Search Results (54)

(1) Background: Midwifery students require practical training experience to learn how to perform a medical history. Simulation-based training, such as chatbot exercises using large language models like GPT, provide structured practice but require ongoing evaluation. This study explores German midwifery students’ views on using an AI chatbot simulating a pregnant woman, regarding usability, realism, and educational value. (2) Methods: Twenty-six students participated in a descriptive, quantitative cross-sectional survey, using a literature-based, self-developed questionnaire after interacting with the AI generative chatbot. Data were analyzed via SPSS 30.0, with results shown in a stacked horizontal bar chart. (3) Results: The findings indicate that students experienced no difficulties when interacting with the chatbot. Both the quality and realism of the conversations were evaluated positively. Chatbot training was perceived as helpful in supporting structured medical history interviews and the collection of relevant data but was not considered a substitute for practice with actors or real-life situations. (4) Conclusions: The findings suggest that the medical history chatbot offers midwifery students an innovative, flexible simulation for training. Students responded positively, and it may help develop structured history-taking skills. Further study is needed to determine if repeated chatbot use improves medical history collection skills. Full article

This paper examines students’ perceptions of factors influencing normative support for the integration of AI Chatbots in universities, providing an empirical basis for developing institutional policies and implementation strategies in higher education. Framed within the sustainability perspective, the study examines how ethical, cognitive, and perceptual factors shape the long-term adoption of AI technologies in academic environments. Our study employs a structural model comprising 10 constructs, 46 items, and 9 hypotheses, tested on a sample of 408 economics students from Timisoara. The research identifies AI literacy as the most influential factor in the formal integration of these technologies in universities. The following factors have a direct impact: teacher perception, student perception, and cognitive risks (reliance on AI Chatbots and avoidance of intellectual effort). Use for personalized learning is a factor with a significant direct effect on positive perceptions and intentions to use AI Chatbots among students. Academic integrity risks, as well as limitations on accuracy and reliability, have no significant impact. AI Chatbots represent an essential opportunity to transform higher education. However, their positive impact is realized only through responsible formal integration, grounded in ethical policies, adequate digital education, and the adaptation of pedagogical practices. Universities must regard AI as a strategic ally for teachers and students, while keeping human interaction, critical thinking, and academic integrity at the centre of the educational process. The study argues that students’ perceptions are that universities must approach AI Integration as a strategic component of sustainable educational ecosystems, aligning innovation with long-term academic integrity and the objectives of sustainable development, particularly Sustainable Development Goal 4 (Quality Education). Full article

The increasing use of digital devices by young learners often results in passive content consumption rather than active skill development. This exploratory study examines whether a peer-like Artificial Intelligence (AI) agent can improve the quality of computer-supported collaborative learning. The aim was to assess the impact of a hidden AI-based chatbot on the dynamics and outcomes of group problem-solving in a school setting. A gamified application was developed in which student groups collaborated on challenging tasks. In a controlled experiment, some groups included a hidden AI-based chatbot acting as a peer, programmed to provide Socratic prompts and motivational scaffolding without giving direct answers, while control groups consisted only of human participants. Quantitative and qualitative data, including time to solution, answer correctness, and chat logs, were collected to compare performance and interaction patterns between the two conditions. Given the limited sample size and primarily descriptive analyses, the findings should be interpreted as preliminary. The results suggest differences in collaborative dynamics and problem-solving efficiency between groups assisted by the AI agent and the unassisted control groups. The findings suggest that integrating a hidden, peer-like pedagogical agent may represent a promising approach for supporting collaborative learning processes, enhancing group engagement by subtly guiding discussion without disrupting the natural peer-to-peer dynamic. These results highlight the potential of hidden AI to enhance collaborative learning environments through non-intrusive support. Further research with larger samples is needed to validate these initial observations. Full article

The interdisciplinary nature of artificial intelligence courses forces non-computer science majors to contend with the simultaneous challenges of terminology comprehension and language cognition. To increase the efficiency of terminology teaching, this project develops and deploys an OpenAI-based AI chatbot teaching system that incorporates the concept of content and language integrated learning (CLIL). The system creates a dual-track “terminology layer-cognition layer” framework that includes term recognition, multi-level explanation (contextual examples and conceptual associations), task-driven dialogues, and conversation memory bank (CMB) modules. It then guides students through natural language interactions to master the core AI terms in context. The system’s effectiveness was confirmed in a controlled experiment with 98 participants (including computer and non-computer majors) separated into two groups: experimental (chatbot teaching) and control (conventional PPT teaching). In terms of terminology mastery, the experimental group’s posttest score (86.0 ± 5.33) was considerably higher than that of the control group (66.98 ± 5.6). Non-computer science major students showed a more significant improvement effect (83.29 ± 4.5 vs. 63.62 ± 4.68 for the control group). Non-computing students evaluated the clarity of systematic terminology explanation (4.33 ± 0.76) and the effectiveness of contextual assistance (4.21 ± 0.88) as the most important aspects of their learning experience. These experimental results show that the fusion AI chatbot teaching system developed in this study can improve teaching efficiency while effectively reducing cognitive load, and that the task-guided and immediate feedback mechanism can significantly increase students’ learning engagement. Full article

During the past decade, chatbots have been integrated into commercial platforms to facilitate second language acquisition (SLA) by providing opportunities for interactive conversations. However, SLA learner progress is limited by chatbots that lack the contextualization typically added by instructors to college and university courses. The present study focuses on a collaborative Digital Learning Incubator (DLI) project dedicated to creating and testing a chatbot with a physical form, or avatar chatbot, called Slabot (Second Language Acquisition Bot), in two upper-level university courses at the University of Tennessee, asynchronous online Spanish 331 (Introduction to Hispanic Culture), and in-person Spanish 434 (Hispanic Culture Through Film). Students in these two courses believe that their oral skills would benefit from more opportunities to speak in Spanish. To provide the students with more practice and instructors with a tool for assessing Spanish oral skills in online and in-person courses, the DLI project objective was to advance current avatar chatbot platforms by enabling Slabot to elicit student responses appropriate for evaluation according to the American Council on the Teaching of Foreign Languages (ACTFL) standards. An initial test of Slabot was conducted, and the results demonstrated the potential for Slabot to achieve the project objective. Full article

Background/Objectives: The growing integration of Artificial Intelligence (AI) and chatbots in health professional education offers innovative methods to enhance learning and clinical preparedness. This study aimed to evaluate the educational impact and perceptions in university students of Human Nutrition and Dietetics, regarding the utility, usability, and design of the E+DIEting_Lab chatbot platform when implemented in clinical nutrition training. Methods: The platform was piloted from December 2023 to April 2025 involving 475 students from multiple European universities. While all 475 students completed the initial survey, 305 finished the follow-up evaluation, representing a 36% attrition rate. Participants completed surveys before and after interacting with the chatbots, assessing prior experience, knowledge, skills, and attitudes. Data were analyzed using descriptive statistics and independent samples t-tests to compare pre- and post-intervention perceptions. Results: A total of 475 university students completed the initial survey and 305 the final evaluation. Most university students were females (75.4%), with representation from six languages and diverse institutions. Students reported clear perceived learning gains: 79.7% reported updated practical skills in clinical dietetics and communication were improved, 90% felt that new digital tools improved classroom practice, and 73.9% reported enhanced interpersonal skills. Self-rated competence in using chatbots as learning tools increased significantly, with mean knowledge scores rising from 2.32 to 2.66 and skills from 2.39 to 2.79 on a 0–5 Likert scale (p < 0.001 for both). Perceived effectiveness and usefulness of chatbots as self-learning tools remained positive but showed a small decline after use (effectiveness from 3.63 to 3.42; usefulness from 3.63 to 3.45), suggesting that hands-on experience refined, but did not diminish, students’ overall favorable views of the platform. Conclusions: The implementation and pilot evaluation of the E+DIEting_Lab self-learning virtual patient chatbot platform demonstrate that structured digital simulation tools can significantly improve perceived clinical nutrition competences. These findings support chatbot adoption in dietetics curricula and inform future digital education innovations. Full article

Individuals are increasingly using artificial intelligence chatbots, such as ChatGPT, to seek conversational support for their personal mental health difficulties. Heightened concerns about mental health stigma may make anonymous, on-demand chatbot interactions more appealing for some than traditional face-to-face support. This study examined if using ChatGPT-4 for personal mental health difficulties is associated with two distinct forms of stigma, anticipated stigma and self-stigma. Our main aim was to investigate if the perceived effectiveness of ChatGPT use for mental health issues mediates the relationship between ChatGPT usage and anticipated stigma and self-stigma. The sample comprised 73 participants, mostly undergraduate psychology students. Participants completed online self-report measures to assess ChatGPT usage for mental health purposes, perceived effectiveness of ChatGPT for mental health issues, and anticipated stigma and self-stigma. Perceived effectiveness of ChatGPT was significantly and positively correlated with ChatGPT usage, and significantly negatively correlated with reduced anticipated stigma. Cross-sectional analyses found that perceived effectiveness significantly mediated the relationship between ChatGPT use and anticipated stigma, but not for self-stigma. The results indicate that ChatGPT use, when perceived as effective, is associated with a reduction in anticipated stigma concerning mental health issues. More research is now needed in this emerging area to inform best practice on the use of AI aids for mental health issues. Full article

We surveyed 297 STEM undergraduates at a single English-medium Sino–UK joint institution to document perceptions of AI chatbots for learning. Students reported high willingness to adopt AI chatbots (78%; 95% CI: 73.1–82.4) alongside concerns about over-reliance (67%; 95% CI: 61.4–72.1), content quality (52%; 95% CI: 46.2–57.5), and reduced human interaction (42%; 95% CI: 36.5–47.8). Over half (52%; 95% CI: 46.3–57.7) requested language/terminology support features, whereas only 16.8% reported language-related barriers. We attempted exploratory factor analysis and k-means clustering, but neither met the inclusion criteria; therefore, we report item-level frequencies only. The findings are descriptive and not generalisable (53% first-year, 80% male convenience sample). These patterns generate testable hypotheses about verification scaffolds, language support utility, and human–AI balance that warrant investigation through controlled studies. Full article

This study investigates the adoption of PyChatAI, a bilingual AI-powered chatbot for Python programming education, among female computer science students at Jouf University. Guided by the Technology Acceptance Model 3 (TAM3), it examines the determinants of user acceptance and usage behaviour. A Solomon Four-Group experimental design (N = 300) was used to control pre-test effects and isolate the impact of the intervention. PyChatAI provides interactive problem-solving, code explanations, and topic-based tutorials in English and Arabic. Measurement and structural models were validated via Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM), achieving excellent fit (CFI = 0.980, RMSEA = 0.039). Results show that perceived usefulness (β = 0.446, p < 0.001) and perceived ease of use (β = 0.243, p = 0.005) significantly influence intention to use, which in turn predicts actual usage (β = 0.406, p < 0.001). Trust, facilitating conditions, and hedonic motivation emerged as strong antecedents of ease of use, while social influence and cognitive factors had limited impact. These findings demonstrate that AI-driven bilingual tools can effectively enhance programming engagement in gender-specific, culturally sensitive contexts, offering practical guidance for integrating intelligent tutoring systems into computer science curricula. Full article

Drawing on the Technological Pedagogical Content Knowledge (TPACK) framework, this qualitative case study investigates how university English teachers integrate AI chatbots into flipped classrooms. Findings reveal that teachers employed chatbots across multiple pedagogical functions—including vocabulary support, grammar explanation, dialogue simulation, and creative content generation—embedded purposefully into both pre-class preparation and in-class collaboration. Rather than passively adopting these tools, teachers strategically positioned chatbots to enhance student autonomy, confidence, and interaction, while tailoring their use to suit specific flipped classroom designs. Meanwhile, teachers acknowledged the risks of over-reliance on AI chatbot content and the disruptions caused by vague or incorrect responses. They responded by developing structured guidance and reforming their roles as facilitators rather than content deliverers. This study contributes new insights into teacher agency in AI-mediated language education, highlighting the complex pedagogical negotiations required to meaningfully integrate emerging technologies into flipped learning environments. Full article

A learning management system (LMS) plays a crucial role in supporting students’ educational activities by centralized platforms for course delivery, communication, and student support. Recently, many universities have integrated chatbots into their LMS to assist students with various inquiries and tasks. However, existing chatbots often necessitate human interventions to manually respond to complex queries, resulting in limited scalability and efficiency. In this paper, we present a memory-augmented large language model (LLM) framework that enhances the reasoning and contextual continuity of LMS-based chatbots. The proposed framework first embeds user queries and retrieves semantically relevant entries from various LMS resources, including instructional documents and academic frequently asked questions. Retrieved entries are then filtered through a two-stage confidence filtering process that combines similarity thresholds and LLM-based semantic validation. Validated information, along with user queries, is processed by LLM for response generation. To maintain coherence in multi-turn interactions, the chatbot incorporates short-term, long-term, and temporal event memories, which track conversational flow and personalize responses based on user-specific information, such as recent activity history and individual preferences. To evaluate response quality, we employed a multi-layered evaluation strategy combining BERTScore-based quantitative measurement, an LLM-as-a-Judge approach for automated semantic assessment, and a user study under multi-turn scenarios. The evaluation results consistently confirm that the proposed framework improves the consistency, clarity, and usefulness of the responses. These findings highlight the potential of memory-augmented LLMs for scalable and intelligent learning support within university environments. Full article

The growing application of artificial intelligence in education has created new avenues for second language learning. The following research explores the impact of learning with the help of chatbots on English conversation among secondary students in the Northern Borders Region in Saudi Arabia. The quasi-experimental design involved 30 students divided into two groups: an experimental group that interacted with an intervention using a GPT-powered chatbot for three weeks, and a control group that underwent traditional teaching. Pre- and post-tests were given to assess conversation competence. At the same time, students’ attitudes toward the chatbot-assisted learning experience were measured through questionnaires, teacher observation, and usage logs in the chatbot. Results showed statistically significant improvement in the experimental group’s speaking competence (mean gain = 5.24, p < 0.001). Students showed high motivation, elevated confidence, and high satisfaction with the learning experience provided through the chatbot (overall attitude mean = 4.35/5). Teacher observations testified that the students were much more engaged and spontaneous, and using the chatbot was positively correlated with score gain (r = 0.61). The outcomes indicate that chatbot-based learning is a practical approach for facilitating the development of spoken English, particularly in low-resource learning environments. The research provides empirical proof in favour of the incorporation of interactive AI into EFL teaching in all the secondary schools in Saudi Arabia. Full article

Artificial intelligence (AI) is reshaping customer service, with AI-powered chatbots serving as a critical component in delivering continuous support across sales, marketing, and service domains, thereby enhancing operational efficiency. However, consumer engagement remains suboptimal, as many users favor human interaction due to concerns regarding chatbots’ ability to address complex issues and their perceived lack of empathy, which subsequently reduces satisfaction and sustainable usage. This study examines the determinants of user attitude and identifies factors influencing sustainable chatbot use. Utilizing survey data from 735 Chinese university students who have engaged with AI-powered chatbots, the analysis reveals that four key motivational categories: utilitarian (information acquisition), hedonic (enjoyment and time passing), technology (media appeal), and social (social presence and interaction) significantly influence user attitude toward chatbot services. Conversely, privacy invasion exerts a negative impact on user attitude, suggesting that while chatbots provide certain benefits, privacy issues can significantly undermine user satisfaction. Moreover, the findings suggest that user attitude serves as a pivotal determinant in fostering both user stickiness and sustainable usage of chatbot services. This study advances prior U&G-, TAM-, and ECM-based research by applying these frameworks to AI-powered chatbots in business communication, refining the U&G model with four specific motivations, integrating perceived privacy invasion to bridge gratification theory with risk perception, and directly linking user motivations to business outcomes such as attitude and stickiness. This study underscores that optimizing chatbot functionalities to enhance user gratification while mitigating privacy risks can substantially improve user satisfaction and stickiness, offering valuable implications for businesses aiming to enhance customer loyalty through AI-powered services. Full article

As digital transformation continues to redefine education, understanding how emerging technologies can enhance self-directed learning (SDL) becomes essential for learners, educators, instructional designers, and policymakers, as this approach supports personalized learning, strengthens student autonomy, and responds to the demands of more flexible and dynamic educational environments. This systematic review examines how artificial intelligence (AI) tools enhance SDL by offering personalized, adaptive, and real-time support for learners in online environments. Following the PRISMA 2020 methodology, a literature search was conducted to identify relevant studies published between 2020 and 2025. After applying inclusion, exclusion, and quality criteria, 77 studies were selected for in-depth analysis. The findings indicate that AI-powered tools such as intelligent tutoring systems, chatbots, conversational agents, and natural language processing applications promote learner autonomy, enable self-regulation, provide real-time feedback, and support individualized learning paths. However, several challenges persist, including overreliance on technology, cognitive overload, and diminished human interaction. These insights suggest that, while AI plays a transformative role in the evolution of education, its integration must be guided by thoughtful pedagogical design, ethical considerations, and a learner-centered approach to fully support the future of education through the internet. Full article

As generative artificial intelligence (GenAI) chatbots gain traction in educational settings, a growing number of studies explore their potential for personalized, scalable learning. However, methodological fragmentation has limited the comparability and generalizability of findings across the field. This study proposes a unified, learning analytics–driven framework for evaluating the impact of GenAI chatbots on student learning. Grounded in the collection, analysis, and interpretation of diverse learner data, the framework integrates assessment outcomes, conversational interactions, engagement metrics, and student feedback. We demonstrate its application through a multi-week, quasi-experimental study using a Socratic-style chatbot designed with pedagogical intent. Using clustering techniques and statistical analysis, we identified patterns in student–chatbot interaction and linked them to changes in learning outcomes. This framework provides researchers and educators with a replicable structure for evaluating GenAI interventions and advancing coherence in learning analytics–based educational research. Full article