Research Article
Emilio Flores-Mamani, Arcelia-Olga Rojas-Salazar, Pedro-Basilio Tapia-Espinoza, Constantino-Miguel Nieves-Barreto, Juan Inquilla-Mamani, Ányela-Yésica Flores-Yapuchura, Gilberto Vilca-Cutipa
CONT ED TECHNOLOGY, Volume 18, Issue 4, Article No: ep682
ABSTRACT
The use of artificial intelligence (AI) in higher education pedagogy has gained increasing relevance; however, there remains a shortage of psychometrically validated instruments capable of assessing, in a multidimensional and context-specific manner, faculty adoption of these technologies. The aim of this study was to identify the factorial structure and evaluate the psychometric properties of an instrument designed to measure AI use among university faculty. The instrument was developed based on the unified theory of acceptance and use of technology and was administered to a convenience sample of 330 university instructors. Exploratory factor analysis revealed a robust seven-factor structure explaining 66.90% of the total variance, allowing the refinement and optimization of the instrument from 50 to 30 items. The final scale demonstrated excellent overall internal consistency (Cronbach’s alpha = 0.913). It is concluded that the developed instrument is a valid and reliable tool for higher education institutions to assess and diagnose faculty levels of AI adoption, thereby facilitating the design of context-specific institutional policies and effective professional development programs in AI-enhanced pedagogy.
Keywords: artificial intelligence, university pedagogy, exploratory factor analysis, reliability
Research Article
Sawanan Dangprasert
CONT ED TECHNOLOGY, Volume 18, Issue 3, Article No: ep681
ABSTRACT
The rapid emergence of generative artificial intelligence (GenAI) necessitates a robust and validated framework for digital competency in higher education. This study developed and validated the artificial intelligence-augmented digital competency framework (AIDCF) to address the gap between general digital literacy and artificial intelligence (AI)-specific operational requirements. The research was conducted at a public technological university in Thailand. Employing a developmental mixed-methods design, the primary instrument, a 24-item questionnaire, was reviewed by nine experts, yielding a mean content validity ratio (CVR) of 0.861, with item-level CVRs ranging from 0.778 to 1.000. Exploratory factor analysis with a sample of 300 participants supported a five-factor structure: (1) AI awareness & ethics, (2) prompting & tool proficiency, (3) critical thinking with AI, (4) AI-integrated learning design, and (5) reflective digital practice. These factors collectively explained 65.12% of the total variance, with factor loadings ranging from 0.682 to 0.884 and an overall Cronbach’s alpha of 0.87. The framework’s pedagogical utility was examined through a five-week instructional intervention utilizing the collecting, reviewing, analyzing, framing, and tailoring (CRAFT) model with 36 graduate students. Results revealed high levels of perceived competency (mean = 4.51, standard deviation = 0.53) and qualitative evidence of metacognitive development. The AIDCF provides a theoretically grounded and empirically validated roadmap for fostering advanced AI literacy in the GenAI era.
Keywords: AI literacy, digital competency, critical thinking with AI, technology integration, exploratory factor analysis, CRAFT framework
Research Article
Ozlem Baydas Onlu, Mustafa Serkan Abdusselam, Rabia Meryem Yilmaz
CONT ED TECHNOLOGY, Volume 14, Issue 3, Article No: ep368
ABSTRACT
This study aimed to develop the “Students’ Perception of Instructional Feedback Scale” (SPIFS) determining a framework related to the perception of instructional feedback by students. The sequential exploratory mixed method was used in the study. The study was conducted during the instructional design course offered to sophomores in the Department of Computer Education and Instructional Technology at two different universities. Accordingly, firstly a scale consisting of 31 items with Likert-type responses was prepared based on the literature review. Validity and reliability analyses of the scale were completed with a total of 231 participants. After necessary steps were applied in exploratory factor analysis (EFA, n=100), a structure with three factors and 19 items was established. The internal consistency analysis (Cronbach’s alpha), which was applied to the factors obtained and the whole scale, showed the scale to be reliable (whole scale α=.85, 1st factor (mastery, 8 items) α=.92, 2nd factor (positive affect, 6 items) α=.90, and 3rd factor (negative affect, 5 items) α=.96). Confirmatory factor analysis (CFA) was performed (n=131). The structure established through EFA was tested via CFA. The results indicated that the developed structure had acceptable fit (RMSEA=.08, CFI=.91, and RMR=.03).
Keywords: instructional feedback scale, students’ perception, exploratory factor analysis, confirmatory factor analysis
Review Article
Almira R. Bayanova, Alexey A. Chistyakov, Maria O. Timofeeva, Vladimir V. Nasonkin, Tatiana I. Shulga, Vitaly F. Vasyukov
CONT ED TECHNOLOGY, Volume 14, Issue 1, Article No: ep342
ABSTRACT
Smartphones facilitate communication, education, information, and entertainment through a diverse array of mobile applications. Excessive smartphone use has become a significant societal issue. The research community has explored both the positive and negative consequences of mobile phone use. The phrase “problematic smartphone use” refers to an excessive pattern of smartphone use that may have negative consequences. Smartphone addiction may present with symptoms that are unique from Internet addiction. Severe sadness, anxiety, and tension are all associated with problematic smartphone use. Numerous negative consequences are discussed, including mental health problems, diminished physical fitness, and poor academic achievement. According to the findings of the literature analysis, there is no inventory that evaluates smartphone addiction in the context of Russia. The goal of this study is to examine the psychometric characteristics of the smartphone addiction inventory (SPAI) in a Russian context. Several Russian Federation universities performed the study during the autumn semester of the 2020-2021 academic year. To enhance the inventory, Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were utilized on 209 students. As a result, research on the validity and reliability of the Smartphone Addiction Inventory were done in the Russian setting. The research revealed a brief inventory of 14 items and three factors (functional impairment, anxiety, and compulsive behavior).
Keywords: smartphone addiction inventory, psychometric properties, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), university students