Scholarlink

<p>The current study aimed to develop and validate the Executive Function-Based Learning Difficulties Scale for AI-Driven Educational Environments (EFB-LDS-AI). In this study, EF-BLD refers to AI-mediated executive-control learning difficulties, operationalized as self-reported maladaptive reliance behaviors and perceived reductions in effortful monitoring/verification during AI-supported learning, rather than a diagnosis of stable executive-function deficits. Accordingly, two phases—qualitative and quantitative—were conducted. In the qualitative phase, face-to-face interviews with 18 cognitive science specialists, teachers, and students, along with a literature review, were conducted to generate items and dimensions associated with Executive Function-Based Learning Difficulties (EF-BLD) in AI-based educational settings. The analysis identified six key dimensions reflecting how AI-supported learning may weaken independent cognitive engagement, including overreliance on AI for thinking, superficial understanding, reduced verification of AI-generated information, weaker memory for learned material, diminished cognitive effort, and reduced critical evaluation. Psychometric properties, such as validity and reliability, were assessed during the quantitative phase. These factors captured distinct yet related patterns of AI-mediated learning difficulties: cognitive offloading, illusion of understanding, AI-induced informational bias, memory erosion, AI-induced cognitive laziness, and decline in critical thinking. This structure reflected six theoretically grounded dimensions: cognitive offloading, illusion of understanding, AI-induced informational bias, memory erosion, AI-induced cognitive laziness, and decline of critical thinking. Confirmatory Factor Analysis (CFA) confirmed the scale’s fit, with all items showing factor loadings above 0.4 (<i>p</i> &lt; .001). The scale demonstrated excellent internal consistency, with Cronbach’s alpha values ranging from 0.893 to 0.958 across the six factors. Measurement invariance analysis revealed no significant gender differences. Exploratory Graph Analysis (EGA) further validated the six-factor structure. The results show that the EFB-LDS-AI is a valid, reliable, and robust tool for assessing learning difficulties related to AI interactions in educational environments. In conclusion, this scale offers a practical tool for identifying students’ AI-related patterns of cognitive offloading, weak verification, and reduced independent engagement, thereby supporting the design of AI-literacy instruction, metacognitive scaffolding, and future longitudinal research on learning in AI-integrated educational environments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Exploring executive function-based learning challenges in AI-enhanced education: development and validation of a novel psychometric scale using network and machine learning analysis

  • 期刊论文

  • 发表日期：

    2026年4月24日

查看全文
  • Mohammad Nayef Ayasrah,
  • Mahmoud Gharaibeh,
  • Amal Jamal Al Khatib,
  • Mohamad Ahmad Saleem Khasawneh,
  • Amoura Hassan Aboutaleb

摘要

The current study aimed to develop and validate the Executive Function-Based Learning Difficulties Scale for AI-Driven Educational Environments (EFB-LDS-AI). In this study, EF-BLD refers to AI-mediated executive-control learning difficulties, operationalized as self-reported maladaptive reliance behaviors and perceived reductions in effortful monitoring/verification during AI-supported learning, rather than a diagnosis of stable executive-function deficits. Accordingly, two phases—qualitative and quantitative—were conducted. In the qualitative phase, face-to-face interviews with 18 cognitive science specialists, teachers, and students, along with a literature review, were conducted to generate items and dimensions associated with Executive Function-Based Learning Difficulties (EF-BLD) in AI-based educational settings. The analysis identified six key dimensions reflecting how AI-supported learning may weaken independent cognitive engagement, including overreliance on AI for thinking, superficial understanding, reduced verification of AI-generated information, weaker memory for learned material, diminished cognitive effort, and reduced critical evaluation. Psychometric properties, such as validity and reliability, were assessed during the quantitative phase. These factors captured distinct yet related patterns of AI-mediated learning difficulties: cognitive offloading, illusion of understanding, AI-induced informational bias, memory erosion, AI-induced cognitive laziness, and decline in critical thinking. This structure reflected six theoretically grounded dimensions: cognitive offloading, illusion of understanding, AI-induced informational bias, memory erosion, AI-induced cognitive laziness, and decline of critical thinking. Confirmatory Factor Analysis (CFA) confirmed the scale’s fit, with all items showing factor loadings above 0.4 (p < .001). The scale demonstrated excellent internal consistency, with Cronbach’s alpha values ranging from 0.893 to 0.958 across the six factors. Measurement invariance analysis revealed no significant gender differences. Exploratory Graph Analysis (EGA) further validated the six-factor structure. The results show that the EFB-LDS-AI is a valid, reliable, and robust tool for assessing learning difficulties related to AI interactions in educational environments. In conclusion, this scale offers a practical tool for identifying students’ AI-related patterns of cognitive offloading, weak verification, and reduced independent engagement, thereby supporting the design of AI-literacy instruction, metacognitive scaffolding, and future longitudinal research on learning in AI-integrated educational environments.