Development and validation of a scale for dependence on artificial intelligence in university students

Frontiers in Education, Journal Year: 2024, Volume and Issue: 9

Published: March 12, 2024

Background Artificial Intelligence (AI) has permeated various aspects of daily life, including education, specifically within higher education settings. These AI technologies have transformed pedagogy and learning, enabling a more personalized approach. However, ethical practical concerns also emerged, the potential decline in cognitive skills student motivation due to excessive reliance on AI. Objective To develop validate Scale for Dependence (DIA). Methods An Exploratory Factor Analysis (EFA) was used identify underlying structure DIA scale, followed by Confirmatory (CFA) assess confirm this structure. In addition, scale’s invariance based participants’ gender evaluated. Results A total 528 university students aged between 18 37 years ( M = 20.31, SD 3.8) participated. The EFA revealed unifactorial which subsequently confirmed CFA. Invariance analyses showed that scale is applicable consistent both men women. Conclusion DAI emerges as robust reliable tool measuring students’ dependence Its makes it diverse population studies. age digitalization, essential understand dynamics humans navigate wisely ensure beneficial coexistence.

Language: Английский

---

Explanatory model of behavioral adaptation in video game addiction among adolescents in urban and rural Peru: the mediating role of anxiety

Frontiers in Psychology, Journal Year: 2025, Volume and Issue: 16

Published: April 8, 2025

The study aimed to determine the relationships between behavioral adaptation and video game addiction, mediated by anxiety, in Peruvian adolescents from urban rural areas, using a structural equation modeling (SEM) approach. This explanatory cross-sectional employed convenience sampling, comprising 606 students of both sexes, aged 11 13, with 62.4% areas 37.6% areas. instruments used included State-Trait Anxiety Inventory for Children (STAIC) measure state trait Behavioral Adaptation (IAC), Video Game Dependency Test (TDV). These demonstrated adequate validity reliability sample through confirmatory factor analysis (CFA), ensuring their relevance context. SEM results confirmed that influences good model fit indices (χ2/df = 4.836; TLI 0.945; CFI 0.964; GFI 0.950; RMSEA 0.080, 90% CI [0.068, 0.092]). Regarding anxiety types, showed stronger negative mediating effect (β -0.31; β 0.20) compared -0.22; 0.16). Significant differences were found students, exhibiting lower higher levels (p < 0.001) peers. findings support theories emphasizing interaction emotional factors development problematic behaviors. Additionally, is identified as having greater impact than suggesting situational responses, rather stable predispositions, are key determinants intensifying addictive behaviors specific contexts.

Language: Английский

---

Using Risk-Free Artificial Intelligence in the Classroom

Advances in educational technologies and instructional design book series, Journal Year: 2024, Volume and Issue: unknown, P. 329 - 362

Published: Dec. 13, 2024

This chapter examines professors' perspectives on using risk-free artificial intelligence (AI) in higher education classrooms, focusing the perceived benefits, challenges, and ethical considerations surrounding AI implementation. Researchers gathered insights into experiences viewpoints integrating educational settings through a qualitative study survey method, semi-structured interviews, questionnaires. The findings reveal that while offers substantial opportunities for enhancing teaching learning, it also brings notable challenges concerns. Based these insights, recommends best practices to ensure responsible effective use of tools education.

Language: Английский

---

YAPAY ZEKÂYA BAĞIMLILIK ÖLÇEĞİNİN TÜRKÇE’YE UYARLANMASI: GEÇERLİK VE GÜVENİRLİK ÇALIŞMASI

Herkes için Spor ve Rekreasyon Dergisi, Journal Year: 2024, Volume and Issue: 6(3), P. 306 - 315

Published: Aug. 28, 2024

Bu çalışmada, Morales-García ve ark. (2024) tarafından geliştirilmiş olan Yapay Zekâya Bağımlılık Ölçeğini (Scale for Dependence on Artificial Intelligence - DAI) Türkçe diline uyarlayarak güvenirlik geçerliliğinin incelenmesi amaçlanmıştır. Ölçek üniversite öğrencilerinin zekâya bağımlılık düzeylerini ölçmeyi amaçlamaktadır. Çalışma dört aşamada gerçekleştirilmiştir. Ölçeğin Türkçe’ye çevrilmesi, açımlayıcı doğrulayıcı faktör analizi, madde geçerliği, güvenirlik. kapsamında 584 katılımcının oluşturduğu öğrencilerinden veri toplanmıştır. geçerliliğini test etmek amacıyla Açımlayıcı Faktör Analizi Doğrulayıcı yapılmıştır. Analizinde ölçeğin tek boyutlu bir yapıda olduğu varyansın % 58,955’inin açıklandığı bulunmuştur. Güvenirlik için Cronbach Alfa iç tutarlılık katsayısı (.82) test- tekrar değerleri (0,79) hesaplanmıştır. Tek boyut 5 maddeden oluşan Ölçeği yapılan analizinde; x2/df=2.609 [χ2=13.045 (Sd=, p

---