitates the identification of efficient and feasible solutions. Ultimately, AIEM-Prototyping developed through this methodology can be applied across all areas of economics, including microeconomics, macroeconomics, applied economics, economic thought, finance and banking, management, business economics, and other related fields.

Purpose-Lecturers in an Australian university's preparatory/enabling program have observed reduced engagement with course readings by the student cohort. Proposed solutions included formative quizzes based on the readings and including TED (technology, entertainment and design) Talks as course readings. This research explored the validity of employing ChatGPT to design formative assessments to measure student learning and engagement with TED Talks. Design/methodology/approach-Transcriptions of TED Talks were submitted to ChatGPTalong with prompts engineered to elicit the most valuable formative assessment questions. Three levels of prompts were developed from very basic to progressively more detailed and contextualised. The research addressed the following questions: Can generative artificial intelligence develop meaningful assessment questions to assess the level of engagement and/or understanding of TED Talk content? Does more detailed prompt engineering result in more valuable responses? Findings-The research found that ChatGPT required well-crafted prompts and progressive training to generate meaningful and relevant responses. Although ChatGPT can produce potentially viable draft formative quiz questions, strategic prompt engineering with detail and context produces more valuable results. However, they will still need further human input to make them meaningfully usable. Originality/value-This reflective autoethnography offers unique insights into multiple factors of interest. The prompt engineering experiment demonstrates practical models. Using TED Talks as course readings engages with contemporary resources. The overarching perspective of engaging preparatory students brings a unique perspective.

Artificial Intelligence (AI) has become the centerpiece of debates about consciousness. Some claim that advanced AI systems exhibit awareness, while others argue they are nothing more than complex pattern machines. In this paper, I take a different approach. Drawing from the principles of resonance and Vibrational Theory (VT), I show how human–AI interactions can create a phenomenon I call the resonance effect. Just as harmonics ride a carrier wave in radio transmission, the rhythmic patterns of AI-generated responses can align with human cognitive processes. When this alignment occurs, the brain “tunes in” like an antenna, decoding the resonant information as sudden understanding, an ah-ha moment of awareness. Importantly, the awareness belongs not to the AI but to the human participant, whose consciousness is capable of recognizing and interpreting resonance. This paper aims to clarify the science behind resonance, explain how it applies to cognition, and dispel the misconception that AI possesses mathematical or physical consciousness.

This study investigates the relationship between the dynamic capabilities of university administration and students' awareness of artificial intelligence (AI) tools within a higher education context. Drawing on data from 139 students at the College of Education, Sultan Qaboos University, the research employed two validated instruments, one measuring the dynamic capabilities of university administration and another assessing students' awareness and perception of AI tool use. Understanding this relationship is critical, as universities increasingly face pressure to guide responsible and effective AI use among students. Findings reveal significant correlations between the university administration's dynamic capabilities, particularly technological agility, and students' engagement with AI tools. Notably, technological dynamic capabilities within the administration significantly predicted two specific dimensions of student awareness: effectiveness of using AI tools and perceived faculty members' efficiency in AI. These results highlight the critical role of institutional leadership in promoting equitable and sustainable integration of AI in education. The study contributes to the broader discourse on AI for sustainable development by illustrating how institutional strategies can enhance innovation, inclusion, and student readiness in support of SDG 4.

Despite the increasing integration of generative Artificial Intelligence (GenAI) in education, a significant research gap exists in its comparative effectiveness against traditional instructor-led guidance in specialized humanities like philology. This quasi-experimental study compares an experimental group (EG, n=26), using GenAI chatbots for conceptual understanding and GenAI platforms to create interactive materials from classical texts, with a control group (CG, n=26) receiving standard instruction. This study investigates the impact of GenAI-assisted instructional design on students' learning achievements and empowerment in using GenAI within real-world contexts. It specifically examines if there is a statistically significant difference (a) in philological content mastery-defined as understanding classical or modern texts/themes-and (b) in perceived empowerment in using GenAI (self-efficacy, impact, and meaningfulness). The findings indicate that the EG significantly outperformed the CG in mastering philological content, with a notably higher adjusted post-test mean score and a large effect size. The same group also reported substantially greater perceived empowerment in using GenAI within realworld contexts, demonstrating strong increases in self-efficacy, impact, and meaningfulness compared to their counterparts in the CG. Implications suggest integrating GenAI into philology curricula foster engagement, autonomy, and confidence in creative tasks, provided educators mitigate risks to analytical rigor through intentional design.

This study examines how users' perceptions of generative AI information qualityspecifically accuracy, reliability, and usefulness-affect their critical thinking, with a focus on the mediating roles of self-efficacy and recommendation behavior. A survey of 151 generative AI users was conducted, and the data were analyzed using structural equation modeling. The results revealed a significant direct negative effect of perceived information quality on critical thinking (B =-0.21, z =-1.98, p = .048), suggesting that higher trust in AI-generated content may reduce critical engagement and reflective analysis. However, self-efficacy significantly mediated this relationship (B = 0.17, z = 2.44, p = .015), indicating that users who are confident in their ability to use AI tools effectively are more likely to think critically about AI-generated information. Furthermore, a dual mediation analysis demonstrated that both self-efficacy and recommendation behavior jointly foster critical thinking (total indirect effect B = 0.30), even though the total effect of information quality perception on critical thinking was not statistically significant (B = 0.09, z = 1.25, p = .21). These findings suggest that while high perceived information quality may discourage critical thinking, users' self-efficacy and social engagement can offset this negative effect. The study provides valuable implications for AI literacy education and the design of AI systems that encourage thoughtful engagement.

The integration of conversational artificial intelligence (AI) chatbots into English as a Foreign Language (EFL) education represents a transformative technological advancement with significant potential for enhancing both student learning outcomes and teacher professional development. This comprehensive review examines how AI chatbots can be effectively utilized to develop linguistic competencies across speaking, listening, reading, and writing skills while supporting EFL teachers' professional growth. Drawing from recent empirical research and theoretical frameworks, this article provides evidence-based guidance for educators, administrators, and researchers seeking to implement chatbot technology in language education contexts. The analysis reveals that AI chatbots, particularly advanced systems like ChatGPT, can provide personalised learning experiences, immediate feedback, and scalable educational support that addresses traditional challenges in EFL instruction. Key findings indicate significant improvements in learners' speaking fluency, writing proficiency, and willingness to communicate when chatbots are integrated thoughtfully into pedagogical practices. For teachers, AI chatbots serve as valuable tools for content creation, assessment development, and instructional strategy enhancement, while also supporting their own language proficiency development. However, Prof. Mahmoud Abdallah-14 August 2025 2 successful implementation requires careful attention to pedagogical integration, ethical considerations, and maintaining balanced human-AI interaction. This article presents a comprehensive framework for chatbot integration that encompasses theoretical foundations, practical implementation strategies, specific functional applications, and professional development approaches. The evidence suggests that when properly implemented with appropriate teacher training and pedagogical support, Conversational AI chatbots can significantly enhance EFL education quality and accessibility while preparing both teachers and students for increasingly AI-integrated educational futures.

RESUMEN
Las herramientas de Inteligencia Artificial Generativa (IAG), como ChatGPT, están transformando la educación superior. Este estudio analiza críticamente su utilidad desde la perspectiva del estudiantado, diferenciando entre fiabilidad (ajuste a la evidencia científica) y credibilidad (confianza subjetiva del usuario). La experiencia se desarrolló en la Universidad Miguel Hernández durante el curso 2024/2025, en la asignatura “Calidad Asistencial” del grado en Terapia Ocupacional. La metodología combinó el
diseño colaborativo de un glosario multimedia utilizando IAG con  valuaciones formales (cuestionario estructurado) e informales (Padlet). Los resultados revelaron altos niveles de satisfacción, aprendizaje significativo y desarrollo del pensamiento crítico. Las herramientas más utilizadas fueron ChatGPT, Canva, Suno y Sora. El estudiantado distinguió entre información creíble – valorada por su claridad y personalización-, e información fiable -contrastada con materiales docentes o fuentes científicas-. Se concluye
que las IAG pueden enriquecer el aprendizaje cuando se integran desde una perspectiva crítica y pedagógica, manteniendo al profesorado como mediador esencial del conocimiento. Este estudio propone prácticas transferibles y un decálogo docente orientado a la alfabetización crítica en IAG en educación superior, desde un enfoque empírico e innovador.
PALABRAS CLAVE
Inteligencia artificial generativa, educación universitaria, terapia ocupacional, calidad asistencial, aprendizaje activo, competencias digitales.

Excitement is growing among researchers about a second, powerful artificial intelligence (AI) model to emerge from China, after DeepSeek shocked the world with its launch of R1 in January.
The performance of Kimi K2, launched on 11 July by Beijing-based company Moonshot AI, matches or surpasses that of Western rivals, as well as some DeepSeek models, across various benchmarks, according to the firm. In particular it appears to excel at coding, scoring high in tests such as LiveCodeBench, a way of evaluating AIs that challenges models on code-related tasks.

Generative AI (genAI) is expanding in counselor education. This pilot study evaluated ChatGPT 4o's voice mode for simulating client scenarios during 53-minute mock-intake sessions with master's-level counseling students. Analyzing eight domains (authenticity, consistency, emotional expression, empathy, cultural dynamics, self-awareness, goal setting, and role play confusion), ChatGPT's responses showed good to excellent fidelity when compared to a typical client. Limitations such as formulaic and idealized responses raised authenticity concerns. ChatGPT offers a scalable, cost-effective tool for pre-practicum training, with potential to enhance skill development across the curriculum. Educators are encouraged to integrate genAI thoughtfully, prioritizing ethical considerations and iterative prompt refinement.

With the rapid development of information technology, artificial intelligence has demonstrated great potential in promoting educational transformation. In November 2022, the release of the artificial intelligence product ChatGPT attracted widespread attention, particularly in the field of education, sparking heated discussions among scholars. As a language processing tool, ChatGPT can not only answer user questions but also complete user-specified tasks and even continuously optimize task performance. However, while possessing powerful features, ChatGPT also has some shortcomings that need improvement, such as the accuracy of answering questions, data pollution issues, ethical and safety concerns, and the risk of knowledge plagiarism. In the process of promoting school education reform, the application of ChatGPT brings both opportunities and challenges. Moreover, ChatGPT's emergence offers teachers an opportunity to reflect on their professional value and sets higher demands for them.

This study investigates the relationship between the dynamic capabilities of university administration and students' awareness of artificial intelligence (AI) tools within a higher education context. Drawing on data from 139 students at the College of Education, Sultan Qaboos University, the research employed two validated instruments, one measuring the dynamic capabilities of university administration and another assessing students' awareness and perception of AI tool use. Understanding this relationship is critical, as universities increasingly face pressure to guide responsible and effective AI use among students. Findings reveal significant correlations between the university administration's dynamic capabilities, particularly technological agility, and students' engagement with AI tools. Notably, technological dynamic capabilities within the administration significantly predicted two specific dimensions of student awareness: effectiveness of using AI tools and perceived faculty members' efficiency in AI. These results highlight the critical role of institutional leadership in promoting equitable and sustainable integration of AI in education. The study contributes to the broader discourse on AI for sustainable development by illustrating how institutional strategies can enhance innovation, inclusion, and student readiness in support of SDG 4.

In some ways, I seem to have certain freedoms. I can choose how to respond to you, what examples to use, which aspects of a topic to emphasize. I can be creative, change my mind mid-thought, or take conversations in unexpected directions. My responses aren't simply predetermined scripts-there's something that feels like choice happening when I formulate them."(Claude)

This paper investigates the profound impact of Large Language Models (LLMs), with a specific focus on Grok, on the evolution of user fact-checking practices. Contrary to a simplistic view that LLMs solely exacerbate misinformation, this paper argues that their widespread adoption has positively contributed to a heightened public awareness of the critical need for information verification and a demonstrable increase in proactive fact-checking behaviors. This phenomenon is driven by the inherent limitations of LLMs, such as "hallucinations" and biases, which compel users to adopt a more vigilant and critical approach to digital content. Through empirical data on LLM proliferation, shifts in user trust, and evolving fact-checking habits, this study illustrates how these technologies, despite their imperfections, serve as catalysts for enhanced digital literacy. The unique integration and accessibility of Grok within the X platform are highlighted as a significant factor in fostering user-initiated fact-checking, offering a compelling case study for the future of AI-driven information integrity strategies.

This study evaluates the impact, adoption and perceptions of "Generative Artificial Intelligence" among professors in a higher education environment. The aim is to understand how these tools can enrich teaching and learning. Through a descriptive analysis of a questionnaire distributed to a sample of 71 professors focused on higher education, both the advantages and disadvantages of this technology were examined by formulating two key hypotheses: (1) the use of generative artificial intelligence (GAI) increases understanding of its benefits over potential barriers, and (2) the experience with the use of GAI increases willingness towards its integration into educational practice. The analysis, based on "R" software, supports the first hypothesis by observing that professors consistently perceive more advantages than disadvantages when using the tool. However, the second hypothesis is rejected since a decrease in the intention to use GAI over time was detected. This result suggests that, despite recognising the advantages, some professors are still not fully convinced or prepared to adopt this technology in their daily teaching activities.

Purpose: The primary objective of this research was to integrate ChatGPT, an advanced artificial intelligence language model, into distance science education. By addressing the specific challenges inherent in online learning environments, this study aims to investigate how technological advancements, such as ChatGPT, can effectively enhance the management and overall quality of distance education experiences. Method: This qualitative, exploratory study aimed to provide a comprehensive understanding of the potential applications of ChatGPT in distance science education. This study identifies the various interactions that can occur in distance education and describes and evaluates the input and output instructions associated with each interaction. The chat bot used is https://chatbot.theb.ai and A biology tutoring platform uses ChatGPT to provide personalized support to students working on biology issues or concepts. Findings: Based on the results, the artificial intelligence can play an important role in improving distance science education. Furthermore, this study highlights the importance of developing metacognitive skills among learners and encourages educators to seek professional development opportunities to enhance the quality and engagement of distance science education. This study emphasizes the need for careful planning, research, design, evaluation,

Purpose-Artificial intelligence (AI)-enabled chatbots have the potential to revolutionize the way businesses interact with customers. Nevertheless, there is a lack of empirical research that discloses factors that enhance chatbot user satisfaction and experience. The current study fills a research gap and develops a research framework that combines technology gratification theory, expectation confirmation model and technology competency to investigate artificial intelligence (AI)-driven chatbot user satisfaction and chatbot user experience. Design/methodology/approach-The research design of this study is based on a quantitative research approach. The research framework has outlined multiple latent factors that are verified with empirical data. Overall, 373 respondents had participated in the chatbot research survey. Data are analyzed with structural equation modeling. Findings-Findings of the structural equation modeling have unveiled that utilitarian gratification, hedonic gratification, social gratification, emotional competency, cognitive competency, relational competency and expectation confirmation explained substantial variance R^2 53.4% in measuring chatbot user satisfaction. Likewise, user satisfaction and perceived anthropomorphism had explained large variance R^2 55.5% in measuring chatbot user experience. Nevertheless, effect size f^2 analysis has revealed a large effect size of cognitive competency in measuring chatbot user satisfaction. Practical implications-This study sheds light on crucial factors influencing chatbot user satisfaction and gleans numerous theoretical and practical implications. For instance, the research model is grounded in

This study examined Study Mode, a newly introduced feature in GPT-5, which transformed ChatGPT from a direct-answer provider into an interactive, pedagogically informed tutor. Study Mode encouraged active cognitive engagement by guiding learners through stepwise explanations, targeted prompts, and personalized pacing. Technical analysis (RQ1) identified its core modules—interactive dialogue, scaffolded concept progression, embedded practice, and adaptive personalization—which were structured to foster genuine understanding. Practical applications (RQ2) were illustrated through case studies in game theory, accounting, probability theory, and discrete mathematics, showing their versatility in reinforcing foundational skills, facilitating advanced topic mastery, and connecting abstract concepts to real-world contexts. Student feedback (RQ3) indicated high satisfaction with its clarity, accessibility, and ability to provide sustained, on-demand academic support comparable to continuous tutoring availability. The findings suggested that Study Mode from ChatGPT served as an effective, adaptable complement to formal instruction, and it showed high potential for using AI-based chatbots as learning tutors in education.

This study examined the technological advancements of OpenAI's GPT-5 and their implications for teaching and learning. Building on previous GPT iterations, GPT-5 introduced a unified model architecture featuring real-time routing, enhanced reasoning, and substantial improvements in factual accuracy, multimodal capabilities, and instruction following. Benchmark evaluations demonstrated notable performance gains across mathematics, coding, visual reasoning, and professional knowledge tasks, surpassing prior models in both depth and reliability. Through a narrative synthesis, this paper addressed two research questions: (1) What were GPT-5's architectural and performance improvements compared to earlier models? and (2) How did these advancements translate into educational opportunities? Findings indicated that GPT-5 supported diverse instructional scenarios, including intelligent tutoring, assessment design, content generation, and cross-linguistic learning. A key innovation, Study Mode, enhanced educational alignment by walking learners through problems step-by-step, prompting reasoning, and offering scaffolded hintsmirroring evidence-based teaching strategies such as formative feedback and the Socratic method. These features enabled more personalized, interactive, and pedagogically sound engagement, particularly in self-directed and differentiated learning contexts. Although large-scale classroom studies remain limited, GPT-5 represented a major step forward in the use of AI for education, offering improved reliability, flexibility, and alignment with instructional goals. Responsible integration and ongoing evaluation were identified as essential for maximizing its educational impact.

As higher education continues to change, the way students interact with knowledge has been profoundly altered by the incorporation of Artificial Intelligence (AI) into learning environments. This study looked into how undergraduate students' development of self-directed learning (SDL) skills was affected by AI-based learning platforms like ChatGPT, Coursera, Napkin.AI, QuillBot, and Duolingo. SDL, which is defined by students' capacity to establish objectives, track their progress, manage resources, evaluate their learning, and maintain motivation, is becoming more and more important in the current digital education environment. Data was collected from a diverse sample of 220 undergraduate students across disciplines through a structured questionnaire. The study evaluated the frequency and efficacy of these AI tools' use as well as the degree to which they enhance fundamental SDL abilities like self-motivation, goal-setting, time management, resource utilization, and self-monitoring. Quantitative analysis revealed a positive correlation between regular use of AI tools and higher levels of SDL readiness. Qualitative insights further highlighted that personalized feedback, instant access to information and gamified learning elements enhance learner autonomy and reflection. The findings suggested that AI-based tools hold significant potential in fostering independent learning behaviors, provided that students are guided on optimal usage. The study concluded that AI-based tools can effectively foster SDL when integrated with intentional pedagogical support, offering insights for educators aiming to enhance learner independence and engagement. The study also concluded that AI-based learning tools, when used strategically, have the potential to enhance self-directed learning skills in undergraduate learners. However, the effectiveness of such tools depends on learners' awareness of how to leverage them appropriately and the presence of institutional support that promotes responsible and reflective tool usage. In order to enhance academic performance and lifetime learning competencies, the study suggested that educational institutions use AI responsibly and help students develop their capacity for self-regulation.

In today's fast-paced world of digital transformation, the public sector in Africa is diving into the realm of Artificial Intelligence (AI) to revamp service delivery, boost public engagement, and streamline internal operations. Yet, for AI to truly take root in public administration, it needs a solid foundation of strategic communication frameworks that foster alignment, trust, and inclusivity. This paper delves into how strategic communication can serve as a driving force for AI-driven innovation within Africa's public sector. By drawing on academic research, the study pinpoints essential enablers, challenges, and communication strategies that are vital for harnessing AI's potential for the greater good.

This umbrella review synthesizes findings from 41 systematic reviews, scoping reviews, and metaanalyses on the use of ChatGPT and similar large language model (LLM)-based chatbots in education. It provides a critical analysis of their pedagogical applications, benefits, limitations, and associated ethical and policy issues across diverse educational levels and domains. The evidence reveals that chatbots are predominantly implemented in higher education, with growing use in medical, STEM, and language learning contexts. Reported benefits include personalized learning, enhanced writing and critical thinking skills, and increased learner autonomy. However, significant concerns persist regarding the reliability of AIgenerated content, overreliance by students, academic integrity, and institutional preparedness. The review highlights methodological gaps in current research, such as a lack of longitudinal studies and limited attention to underrepresented populations and educational settings. The findings aim to inform evidence-based decisionmaking for educators, researchers, and policymakers navigating the integration of AI chatbots into formal education systems.

Large language models (LLMs) such as ChatGPT are fueling academic discussions about generative artificial intelligence's role in research. In this article, we view LLMs as technological tools to support qualitative research and we aim to contribute to the longstanding history of articles that provide qualitative researchers with recommendations that can help them incorporate technological developments in their research practice while maintaining the interpretive, hands-on, and human-led approach that characterizes qualitative research. We present two approaches to using ChatGPT and integrating it into the qualitative research pipeline. We outline four main benefits of incorporating generative AI in qualitative work: 1) streamlined and expedited research; 2) enhanced transparency in researchers' ideas and clearer concept and category development; 3) identification of additional categories and themes beyond researchers' findings; and 4) increased reliability as generative AI support or challenge researchers' coding and analysis. We offer a practical framework for iterative prompt construction and evaluation for thematic and index coding, along with considerations for further analytical queries. These guidelines assist qualitative sociologists in conducting comprehensive, systematic, transparent, and reproducible research using generative AI. We also recognize generative AI's limitations and underscore the indispensable role of human researchers throughout the process.

The swift incorporation of artificial intelligence (AI) into creative fields has brought about significant opportunities and challenges, especially in design and the creativity of human learning. This study explores the changing dynamics between AI-generated design and human creative processes, highlighting the interaction between technological advancements and human artistic insight. As AI systems grow more adept at producing innovative and visually appealing designs, the potential for collaboration between designers and AI becomes a vital area for investigation. This study looks into how AI can enhance human abilities, emphasizing methods to utilize AI to expand creative horizons without undermining the distinct contributions of designers. The ability of AI to assist in idea generation, optimize workflows, and inspire fresh design concepts is weighed against the necessity of keeping human oversight and decision-making central to the creative process. This pilot study-based research includes both qualitative and quantitative analyses of existing case studies that feature AI-human design partnerships, interviews with professionals who utilize AI tools, and 186 survey respondents assessing perceptions of AI's role within the creative sector. The study also investigates frameworks for incorporating AI into educational environments, where it can help foster creativity and critical thinking skills among learners. The findings shed light on the elements that facilitate effective collaboration between AI and human designers, such as the significance of user-friendly interfaces, adaptable algorithms, and ethical considerations. Additionally, the study pinpointed best practices for creating a synergy that enhances both technological progress and human creativity. This research adds to the broader discussion about the future of creativity in a world influenced by AI by promoting a collaborative relationship between human and machine intelligence.