Artificial Intelligence Chatbot Research Articles

The Impact of Performance Expectancy, Workload, Risk, and Satisfaction on Trust in ChatGPT: Cross-Sectional Survey Analysis.

ChatGPT (OpenAI) is a powerful tool for a wide range of tasks, from entertainment and creativity to health care queries. There are potential risks and benefits associated with this technology. In the discourse concerning the deployment of ChatGPT and similar large language models, it is sensible to recommend their use primarily for tasks a human user can execute accurately. As we transition into the subsequent phase of ChatGPT deployment, establishing realistic performance expectations and understanding users' perceptions of risk associated with its use are crucial in determining the successful integration of this artificial intelligence (AI) technology. The aim of the study is to explore how perceived workload, satisfaction, performance expectancy, and risk-benefit perception influence users' trust in ChatGPT. A semistructured, web-based survey was conducted with 607 adults in the United States who actively use ChatGPT. The survey questions were adapted from constructs used in various models and theories such as the technology acceptance model, the theory of planned behavior, the unified theory of acceptance and use of technology, and research on trust and security in digital environments. To test our hypotheses and structural model, we used the partial least squares structural equation modeling method, a widely used approach for multivariate analysis. A total of 607 people responded to our survey. A significant portion of the participants held at least a high school diploma (n=204, 33.6%), and the majority had a bachelor's degree (n=262, 43.1%). The primary motivations for participants to use ChatGPT were for acquiring information (n=219, 36.1%), amusement (n=203, 33.4%), and addressing problems (n=135, 22.2%). Some participants used it for health-related inquiries (n=44, 7.2%), while a few others (n=6, 1%) used it for miscellaneous activities such as brainstorming, grammar verification, and blog content creation. Our model explained 64.6% of the variance in trust. Our analysis indicated a significant relationship between (1) workload and satisfaction, (2) trust and satisfaction, (3) performance expectations and trust, and (4) risk-benefit perception and trust. The findings underscore the importance of ensuring user-friendly design and functionality in AI-based applications to reduce workload and enhance user satisfaction, thereby increasing user trust. Future research should further explore the relationship between risk-benefit perception and trust in the context of AI chatbots.

Using artificial intelligence chatbots to improve patient history taking in dental education (Pilot study).

Using artificial intelligence chatbots to improve patient history taking in dental education (Pilot study).

Student satisfaction with artificial intelligence chatbots in Ethiopian academia

This study explores student satisfaction with artificial intelligence chatbots in Ethiopian academia, examining their usage patterns, satisfaction levels, benefits, concerns and recommendations for improvement. A quantitative survey was conducted among 367 Ethiopian students from various educational levels and regions. The survey collected data on artificial intelligence chatbot usage, satisfaction, benefits, concerns and improvement recommendations. The findings reveal that artificial intelligence chatbots are frequently used for academic purposes, with students primarily using them for research assistance, assignment help and exam preparation. The students reported moderate to high satisfaction with chatbots, particularly regarding responsiveness, accuracy and adaptability. The benefits included time-saving, improved academic performance and 24/7 access to information. Concerns focused on access to technology, privacy and data security. The students also provided recommendations for enhancing chatbots, such as localisation, cultural sensitivity, affordability and security.

Rubric Development and Validation for Assessing Tasks' Solving via AI Chatbots

This research aimed to develop and validate a rubric to assess Artificial Intelligence (AI) chatbots' effectiveness in accomplishing tasks, particularly within educational contexts. Given the rapidly growing integration of AI in various sectors, including education, a systematic and robust tool for evaluating AI chatbot performance is essential. This investigation involved a rigorous process including expert involvement to ensure content validity, as well as the application of statistical tests for assessing internal consistency and reliability. Factor analysis also revealed two significant domains, "Quality of Content" and "Quality of Expression", which further enhanced the construct validity of the evaluation scale. The results from this investigation robustly affirm the reliability and validity of the developed rubric, thus marking a significant advancement in the sphere of AI chatbot performance evaluation within educational contexts. Nonetheless, the study simultaneously emphasizes the requirement for additional validation research, specifically those entailing a variety of tasks and diverse AI chatbots, to further corroborate these findings. The ramifications of this research are profound, offering both researchers and practitioners engaged in chatbot development and evaluation a comprehensive and validated framework for the assessment of chatbot performance.

Assessing ChatGPT vs. Standard Medical Resources for Endoscopic Sleeve Gastroplasty Education: A Medical Professional Evaluation Study.

The Chat Generative Pre-Trained Transformer (ChatGPT) represents a significant advancement in artificial intelligence (AI) chatbot technology. While ChatGPT offers promising capabilities, concerns remain about its reliability and accuracy. This study aims to evaluate ChatGPT's responses to patients' frequently asked questions about Endoscopic Sleeve Gastroplasty (ESG). Expert Gastroenterologists and Bariatric Surgeons, with experience in ESG, were invited to evaluate ChatGPT-generated answers to eight ESG-related questions, and answers sourced from hospital websites. The evaluation criteria included ease of understanding, scientific accuracy, and overall answer satisfaction. They were also tasked with discerning whether each response was AI generated or not. Twelve medical professionals with expertise in ESG participated, 83.3% of whom had experience performing the procedure independently. The entire cohort possessed substantial knowledge about ESG. ChatGPT's utility among participants, rated on a scale of one to five, averaged 2.75. The raters demonstrated a 54% accuracy rate in distinguishing AI-generated responses, with a sensitivity of 39% and specificity of 60%, resulting in an average of 17.6 correct identifications out of a possible 31. Overall, there were no significant differences between AI-generated and non-AI responses in terms of scientific accuracy, understandability, and satisfaction, with one notable exception. For the question defining ESG, the AI-generated definition scored higher in scientific accuracy (4.33 vs. 3.61, p = 0.007) and satisfaction (4.33 vs. 3.58, p = 0.009) compared to the non-AI versions. This study underscores ChatGPT's efficacy in providing medical information on ESG, demonstrating its comparability to traditional sources in scientific accuracy.

Physician and Artificial Intelligence Chatbot Responses to Cancer Questions From Social Media.

Artificial intelligence (AI) chatbots pose the opportunity to draft template responses to patient questions. However, the ability of chatbots to generate responses based on domain-specific knowledge of cancer remains to be tested. To evaluate the competency of AI chatbots (GPT-3.5 [chatbot 1], GPT-4 [chatbot 2], and Claude AI [chatbot 3]) to generate high-quality, empathetic, and readable responses to patient questions about cancer. This equivalence study compared the AI chatbot responses and responses by 6 verified oncologists to 200 patient questions about cancer from a public online forum. Data were collected on May 31, 2023. Random sample of 200 patient questions related to cancer from a public online forum (Reddit r/AskDocs) spanning from January 1, 2018, to May 31, 2023, was posed to 3 AI chatbots. The primary outcomes were pilot ratings of the quality, empathy, and readability on a Likert scale from 1 (very poor) to 5 (very good). Two teams of attending oncology specialists evaluated each response based on pilot measures of quality, empathy, and readability in triplicate. The secondary outcome was readability assessed using Flesch-Kincaid Grade Level. Responses to 200 questions generated by chatbot 3, the best-performing AI chatbot, were rated consistently higher in overall measures of quality (mean, 3.56 [95% CI, 3.48-3.63] vs 3.00 [95% CI, 2.91-3.09]; P < .001), empathy (mean, 3.62 [95% CI, 3.53-3.70] vs 2.43 [95% CI, 2.32-2.53]; P < .001), and readability (mean, 3.79 [95% CI, 3.72-3.87] vs 3.07 [95% CI, 3.00-3.15]; P < .001) compared with physician responses. The mean Flesch-Kincaid Grade Level of physician responses (mean, 10.11 [95% CI, 9.21-11.03]) was not significantly different from chatbot 3 responses (mean, 10.31 [95% CI, 9.89-10.72]; P > .99) but was lower than those from chatbot 1 (mean, 12.33 [95% CI, 11.84-12.83]; P < .001) and chatbot 2 (mean, 11.32 [95% CI, 11.05-11.79]; P = .01). The findings of this study suggest that chatbots can generate quality, empathetic, and readable responses to patient questions comparable to physician responses sourced from an online forum. Further research is required to assess the scope, process integration, and patient and physician outcomes of chatbot-facilitated interactions.

Mental health stigma: a conundrum for healthcare practitioners in conservative communities

This paper presents perspectives on the stigma and shame around mental health in conservative communities, and some of the issues faced by health systems in those communities. The various causes of stigma are explored, and how these are often more pronounced in culturally reserved, conservative communities. While health systems are supposed to provide support for mental health sufferers, this stigma sometimes even extends to healthcare workers, which can discourage patients from asking for assistance. Solutions and reforms are needed, for example education programs; addressing gender norms, and the consideration of culture and religion, to form effective solutions. It is also suggested that alternative therapies and support mechanisms, including digital solutions such as artificial intelligence chatbots, may be useful to provide much needed support to individuals with poor mental health. Along with integrating options such as CBT (cognitive behavioral therapy), it may be useful to draw on indigenous psychologies, such as Islamic psychology, as a way of decolonizing approaches. Therefore, when considering solutions, cultural and religious norms must be considered to ensure their efficacy and acceptance.

Enhancing Readability of Online Patient-Facing Content: The Role of AI Chatbots in Improving Cancer Information Accessibility.

Internet-based health education is increasingly vital in patient care. However, the readability of online information often exceeds the average reading level of the US population, limiting accessibility and comprehension. This study investigates the use of chatbot artificial intelligence to improve the readability of cancer-related patient-facing content. We used ChatGPT 4.0 to rewrite content about breast, colon, lung, prostate, and pancreas cancer across 34 websites associated with NCCN Member Institutions. Readability was analyzed using Fry Readability Score, Flesch-Kincaid Grade Level, Gunning Fog Index, and Simple Measure of Gobbledygook. The primary outcome was the mean readability score for the original and artificial intelligence (AI)-generated content. As secondary outcomes, we assessed the accuracy, similarity, and quality using F1 scores, cosine similarity scores, and section 2 of the DISCERN instrument, respectively. The mean readability level across the 34 websites was equivalent to a university freshman level (grade 13±1.5). However, after ChatGPT's intervention, the AI-generated outputs had a mean readability score equivalent to a high school freshman education level (grade 9±0.8). The overall F1 score for the rewritten content was 0.87, the precision score was 0.934, and the recall score was 0.814. Compared with their original counterparts, the AI-rewritten content had a cosine similarity score of 0.915 (95% CI, 0.908-0.922). The improved readability was attributed to simpler words and shorter sentences. The mean DISCERN score of the random sample of AI-generated content was equivalent to "good" (28.5±5), with no significant differences compared with their original counterparts. Our study demonstrates the potential of AI chatbots to improve the readability of patient-facing content while maintaining content quality. The decrease in requisite literacy after AI revision emphasizes the potential of this technology to reduce health care disparities caused by a mismatch between educational resources available to a patient and their health literacy.

Performance of artificial intelligence chatbots in interpreting clinical images of pressure injuries.

To evaluate the accuracy of AI chatbots in staging pressure injuries according to the National Pressure Injury Advisory Panel (NPIAP) Staging through clinical image interpretation, a cross-sectional design was conducted to assess five leading publicly available AI chatbots. As a result, three chatbots were unable to interpret the clinical images, whereas GPT-4 Turbo achieved a high accuracy rate (83.0%) in staging pressure injuries, notably outperforming BingAI Creative mode (24.0%) with statistical significance (p < 0.001). GPT-4 Turbo accurately identified Stages 1 (p < 0.001), 3 (p = 0.001), and 4 (p < 0.001) pressure injuries, and suspected deep tissue injuries (p < 0.001), while BingAI demonstrated significantly lower accuracy across all stages. The findings highlight the potential of AI chatbots, especially GPT-4 Turbo, in accurately diagnosing images and aiding the subsequent management of pressure injuries.

Evaluating ChatGPT's Ability to Answer Common Patient Questions Regarding Hip Fracture.

ChatGPT is an artificial intelligence chatbot software programmed for conversational applications using reinforcement learning techniques. With its growing popularity and overall versatility, it is likely that ChatGPT's applications will expand into health care especially because it relates to patients researching their injuries. The purpose of this study was to investigate ChatGPT's ability to accurately answer frequently asked questions regarding hip fractures. Eleven frequently asked questions regarding hip fractures were posed to ChatGPT, and the responses were recorded in full. Five of these questions were determined to be high-yield based on the likelihood that a patient would ask the question to a chatbot software. The chatbot's responses were analyzed by five fellowship-trained orthopaedic trauma surgeons for their quality and accuracy using an evidence-based approach. The chatbot's answers were rated as "Excellent response requiring no clarification", "Satisfactory response requiring minimal clarification", "Satisfactory response requiring moderate clarification", or "Unsatisfactory response requiring significant clarification." Of the five high-yield questions posed to the chatbot, no question was determined to be unsatisfactory requiring significant clarification by the authors. The remaining responses were either satisfactory requiring minimal clarification (n = 3) or satisfactory requiring moderate clarification (n = 2). The chatbot was generally found to provide unbiased and evidence-based answers that would be clearly understood by most orthopaedic patients. These findings suggest that ChatGPT has the potential to be an effective patient education tool especially because it continues to grow and improve as a chatbot application. Level IV study.

Subscription intentions for ChatGPT plus: a look at user satisfaction and self-efficacy

PurposeThis study examines the key determinants of subscription intentions for ChatGPT Plus (paid version) in business settings, focusing on tasks such as system quality, information support, service quality, perceived intelligence, goal-congruent outcome and self-efficacy.Design/methodology/approachThe study utilized a survey of office workers, analyzed through structural equation modeling, to explore these determinants.FindingsThe results demonstrate that system quality, service quality and perceived intelligence significantly influence satisfaction, while service quality and perceived intelligence also impact goal-congruent outcomes. Contrary to traditional models, satisfaction does not significantly correlate with usage. Instead, a significant relationship is observed between goal-congruent outcomes and usage. Self-efficacy emerges as a crucial predictor of subscription intentions, further underlined by the significant impact of usage on subscription intention.Research limitations/implicationsThe study’s focus on office workers and a single artificial intelligence (AI) chatbot type may limit generalizability. Its findings illuminate several avenues for future research, particularly in diversifying the context and demographics studied.Practical implicationsThis research offers actionable insights for businesses and practitioners in the implementation of AI chatbots. It highlights the importance of enhancing system quality, personalization and user confidence to boost subscription intentions, thereby guiding strategies for user engagement and technology adoption.Originality/valueThis study pioneers in investigating subscription intentions towards AI chatbots, particularly ChatGPT, providing a novel framework that expands upon traditional user behavior theories.

Use of large language model-based chatbots in managing the rehabilitation concerns and education needs of outpatient stroke survivors and caregivers.

The utility of large language model-based (LLM) artificial intelligence (AI) chatbots in many aspects of healthcare is becoming apparent though their ability to address patient concerns remains unknown. We sought to evaluate the performance of two well-known, freely-accessible chatbots, ChatGPT and Google Bard, in responding to common questions about stroke rehabilitation posed by patients and their caregivers. We collected questions from outpatients and their caregivers through a survey, categorised them by theme, and created representative questions to be posed to both chatbots. We then evaluated the chatbots' responses based on accuracy, safety, relevance, and readability. Interrater agreement was also tracked. Although both chatbots achieved similar overall scores, Google Bard performed slightly better in relevance and safety. Both provided readable responses with some general accuracy, but struggled with hallucinated responses, were often not specific, and lacked awareness of the possibility for emotional situations with the potential to turn dangerous. Additionally, interrater agreement was low, highlighting the variability in physician acceptance of their responses. AI chatbots show potential in patient-facing support roles, but issues remain regarding safety, accuracy, and relevance. Future chatbots should address these problems to ensure that they can reliably and independently manage the concerns and questions of stroke patients and their caregivers.

Comparing and assessing four AI chatbots' competence in economics.

Artificial Intelligence (AI) chatbots have emerged as powerful tools in modern academic endeavors, presenting both opportunities and challenges in the learning landscape. They can provide content information and analysis across most academic disciplines, but significant differences exist in terms of response accuracy for conclusions and explanations, as well as word counts. This study explores four distinct AI chatbots, GPT-3.5, GPT-4, Bard, and LLaMA 2, for accuracy of conclusions and quality of explanations in the context of university-level economics. Leveraging Bloom's taxonomy of cognitive learning complexity as a guiding framework, the study confronts the four AI chatbots with a standard test for university-level understanding of economics, as well as more advanced economics problems. The null hypothesis that all AI chatbots perform equally well on prompts that explore understanding of economics is rejected. The results are that significant differences are observed across the four AI chatbots, and these differences are exacerbated as the complexity of the economics-related prompts increased. These findings are relevant to both students and educators; students can choose the most appropriate chatbots to better understand economics concepts and thought processes, while educators can design their instruction and assessment while recognizing the support and resources students have access to through AI chatbot platforms.

How artificial intelligence chatbots becomes author’s true friend in medical writing without risking ethical violations

[No abstract available]

Unlocking Health Literacy: The Ultimate Guide to Hypertension Education From ChatGPT Versus Google Gemini.

Background Google Gemini (Google, Mountain View, CA) represents the latest advances in the realm of artificial intelligence (AI) and has garnered attention due to its capabilities similar to the increasingly popular ChatGPT (OpenAI, San Francisco, CA). Accurate dissemination of information on common conditions such as hypertension is critical for patient comprehension and management. Despite the ubiquity of AI, comparisons between ChatGPT and Gemini remain unexplored. Methods ChatGPT and Gemini were asked 52 questions derived from the American College of Cardiology's (ACC) frequently asked questions on hypertension, following a specified prompt. Prompts included: no prompting (Form 1), patient-friendly prompting (Form 2), physician-level prompting (Form 3), and prompting for statistics/references (Form 4). Responses were scored as incorrect, partially correct, or correct. Flesch-Kincaid (FK) grade level and word count were recorded. Results Across all forms, scoring frequencies were as follows: 23 (5.5%) incorrect, 162 (38.9%) partially correct, and 231 (55.5%) correct. ChatGPT showed higher rates of partially correct answers than Gemini (p = 0.0346). Physician-level prompts resulted in a higher word count across both platforms (p < 0.001). ChatGPT showed a higher FK grade level (p = 0.033) in physician-friendly prompting. Gemini exhibited a significantly higher mean word count (p < 0.001); however, ChatGPT had a higher FK grade level across all forms (p > 0.001). Conclusion To our knowledge, this study is the first to compare cardiology-related responses from ChatGPT and Gemini, two of the most popular AI chatbots. The grade level for most responses was collegiate level, which was above average for the National Institutes of Health (NIH) recommendations, but on par with most online medical information. Both chatbots responded with a high degree of accuracy, with inaccuracies being rare. Therefore, it is reasonable that cardiologists suggest either chatbot as a source of supplementary education.

Examining the Influence of AI Chatbots on Semantic Web-Based Global Information Management in Various Industries

This article presents a comprehensive analysis of the application and effect of ChatGPT, an advanced AI chatbot model, on global information management across various industries such as healthcare, industry, education, and more. Leveraging a dataset obtained from the Scopus database encompassing research papers from 2022 to 2023, this study investigates the influence of ChatGPT by examining publisher impact, authorship patterns based on Lotka's Law, country-specific scientific production, and keyword distribution. The analysis sheds light on prominent publishers, prolific authors, geographic distribution of research contributions, and prevailing research themes. By understanding the impact of ChatGPT in these sectors, this research contributes to the advancement of knowledge and facilitates informed decision-making regarding the responsible and effective utilization of artificial intelligence (AI) chatbot technologies in global information management.

Study of adoption of artificial intelligence technology-driven natural large language model-based chatbots by firms for customer service interaction

Purpose The purpose of this study was to comprehend the adoption of artificial intelligence (AI) technology-driven natural large language model (LLM)-based chatbots by customers. Design/methodology/approach A qualitative research study method was conducted. This was to explore managerial perspectives towards consumer centric technology adoption of AI plus LLM-based chatbots. This was specifically for AI-driven natural LLM-based chatbots services. The author conducted conducted in-depth personal interviews with 32 experts of digital content AI + LLM chatbot services. Thematic content analysis was undertaken to analyse the data. Findings The advent of natural language processing tools driven by AI technology chatbots has altered human-firm interaction. The research findings indicated that the push-pull-mooring (PPM) factors captured the phenomenon in the most comprehensive way. A total of 15 key factors influencing the adoption of AI technology-driven natural LLM-based chatbots by customers during firm customer interaction were identified in this study by the author. The thematic content analysis unraveled insights regarding transformed consumer adoptions towards AI-driven LLM-based chatbots by means of the PPM framework factors. Research limitations/implications The empirical research investigation contributed to the literature on the PPM theoretical framework. This was specifically in the context of adoption of AI technology-driven natural LLM-based chatbots by customers during firm customer interaction. Practical implications The research study insights would help managers to restructure and reconfigure their organizational processes. This would neccessiated a shift in firm-customer interactions as demanded because of the availability of AI technology-driven natural LLM-based chatbots by customers. Originality/value This research study was based upon the PPM theoretical framework. This study provided a unique analysis of the altered firm customer interaction needs and requirements. This was one of the first studies that applied the framework of PPM theory regarding the adoption of AI technology-driven natural LLM-based chatbots by customers.

The scientific knowledge of three large language models in cardiology: multiple choice questions examination-based performance

Background: The integration of artificial intelligence (AI) chatbots like Google’s Bard, OpenAI’s ChatGPT, and Microsoft’s Bing Chatbot into academic and professional domains, including cardiology, has been rapidly evolving. Their application in educational and research frameworks, however, raises questions about their efficacy, particularly in specialized fields like cardiology. This study aims to evaluate the knowledge depth and accuracy of these AI chatbots in cardiology using a multiple-choice question (MCQ) format. Methods: The study was conducted as an exploratory, cross-sectional study in November 2023 on a bank of 100 MCQs covering various cardiology topics that was created from authoritative textbooks and question banks. These MCQs were then used to assess the knowledge level of Google’s Bard, Microsoft Bing, and ChatGPT 4.0. Each question was entered manually into the chatbots, ensuring no memory retention bias. Results: The study found that ChatGPT 4.0 demonstrated the highest knowledge score in cardiology, with 87% accuracy, followed by Bing at 60% and Bard at 46%. The performance varied across different cardiology subtopics, with ChatGPT consistently outperforming the others. Notably, the study revealed significant differences in the proficiency of these chatbots in specific cardiology domains. Conclusion: This study highlights a spectrum of efficacy among AI chatbots in disseminating cardiology knowledge. ChatGPT 4.0 emerged as a potential auxiliary educational resource in cardiology, surpassing traditional learning methods in some aspects. However, the variability in performance among these AI systems underscores the need for cautious evaluation and continuous improvement, especially for chatbots like Bard, to ensure reliability and accuracy in medical knowledge dissemination.

Embracing the Academic Change Caused by the Proliferation of Generative Artificial Intelligence Chatbots in Higher Education

The World Economic Forum (World Economic Forum 2020) projects that, by 2025, the reorganisation of labour between humans and machines could result in the displacement of 85 million jobs while also creating 97 million jobs that are better suited to the new division of labour involving machines and algorithms. The aim of this small-scale study was to provide a basic understanding of one of the latest technological advancements, the Generative Artificial Intelligence (GAI) chatbot (Taecharungroj 2023), and how it is influencing teaching and learning in higher education. A particular focus was given to understanding the effectiveness of this technology, its adoption process, and challenges or limitations that arose, all from the perspectives of academics and policy-makers. Utilising a pragmatic stance towards grounded theory, this study employed an inductive and abductive approach towards understanding the real-world problem, generating actionable insights as theory. Rubin and Rubin’s (2012) responsive interviewing method was chosen for the primary source of data collection, utilising convenience and purposive sampling to identify information-rich sources. Data was analysed through a constant comparative process and qualitative coding techniques within MAXQDA software. The emergent prepositions highlight the necessity of revising assessment methods and academic policies in higher education to maintain a strong academic integrity. They go as far as to recommend the integration of multi-faceted evaluations, the adjustment of plagiarism policies, and fostering a culture of continuous pedagogical improvement to ensure a balanced and effective approach towards the utilisation of AI tools in higher educational settings.

ChatGPT in higher education: factors influencing ChatGPT user satisfaction and continued use intention

Artificial intelligence (AI) chatbots, represented by ChatGPT, have shown significant improvement in natural learning and problem analysis and solving, which could trigger a profound change in the education sector and have a far-reaching impact on educational practices. This study aimed to gain insights into the various impacts on users’ experience when using the AI tool ChatGPT in higher education. The study analyzed questionnaire data from 328 college students who used ChatGPT. Employing structural equation modeling, this study examined the technology acceptance model in the higher educational setting to identify factors influencing continued use of ChatGPT. The results show that ChatGPT’s compatibility positively affected users’ perceived ease of use and that efficiency positively affected perceived usefulness. Furthermore, perceived ease of use and perceived usefulness were identified as core factors affecting users’ satisfaction and continued use intentions, with user satisfaction showing a significant positive effect on continued use intention. The findings not only provide new perspectives on human–computer interaction theory in higher education but also further refine the functions of current smart devices. This study will help to promote the continued progress and improvement of educational technology, while also deepening understanding of the relationship between attitude and behavior.