The ethical implications of using generative chatbots in higher education

Frontiers in Education, Год журнала: 2024, Номер 8

Опубликована: Янв. 8, 2024

Incorporating artificial intelligence (AI) into education, specifically through generative chatbots, can transform teaching and learning for education professionals in both administrative pedagogical ways. However, the ethical implications of using chatbots must be carefully considered. Ethical concerns about advanced have yet to explored sector. This short article introduces associated with introducing platforms such as ChatGPT education. The outlines how handling sensitive student data by presents significant privacy challenges, thus requiring adherence protection regulations, which may not always possible. It highlights risk algorithmic bias could perpetuate societal biases, problematic. also examines balance between fostering autonomy potential impact on academic self-efficacy, noting over-reliance AI educational purposes. Plagiarism continues emerge a critical concern, AI-generated content threatening integrity. advocates comprehensive measures address these issues, including clear policies, plagiarism detection techniques, innovative assessment methods. By addressing argues that educators, developers, policymakers, students fully harness creating more inclusive, empowering, ethically sound future.

Язык: Английский

---

Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education

Education and Information Technologies, Год журнала: 2024, Номер unknown

Опубликована: Авг. 24, 2024

Язык: Английский

---

What drives college students to use AI for L2 learning? Modeling the roles of self-efficacy, anxiety, and attitude based on an extended technology acceptance model

Acta Psychologica, Год журнала: 2024, Номер 249, С. 104442 - 104442

Опубликована: Авг. 6, 2024

Prior research highlights the critical role of AI in enhancing second language (L2) learning. However, factors that practically affect L2 learners to engage with resources are still underexplored. Given widespread availability digital devices among college students, they particularly poised benefit from AI-assisted As such, this study, grounded an extended Technology Acceptance Model (TAM), investigates predictors learners' actual use tools, focusing on self-efficacy, AI-related anxiety, and their overall attitude toward AI. Data was gathered 429 at Chinese universities via online questionnaire, utilizing four established scales. Through structural equation modeling (SEM) AMOS 24, results indicate self-efficacy could negatively positively influence both tools. Besides, anxiety predicted Moreover, a positive predictor through reducing AI, or combination both. This study also discusses theoretical pedagogical implications suggests directions for future research.

Язык: Английский

---

Developing and Validating a Scale of Artificial Intelligence Anxiety Among Chinese EFL Teachers

European Journal of Education, Год журнала: 2025, Номер 60(1)

Опубликована: Янв. 7, 2025

ABSTRACT As artificial intelligence (AI) technology continues to advance, its influences across various industries have grown, leading increasing levels of anxiety, including that in education. Nonetheless, terms current knowledge, the literature lacks a valid scale measure AI anxiety among EFL teachers, particularly university teachers. Moreover, underlying dimensions this construct yet be clarified. Against these gaps, study aims develop and validate assess teachers China. We used qualitative interviews quantitative surveys combined identify key In so doing, 251 Chinese completed newly designed scale. The result exploratory factor analyses indicated five 21 items questionnaire. Five were identified: technical proficiency, job displacement, technological support, student experience research development. Next, another 415 participated validating confirmatory analysis demonstrated strong reliability, validity an acceptable model fit. This new provides useful tool for assessing highlights unique challenges they face adapting AI, offering basis future targeted support.

Язык: Английский

---

Developing and validating an instrument for teachers’ acceptance of artificial intelligence in education

Education and Information Technologies, Год журнала: 2025, Номер unknown

Опубликована: Янв. 22, 2025

Язык: Английский

---

What motivates academics in Egypt toward generative AI tools? An integrated model of TAM, SCT, UTAUT2, perceived ethics, and academic integrity

Information Development, Год журнала: 2025, Номер unknown

Опубликована: Янв. 31, 2025

In recent years, the adoption of AI technologies in academia has increased, prompting a need to explain factors driving scholars adopt or plan research routines. This study integrates three models into one integrated model: TAM, UTAUT, and SCT. These are combined understand how GenAI self-efficacy, perceived ethics, academic integrity, social influence, facilitating conditions, risks, ease use, usefulness influenced participants’ intention research. Following this, data were collected from Egyptian academics linked universities. There 742 responses this question. Data analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The paper's results showed that ethics significantly related perceptions usefulness, use GenAI. Facilitating conditions have negative effect on risk does not affect significantly. Notably, result found integrity GenAI's usage utility. guide illustrates universities must take proactive steps influence will be used reinforces importance these tools within an ethical lens. paper emphasizes balance generative practices. It examines role attitudes toward AI. They represent step forward our understanding induce adoption–in case, context, specifically Egypt. Additionally, it places sound emphasis technology can beneficial whilst advocating for sensible approach application, which includes principles.

Язык: Английский

---

A deep learning-based hybrid PLS-SEM-ANN approach for predicting factors improving AI-driven decision-making proficiency for future leaders

Journal of International Education in Business, Год журнала: 2025, Номер unknown

Опубликована: Март 3, 2025

Purpose This study explores the factors influencing artificial intelligence (AI)-driven decision-making proficiency (AIDP) among management students, focusing on foundational AI knowledge, data literacy, problem-solving, ethical considerations and collaboration skills. The research examines how these competencies enhance self-efficacy engagement, with curriculum design, industry exposure faculty support as moderating factors. aims to provide actionable insights for educational strategies that prepare students AI-driven business environments. Design/methodology/approach adopts a hybrid methodology, integrating partial least squares structural equation modeling (PLS-SEM) neural networks (ANNs), using quantitative collected from 526 across five Indian universities. PLS-SEM model validates linear relationships, while ANN captures nonlinear complexities, complemented by sensitivity analyses deeper insights. Findings results highlight pivotal roles of literacy problem-solving in fostering self-efficacy. Behavioral, cognitive, emotional social engagement significantly influence AIDP. Moderation analysis underscores importance design enhancing efficacy constructs. identifies most critical predictors AIDP, respectively. Research limitations/implications is limited central universities may require contextual adaptation global applications. Future could explore longitudinal impacts AIDP development diverse cultural settings. Practical implications findings designers, policymakers educators integrate into education. Emphasis experiential learning, frameworks interdisciplinary preparing AI-centric landscapes. Social By equipping future leaders proficiency, this contributes societal readiness technological disruptions, promoting sustainable contexts. Originality/value To author’s best uniquely integrates analyze interplay shaping It advances theoretical models linking learning theories practical education strategies, offering comprehensive framework developing students.

Язык: Английский

---

Driving Factors of Generative AI Adoption in New Product Development Teams from a UTAUT Perspective

International Journal of Human-Computer Interaction, Год журнала: 2024, Номер unknown, С. 1 - 22

Опубликована: Июль 12, 2024

Язык: Английский

---

Validating the AI-assisted second language (L2) learning attitude scale for Chinese college students and its correlation with L2 proficiency

Acta Psychologica, Год журнала: 2024, Номер 248, С. 104376 - 104376

Опубликована: Июль 1, 2024

The positive impact of Artificial Intelligence (AI) on second language (L2) learning is well-documented. An individual's attitude toward AI significantly influences its adoption. Despite this, no specific scale has been designed to measure this attitude, particularly in the Chinese context. To address gap, our study aims construct AI-Assisted L2 Learning Attitude Scale for College Students (AL2AS-CCS) and evaluate reliability, validity, relationship with proficiency. Our research comprises two phases, each involving separate samples. In Phase One (Sample 1: n = 379), we conducted exploratory factor analysis (EFA) determine structure AL2AS-CCS. resulting two-factor consists 12 items, categorized into cognitive behavioral components. Two 2: 429), performed confirmatory (CFA) validate assess model fit. CFA Sample 2 confirmed demonstrated a good Additionally, AL2AS-CCS exhibited high criterion internal consistency, cross-gender invariance. findings suggest that valid measurement tool assessing college students' AI-assisted learning. Moreover, students were discovered maintain moderately correlation was identified between their

Язык: Английский

---

Adaptation and psychometric properties of a brief version of the general self-efficacy scale for use with artificial intelligence (GSE-6AI) among university students

Frontiers in Education, Год журнала: 2024, Номер 9

Опубликована: Март 8, 2024

Background Individual beliefs about one’s ability to carry out tasks and face challenges play a pivotal role in academic professional formation. In the contemporary technological landscape, Artificial Intelligence (AI) is effecting profound changes across multiple sectors. Adaptation this technology varies greatly among individuals. The integration of AI educational setting has necessitated tool that measures self-efficacy concerning adoption use technology. Objective To adapt validate short version General Self-Efficacy Scale (GSE-6) for (GSE-6AI) university student population. Methods An instrumental study was conducted with participation 469 medical students aged between 18 29 ( M = 19.71; SD 2.47). GSE-6 adapted context, following strict translation cultural adaptation procedures. Its factorial structure evaluated through confirmatory analysis (CFA). Additionally, invariance scale based on gender studied. Results GSE-6AI exhibited unidimensional excellent fit indices. All item loads surpassed recommended threshold, both Cronbach’s Alpha (α) McDonald’s Omega (ω) achieved value 0.91. Regarding by gender, proved maintain its meaning men women. Conclusion valid reliable measuring students. gender-related make it robust versatile future research practical applications contexts.

Язык: Английский

---