The validated scale demonstrates strong theoretical alignment with established technology acceptance frameworks while extending traditional models to accommodate AI-specific considerations, and provides researchers and educational institutions with a reliable, theoretically grounded instrument for assessing and optimizing AI chatbot implementation in higher education contexts.
Abstract
The rapid integration of artificial intelligence (AI) chatbots in higher education has transformed student learning experiences and institutional operations, yet comprehensive measurement tools for assessing student usage patterns remain limited. This study presents the development and psychometric validation of the AI Chatbots Usage Scale, a comprehensive instrument measuring perceptual and attitudinal dimensions of AI chatbot acceptance among higher education students. Using a two-phase methodology, the research employed separate samples for Exploratory Factor Analysis (EFA) (n = 374) and Confirmatory Factor Analysis (CFA) (n = 599) among university students aged 17–23 from the Faculty of Education in Egypt. The initial 25-item scale underwent rigorous expert validation and pilot testing before final administration. EFA revealed a four-factor structure comprising Ease of Use, Perceived Usefulness, Trust, and Accessibility, accounting for 42.48% of total variance. CFA demonstrated excellent model fit indices, with the multidimensional model significantly outperforming a unidimensional alternative. Internal consistency reliability was excellent across all factors, with coefficients ranging from 0.731 to 0.928. The validated scale demonstrates strong theoretical alignment with established technology acceptance frameworks while extending traditional models to accommodate AI-specific considerations. These findings provide researchers and educational institutions with a reliable, theoretically grounded instrument for assessing and optimizing AI chatbot implementation in higher education contexts.
This study aimed to develop and validate a scale for measuring students' integration of generative AI, grounded in the SAMR (Substitution, Augmentation, Modification, and Redefinition) model. The study included 1295 participants selected through convenience sampling. Item analysis was first conducted to examine the discrimination and reliability of each item. Exploratory factor analysis results identified a four-factor structure corresponding to the SAMR model's dimensions. Moreover, the Confirmatory Factor Analysis results supported this structure and further revealed a second-order factor structure that included two higher-order dimensions: Enhancement and Transformation, which represent the broader scope of generative AI integration in education. This scale provides a psychometrically sound tool for assessing students' integration of generative AI in learning, offering valuable insights for future research. It demonstrates that the SAMR model is an effective framework for differentiating the various levels at which students utilize generative AI, informing instructional design and strategies for enhancing AI integration in educational contexts.
Artificial intelligence (AI) educational tools are increasingly incorporated into higher education, making students’ awareness, attitudes, satisfaction and continued-use intentions important for understanding their educational adoption. This study examined these dimensions among students at Government College Madappally, Kerala. A descriptive research design was adopted, and primary data were collected from 185 students through a structured questionnaire using stratified sampling. Frequencies, percentages and weighted mean scores were used for descriptive analysis. One-way ANOVA assessed differences in awareness across Commerce, Science and Arts streams, while independent-samples t-tests examined gender differences in attitude and satisfaction. Students demonstrated high awareness of AI educational tools, with an overall mean awareness score of 3.94, and awareness of AI mistakes and limitations also remained high at 3.70. Attitudes were positive, with the highest mean score recorded for the statement that AI makes the learning process easier (4.07). Satisfaction was high for accuracy and effectiveness (4.04) and for feedback and guidance (3.90). Seventy-one per cent of respondents agreed or strongly agreed that they intended to continue using AI tools for their studies. Awareness differed significantly across academic courses (F = 8.07, p = 0.000438), whereas gender differences in attitude (p = 0.17) and satisfaction (p = 0.14) were not statistically significant. The findings indicate positive student acceptance alongside persistent technical and feedback-related challenges, highlighting the importance of AI literacy, responsible use and institutional support.
M. Praveen, C. Anwaya, T. Vineethan· Journal of global research i...· 0 citations
The lack of a comprehensive, theoretically grounded measurement instrument has constrained the systematic diagnosis of multidimensional barriers faced by K−12 teachers in integrating artificial intelligence (AI) into instruction. To address this gap, this study developed and validated a multidimensional scale based on the Cognitive–Emotional–Volitional–Behavioral (CEVB) heuristic framework. Through semi-structured interviews, expert consultation, and pilot testing, an initial 20-item pool was formed and administered to 546 teachers from central and western Anhui Province, China. Psychometric evaluation encompassed item analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), reliability testing, and validity and measurement invariance assessments. Item analysis confirmed satisfactory discrimination and differentiating power. EFA extracted a clear four-factor structure—cognitive understanding difficulties, affective attitudinal difficulties, conative motivational difficulties, and behavioral practice difficulties—accounting for 66.20% of the total variance. CFA supported both a first-order four-factor model (χ2/df = 2.014, CFI = 0.944, RMSEA = 0.061, SRMR = 0.056) and a higher-order model (χ2/df = 2.162, CFI = 0.935, RMSEA = 0.065, SRMR = 0.066). The scale demonstrated strong internal consistency (Cronbach's α = 0.915, McDonald's ω = 0.917). Convergent and discriminant validity were satisfactory, and strict measurement invariance was established across gender and school levels (primary, middle, and high school). Criterion-related validity was supported by significant positive correlations between scale dimensions and teacher AI literacy dimensions. Furthermore, structural equation modeling confirmed a significant sequential mediation pathway (F1 → F2 → F3 → F4), empirically validating the theoretical cascading process from cognitive deficits to affective resistance, then to motivational decline, and ultimately to behavioral impediments. This study provides a psychometrically robust, theory-driven diagnostic tool that enables phased and targeted interventions, moving beyond one-size-fits-all approaches to effectively support teachers' AI-integrated teaching.
Shoujun Qiao, Yong Nie, Fuhe Niu et al.· Frontiers in Psychology· 0 citations
This study explores teachers’ attitudes towards the adoption of ChatGPT as a learning technology, focusing on its potential benefits, limitations and implications for education and can assist policymakers, educators and technology developers in collaborating to implement practical and ethical AI-enabled tools like ChatGPT.
Latifa Alzahrani· International journal of res...· 0 citations
This study aimed to validate the practical components of learning competency by reflecting changes in the university learning environment resulting from the recent expansion of digital environments and artificial intelligence (AI). To achieve this purpose, preliminary items consisting of three dimensions—learning cognition, learning skills, and learning attitudes—and nine subdomains, including items measuring AI-integrated learning performance, were developed and their content validity was confirmed through an expert Delphi survey. The preliminary version of the Learning Competency Diagnostic Test (H-LCT(II)) was administered to 258 undergraduate students at H University. The collected data was analyzed through exploratory factor analysis (EFA), confirmatory factor analysis (CFA), construct validity verification, and competitive model comparisons to establish the validity of the instrument from multiple perspectives. The results indicated that the nine subdomains formed a bifactor hierarchical structure consisting of a learning dimension and an AI-utilization dimension, and the validity of the structure was empirically supported at multiple levels. In addition, the reliability, convergent validity, and discriminant validity at the latent construct level all satisfied the recommended criteria. Consequently, a final measurement model consisting of 41 items across the learning and AI-utilization dimensions was confirmed. The Learning Competency Diagnostic Test (H-LCT(II)) developed in this study is expected to be useful for diagnosing university students’ AI-era learning competencies and for establishing educational interventions and policy support at the university level.
Sejin Park, K. Park, Yongjin Lee· The Korea Association of Yeo...· 0 citations
This study builds on the technology acceptance model (TAM) by proposing authentic learning and prompt engineering competence (PEC) as precursors of perceived usefulness (PU), perceived ease of use (PEU), and behavioral intention (BI) to use AI tools.
S. Alshammari, Mohammed Habib Alshammari· Electronic Journal of e-Lear...· 0 citations
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