Ethical use of ChatGPT in higher education: insights from business students on responsible AI adoption

Generative Artificial Intelligence (GenAI) tools hold significant promises for enhancing teaching and learning outcomes in higher education. However, continues usage behavior and satisfaction of educators with GenAI systems are still less explored. Therefore, this study aims to identify factors influencing academic staff satisfaction and continuous GenAI usage in higher education, employing a survey method and analyzing data using Partial Least Squares Structural Equation Modeling (PLS-SEM). This research utilized the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Expectation Confirmation Model (ECM) as its theoretical foundations, while also integrating ethical concerns as a significant factor. Data was collected from a sample of 127 university academic staff through an online survey questionnaire. The study found a positive correlation between effort expectancy, ethical consideration, expectation confirmation, and academic staff satisfaction. However, performance expectancy did not show a positive correlation with satisfaction. Performance expectancy was positively related to the intention to use GenAI tools, while academic staff satisfaction positively influenced the intention to use GenAI. The social influence did not correlate positively with the use of GenAI. Security and privacy were positively associated with staff satisfaction. Facilitation conditions also positively influenced the intention to use GenAI. The findings of this study provide valuable insights for academia and policymakers, guiding the responsible integration of GenAI tools in education while emphasizing factors for policy considerations and developers of GenAI tools.

Based on Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this research investigates the key factors that influence postgraduate students’ engagement with Artificial Intelligence (AI)-powered chatbots in the context of academic writing, while also analyzing how gender moderates these relationships. A total of 232 postgraduate participants were randomly chosen to complete a structured questionnaire evaluating their responses to nine core constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit, behavioral intention, and actual use. The collected data were examined using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The findings reveal that effort expectancy, habit, and social influence significantly predict behavioral intention, whereas performance expectancy, hedonic motivation, price value, and facilitating conditions do not have a notable influence. Additionally, behavioral intention, habit, and facilitating conditions exert a significant positive effect on actual use behavior in AI-assisted academic writing. These predictors jointly account for 68.0% of the variance in the usage behavior of AI chatbots. Notably, gender significantly moderates the effects of habit and behavioral intention on usage behavior, while its moderating effect on facilitating conditions is not statistically significant. This study constructs a predictive model for postgraduates’ adoption behaviors in AI-based chatbots academic writing, aiming to elevate their writing proficiency, optimize educational decisions, and encourage their more proactive adoption of this technology. Moreover, it provides new theoretical and practical insights for educational technology in the higher education.

Academic integrity needs to be an integral part of collegiate aviation education if students expect to effectively compete in this highly competitive field.Academic integrity is a serious problem in most US colleges and universities today and student dishonesty (in the form of cheating) has presently risen as a major contender for instructors' attention.Recent studies have revealed 40% to 90% of all US college students cheat.To presuppose that academic integrity issues are of little importance and do not present serious problems to collegiate aviation could, in time, irrevocably compromise its very foundation.This paper discusses academic integrity and legal issues in higher education, with implications for collegiate aviation.

PurposeThe notion of using a generative artificial intelligence (AI) engine for text composition has gained excessive popularity among students, educators and researchers, following the introduction of ChatGPT. However, this has added another dimension to the daunting task of verifying originality in academic writing. Consequently, the market for detecting artificially generated content has seen a mushroom growth of tools that claim to be more than 90% accurate in sensing artificially written content.Design/methodology/approachThis research evaluates the capabilities of some highly mentioned AI detection tools to separate reality from their hyperbolic claims. For this purpose, eight AI engines have been tested on four different types of data, which cover the different ways of using ChatGPT. These types are Original, Paraphrased by AI, 100% AI generated and 100% AI generated with Contextual Information. The AI index recorded by these tools against the datasets was evaluated as an indicator of their performance.FindingsThe resulting figures of cumulative mean validate that these tools excel at identifying human generated content (1.71% AI content) and perform reasonably well in labelling AI generated content (76.85% AI content). However, they are perplexed by the scenarios where the content is either paraphrased by the AI (39.42% AI content) or generated by giving a precise context for the output (60.1% AI content).Originality/valueThis paper evaluates different services for the detection of AI-generated content to verify academic integrity in research work and higher education and provides new insights into their performance.

This paper examines the impacts of AI-powered assistive technologies (AIATs) on the academic success of higher education university students with visual impairments. As digital learning contexts become progressively more prevalent in higher education institutions, it is critical to understand how these technologies foster the academic success of university students with blindness or low vision. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study conducted a quantitative research approach and collected data from 390 visually impaired students who were enrolled in different universities across Saudi Arabia (SA). Employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the paper tested the influences of four UTAUT dimensions—Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC)—on Academic Performance (AP), while also evaluating the mediating role of Behavioral Intention (BI). The results revealed a significant positive relationship between the implementation of AI-based assistive tools and students’ academic success. Particularly, BI emerged as a key mediator in these intersections. The results indicated that PE (β = 0.137, R2 = 0.745), SI (β = 0.070, R2 = 0.745), and BI (β = 0.792, R2 = 0.745) significantly affected AP. In contrast, EE (β = −0.041, R2 = 0.745) and FC (β = −0.004, R2 = 0.745) did not have a significant effect on AP. Concerning predictors of BI, PE (β = 0.412, R2 = 0.317), SI (β = 0.462, R2 = 0.317), and EE (β = 0.139, R2 = 0.317) were all positively associated with BI. However, FC had a significant negative association with BI (β = −0.194, R2 = 0.317). Additionally, the analysis revealed that EE, SI, and PE can all indirectly enhance Academic Performance by influencing BI. The findings provide practical insights for higher education policymakers, higher education administrators, and AI designers, emphasizing the need to improve the accessibility and usability of sustainable and long-term assistive technologies to better accommodate learners with visual impairments in higher education contexts.

Adopting digital payment systems is crucial for developing MSMEs, particularly in emerging regions with limited digital infrastructure. This study addresses the knowledge gap regarding digital payment adoption in areas that are not entirely digitally connected, such as Ibu Kota Nusantara (IKN), the new capital of Indonesia. The study aims to identify the factors influencing MSME owners' behavioral intention and usage behavior regarding digital payment systems, using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. This study employs a survey method with a convenience sampling technique, involving 350 MSME owners in IKN and its surrounding areas. Data were collected through a structured questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that performance expectancy, social influence, and facilitating conditions significantly influence behavioral intention to adopt digital payments. However, effort expectancy does not significantly affect the intention to use digital payments. Additionally, facilitating conditions and behavioral intention impact usage behavior significantly. No evidence supported the moderating role of voluntariness of use in the relationship between social influence and behavioral intention. Digital literacy was found to moderate the relationship between behavioral intention and usage behavior, highlighting the critical role of digital literacy in technology adoption. This study provides important contributions to understanding the factors influencing digital payment adoption in developing regions.

Aim/Purpose: This study proposes a theoretical framework that amalgamates Unified Theory of Acceptance and Use of Technology (UTAUT) variables with usability metrics to investigate the impact on students’ intention and use of the Learning Management System (LMS) in Saudi higher education. Background: There is a dearth of academic research on Saudi higher education to examine the effects of usability factors on students use of LMSs, so significant issues have not yet been examined. Methodology: Based on survey data from 605 respondents, the Partial Least Squares Structural Equation Modelling (PLS-SEM) technique was employed to assess the model. Contribution: The findings of the study may help colleges and universities to gain insights into the best way to promote e-learning system perceived usefulness and acceptance among students. Findings: The results confirmed that the UTAUT parameters are valid and robust in the context of LMS in Saudi Arabia. The dimension of social influence emerged to significantly influence the students’ intention and usage behaviour. The performance expectancy was affected by information quality and the system interactivity whereas the effort expectancy was influenced by system navigation, system learnability, and instructional assessment. The usability feature of interactivity was also demonstrated to influence students’ willingness to use the system. Recommendations for Practitioners: University policymakers are expected to benefit from this research for e-learning system acceptance in an academic setting and eliminate any impediments to its implementation. University students will be able to identify the factors and motivations driving their adoption of the system. In particular, usability, social, and organisational factors that affect their use of an e-learning system would be better understood. Recommendation for Researchers: The study should aid the research community in technology acceptance and usability studies to determine the students’ perceptions and experiences towards e-learning usability, social, and organisational factors that influence their acceptance, specifically in a Saudi context where students have unique psychological and social characteristics. Administrators and designers could also better understand areas of improvement for usability issues and develop design solutions based on the findings of this study. Impact on Society: The suggestions have been offered in order to accelerate and increase the use of e-learning services in Saudi higher education. System designers and administrators should have a better insight into the user interface design, considering system-independent metrics that could enhance user acceptance of e-learning systems. Future Research: The study focused on the students’ perspective, a natural progression of this work is to involve other e-learning stakeholders (teachers and administrators). This could enrich the research by providing a better understanding of undisclosed issues, offering different views about the implementation and use of an e-learning system in Saudi Arabia.

Abstract. The article aims to explore factors that influence respondents’ intentions to accept and use mobile advertising by proposing an extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) with perceived enjoyment, perceived irritation, and personalisation. To identify the factors affecting the acceptability of mobile advertising, the intentions and behaviours of respondents regarding mobile advertising are reviewed. The study was conducted in Pakistan by using online survey. Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied to examine the sample size of 446 respondents. The findings revealed certain crucial factors (such as effort expectancy, performance expectancy, perceived enjoyment, perceived irritation, and personalisation) that can affect respondents’ intentions to accept mobile advertising. Also revealed was the relationship between respondents’ intentions to accept and use behaviour regarding mobile advertising. It is established that respondents expect more personalised promotional messages to be shown to them in line with their needs and preferences. Here, advertisers must pay attention to the contextual relevance of the ads while noting the element of irritation that can be felt among consumers, as such ads create negative attitudes and intentions towards mobile advertising. Keywords: mobile advertising, consumer intentions, use behaviour, unified theory of acceptance and use of technology (UTAUT), Partial Least Squares Structural Equation Modelling (PLS-SEM), Pakistan.

This study examines the influence of AI-powered and emerging technologies on pedagogical practices in higher education, focusing on their role on behavioural intention (BI) and actual usage among educators and students. The research hypothesises that the relationship between each Unified Theory of Acceptance and Use of Technology (UTAUT) constructs - Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) - and BI to use AI is mediated by Attitude towards AI. Additionally, it explores the mediation of the relationship between Technological, Organisational, and Environmental (TOE) framework and BI to use AI by Attitude towards AI. Furthermore, it hypothesises that the relationship between UTAUT constructs, and the actual Usage of AI is mediated by BI. Data were collected from 115 respondents through a survey measuring perceptions of UTAUT and technology-organisation-Environment (TOE) constructs. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyse the data, alongside PLS-Multi-Group Analysis (PLS-MGA) to explore gender differences. The key results indicated that PE and EE significantly influenced Attitude towards AI (F = 14.861 and F = 33.887, respectively), which in turn significantly influenced BI (β = 0.468, t = 5.106). However, SI had an insignificant effect (F = 0.346), and FC had a minimal influence (F = 2.225). The study found that Organisational Context played a significant role in shaping Attitude towards AI, influencing BI, whereas the direct influence of TOE on BI was not significant (F = 1.356), indicating full mediation by Attitude. Additionally, higher BI did not necessarily translate to increased AI usage (β = -0.381, t = 4.749), suggesting external barriers or moderating factors. These findings provide valuable insights for developing strategies to enhance AI-driven pedagogical innovations, emphasising the importance of fostering positive attitudes and addressing practical challenges in AI adoption.

With the increasing use of large-scale language model-based AI tools in modern learning environments, it is important to understand students’ motivations, experiences, and contextual influences. These tools offer new support dimensions for learners, enhancing academic achievement and providing valuable resources, but their use also raises ethical and social issues. In this context, this study aims to systematically identify factors influencing the usage intentions of text-based GenAI tools among undergraduates. By modifying the core variables of the Unified Theory of Acceptance and Use of Technology (UTAUT) with AI literacy, a survey was designed to measure GenAI users’ intentions to collect participants’ opinions. The survey, conducted among business students at a university in South Korea, gathered 239 responses during March and April 2024. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software (Ver. 4.0.9.6). The findings reveal that performance expectancy significantly affects the intention to use GenAI, while effort expectancy does not. In addition, AI literacy and social influence significantly influence performance, effort expectancy, and the intention to use GenAI. This study provides insights into determinants affecting GenAI usage intentions, aiding the development of effective educational strategies and policies to support ethical and beneficial AI use in academic settings.

The integration of Artificial Intelligence (AI) into higher education is gaining momentum, offering innovative tools that can improve student engagement, support individualized learning, and enhance academic performance. While global statistics indicate that approximately 86% of students have used AI for academic purposes, its adoption among Malaysian undergraduates remains relatively limited. This is despite ongoing national efforts such as the Malaysia Digital Economy Blueprint (MyDIGITAL), which seeks to accelerate digital transformation across sectors including education. Understanding the drivers and barriers to AI adoption in higher education is crucial for developing effective strategies that can enhance teaching and learning experiences. This study investigates the factors influencing students’ intentions to adopt AI in higher education settings. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), the research focuses on four core constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions. The findings aim to offer insights that can support effective strategies for AI adoption in Malaysia’s higher education landscape, benefiting educators, policymakers, and technology developers. This study will employ a quantitative, cross-sectional survey to examine factors influencing undergraduate students’ intention to use artificial intelligence (AI) in Malaysian higher education. Data will be collected from a convenience sample of undergraduate students through an online questionnaire adapted from the UTAUT framework. A minimum of 137 participants will be targeted to ensure sufficient statistical power. The data will be analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS, which is suitable for exploratory research involving complex models. This research is expected to advance understanding of technology acceptance in higher education by identifying key factors influencing students’ intention to use AI, offering insights for educators and policymakers to support effective AI integration. Ultimately, the study seeks to contribute to the broader adoption of AI technologies that can transform the educational experience.

AI-based assistive technologies (AIATs) are increasingly recognised as essential tools to enhance accessibility, independence, and inclusion for visually impaired students in higher education. However, limited evidence exists regarding the determinants of their acceptance and use, particularly in terms of psychosocial factors. This study aimed to extend the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating physical self-esteem (PSE) as a moderator and behavioural intention (BI) as a mediator within a single model. Data were collected through a validated questionnaire administered to 395 visually impaired undergraduates across five Saudi universities. Constructs included effort expectancy (EE), performance expectancy (PE), facilitating conditions (FCs), social influence (SI), BI, and PSE. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used for analysis. Results showed that PE and SI significantly predicted both BI and adoption, while EE strongly predicted BI but not AIAT adoption; FC had no significant influence on either outcome. BI positively affected AIAT adoption and mediated the effects of PE, EE, and SI, but not FC. Moderation analysis indicated that PSE strengthened the influence of PE, EE, and SI on BI and adoption. These findings underscore the significance of psychological factors, particularly self-esteem, in promoting the adoption of AIAT and offer guidance for developing inclusive educational strategies.

This study analyzes students' preferences regarding mobile banking usage in the Jabodetabek area, involving 250 respondents selected through purposive sampling. The research employs the Unified Theory of Acceptance and Use of Technology (UTAUT) model to evaluate key factors influencing mobile banking adoption. The Partial Least Squares-Structural Equation Modeling (PLS-SEM) technique is used to analyze variables such as performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, and usage behavior. The findings show that effort expectancy, facilitating conditions, and behavioral intention have a significant impact on mobile banking adoption. These results suggest that perceptions of ease of use, good facilitating conditions, and a clear intention to use mobile banking are crucial in shaping user behavior. However, performance expectancy and social influence do not have a significant impact on the intention to use. These findings provide insights for banks to enhance service quality and meet students' expectations.

As mobile payment (m-payment) is critical to the success of mobile commerce, the research incorporates the unified theory of acceptance and use technology (UTAUT) with trust (TR), perceived financial cost (PFC), and the moderating variable of experience to understand the adoption intention. A total of 319 valid questionnaires were collected and subsequently analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) approach. The results demonstrated that only performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), and trust (TR) are significant with the intention to adopt. In addition, experience was found to have a moderating effect on the relationship between PE and intention. As there is limited UTAUT-based research conducted on m-payment, the paper is among the few that attempts to extend the model from a multi-religion and multi-ethnic perspective. The findings will enable mobile stakeholders to understand the rationale behind the use of service leads to higher acceptance.

This study investigates the role of technology‐related anxiety in shaping university teachers’ behavioral intention to adopt ChatGPT. Three distinct types of anxiety are examined: (a) anxiety about the future of the academic profession, (b) anxiety regarding the personal misuse of ChatGPT, and (c) anxiety concerning negative impacts on student learning. A structured questionnaire was administered to 249 faculty members from Spanish public universities. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess both the direct effects of each type of anxiety on behavioral intention and the mediating roles of effort expectancy (EE) and performance expectancy (PE). Results indicate that anxiety about student learning exhibits a significant negative direct effect on behavioral intention and an indirect effect through performance expectancy. Similarly, anxiety related to the misuse of ChatGPT is negatively associated with behavioral intention, with significant mediation through both EE and PE. In contrast, anxiety concerning the future of the academic profession does not show a statistically significant relationship with behavioral intention. The findings underscore the importance of addressing specific psychological barriers—particularly those linked to concerns over student learning and technology misuse—to facilitate ChatGPT integration in higher education. The study suggests that effective implementation strategies should combine technical training with targeted interventions aimed at managing technology-related anxiety, enhancing ethical practices, and improving perceptions of the tool’s utility and ease of use.